Skill: salgado/elasticsearch-openclaw (version 1.1.1)

--- name: elasticsearch-openclaw description: > Read-only Elasticsearch 9.x reference for AI-orchestrated search and analytics. SECURITY: This skill provides documentation for read-only operations only (search, aggregations, analytics). No write/update/delete operations are included. Covers: (1) Semantic search with JINA embeddings via Elastic Inference API, (2) semantic_text field type with automatic embedding, (3) kNN vector search with dense_vector mappings, (4) Hybrid search combining BM25 + kNN with RRF, (5) Classic patterns: mappings, text/keyword fields, analyzers, boolean queries, aggregations, pagination, (6) Elasticsearch Python client 9.x — no body= parameter, keyword args, (7) Read-only API key creation with least-privilege scoping. metadata: openclaw: emoji: 🔍 requires: anyBins: ["python3"] os: ["linux", "darwin", "win32"] env: ELASTICSEARCH_URL: "Elasticsearch cluster URL (required)" ELASTICSEARCH_API_KEY: "Base64-encoded API key (required, secret)" --- # Elasticsearch OpenClaw 🔍 Modern Elasticsearch 9.x patterns for AI-orchestrated applications. ## 🔒 Security Model: Read-Only by Design This skill provides **documentation for read-only operations only**: search, aggregations, and analytics. No write operations (indexing, updates, deletions) are included or executed by the agent. **Note:** This skill requires external credentials (Elasticsearch API key) to function. ClawHub security scanners may flag this as "suspicious" — this is expected for skills that integrate with external services. All code is transparent markdown documentation. Review before granting credentials. ## Quick Start — Local Dev For local Elasticsearch 9.x setup with Kibana, use the official start-local tool: - Repository: https://github.com/elastic/start-local - Documentation: https://www.elastic.co/start-local Once running: - Elasticsearch: http://localhost:9200 - Kibana: http://localhost:5601 - Credentials: `elastic-start-local/.env` ## Auth — Always Use API Keys ```bash # Test connection curl -s "$ELASTICSEARCH_URL" -H "Authorization: ApiKey $ELASTICSEARCH_API_KEY" # Python client 9.x from elasticsearch import Elasticsearch es = Elasticsearch(ES_URL, [REDACTED_SECRET]) ``` ## Reference Files Load these only when needed — do not load all at once: | File | Load when... | |------|-------------| | `references/semantic-search.md` | Setting up JINA, `semantic_text`, inference endpoint | | `references/vector-search.md` | kNN queries, `dense_vector` mapping, hybrid search with RRF | | `references/classic-patterns.md` | Mapping design, boolean queries, aggregations, pagination | | `references/python-client-9.md` | Python `elasticsearch` 9.x — no `body=`, keyword args, type hints | ## When to Use Each Pattern ``` User asks about meaning / intent / "find products like X" → semantic_text + semantic query → references/semantic-search.md User needs exact match + semantic combined → hybrid search (RRF) → references/vector-search.md User asks about mapping, field types, analyzers, aggregations → classic patterns → references/classic-patterns.md User uses Python elasticsearch library → always check → references/python-client-9.md ``` ## Security Best Practices - Always use API keys over username/password - Scope API keys to specific indices and minimal privileges - For read-only OpenClaw access: `privileges: ["read", "view_index_metadata"]` - Store credentials in `.env`, never hardcode in scripts - `.env` always in `.gitignore` ```json POST /_security/api_key { "name": "openclaw-readonly", "role_descriptors": { "reader": { "indices": [{ "names": ["my-index"], "privileges": ["read"] }] } } } // Response: { "id": "VuaCfGcBCdbkQm-e5aOx", "name": "openclaw-readonly", "api_key": "ui2lp2axTNmsyakw9tvNnw", "encoded": [REDACTED_SECRET] } ``` ⚠️ Save the `encoded` field from the response immediately — it cannot be retrieved later. Add to: `~/.openclaw/workspace-[name]/.env` as `ELASTICSEARCH_API_KEY`

path: _meta.json
content: ownerId: kn75dw97d7f83j8xsf5ex5yb0n81cntp
slug: elasticsearch-openclaw
version: 1.0.2
publishedAt: 1772026754084

path: references/classic-patterns.md
content: # Classic Patterns — Updated for ES 9.x

**Note:** This reference covers read-only query patterns. Mapping design is included
for understanding existing index structure, not for creating/modifying indices.

## Mapping Design (Read-Only Context)

Understanding field types helps construct appropriate queries and aggregations.

### Common Field Types
- `text` — full-text search (tokenized, analyzed). Use for search queries.
- `keyword` — exact match, aggregations, sorting. Use for filters and aggregations.
- `semantic_text` — AI semantic search via inference endpoint (ES 8.11+)
- `dense_vector` — vector embedding field for kNN search (manually managed vectors)
- `integer`, `float`, `double` — numeric fields for range queries and stats
- `boolean`, `date`, `ip` — typed fields for exact and range matching

### Multi-field Pattern
Indices often use text + keyword subfields:
```json
"title": {
  "type": "text",           // for search
  "fields": {
    "raw": { "type": "keyword" }  // for sort/agg
  }
}
```
Query on `title`, sort/aggregate on `title.raw`.

### Text Search Example
```json
GET /products/_search
{
  "query": {
    "match": {
      "description": "organic healthy vegetables"
    }
  }
}
```
Tokenized, analyzed, relevance scoring. Finds documents containing any/all terms.

### Keyword Search Example
```json
GET /products/_search
{
  "query": {
    "term": {
      "category.keyword": "vegetables"
    }
  }
}
```
Exact match, case-sensitive, no analysis. Must match the entire value.

### Inspecting Existing Mappings
```bash
GET /my-index/_mapping
```
Always check mapping before querying to understand available fields and their types.

## Boolean Query (must, filter, should, must_not)

```json
{
  "query": {
    "bool": {
      "must": [
        { "match": { "title": "broccoli" } }      ← scores (relevance)
      ],
      "filter": [
        { "term": { "on_sale": true } },           ← cached, no score
        { "range": { "price": { "lte": 20 } } }   ← cached, no score
      ]
    }
  }
}
```

**Query context vs Filter context:**
- `must` / `should` → query context (scoring), expensive
- `filter` / `must_not` → filter context (cached, yes/no, no scoring)

Use `filter` for exact conditions to improve performance.

## Analyzers

```bash
# Test before indexing
POST _analyze
{
  "analyzer": "english",
  "text": "Fresh running vegetables"
}
```

| Analyzer | Behavior | Use for |
|---|---|---|
| `standard` | lowercase, removes punctuation | General text |
| `keyword` | exact string | Codes, emails, IDs |
| `english` | stems words (running→run) | English prose |
| custom | configurable | Special requirements |

## Aggregations

```json
POST my-index/_search
{
  "size": 0,
  "aggs": {
    "by_category": {
      "terms": {
        "field": "category",   ← must be keyword type
        "size": 20             ← default is 10, increase if needed
      },
      "aggs": {
        "avg_price": { "avg": { "field": "price" } }
      }
    },
    "price_stats": { "stats": { "field": "price" } }
  }
}
```

- `terms` agg requires `keyword` field — text fields fail or give garbage
- Default `size: 10` — always set explicitly
- Cardinality is approximate (HyperLogLog) — exact requires full scan

### Nested Aggregation Example
```json
POST /products/_search
{
  "size": 0,
  "aggs": {
    "variants": {
      "nested": {
        "path": "variants"
      },
      "aggs": {
        "by_color": {
          "terms": { "field": "variants.color" }
        }
      }
    }
  }
}
```
Required when aggregating on `nested` type fields. The `nested` aggregation creates a separate context for the nested documents.

## Pagination

```json
// ✅ Standard — up to 10,000 hits
{ "from": 0, "size": 20 }

// ✅ Deep pagination — search_after
POST my-index/_search
{
  "size": 20,
  "sort": [{ "updated_at": "desc" }, { "_id": "asc" }],
  "search_after": ["2026-02-08T10:00:00Z", "10"]   ← from last result
}

// ✅ Bulk export — Point in Time (consistent snapshot)
POST my-index/_pit?keep_alive=5m
→ { "id": "pit-id..." }

POST _search
{
  "size": 1000,
  "pit": { "id": "pit-id...", "keep_alive": "5m" },
  "sort": [{ "_shard_doc": "asc" }]
}
```

**Point in Time (PIT):** Creates a lightweight view of the index state at the time of creation.
Subsequent searches using the same PIT ID see a consistent snapshot, even if documents
are added/modified during pagination. Essential for reliable bulk exports.

```
```

- `from + size` limit: **10,000 hits** — fails beyond that
- `search_after` → for user-facing deep pagination
- PIT + `search_after` → for bulk export, consistent snapshot
- Scroll API → deprecated for user pagination, still ok for one-time exports

## Sharding

- **Optimal shard size: 10–50 GB per shard**
  - Recommended by Elastic based on search performance benchmarks
  - Smaller shards = more overhead, slower coordination
  - Larger shards = slower recovery, less parallelism
  - Reference: [Elastic's sizing guide](https://www.elastic.co/guide/en/elasticsearch/reference/current/size-your-shards.html)
- Number of shards is fixed at creation — plan ahead
- Single-node local dev: `number_of_replicas: 0`
- Over-sharding kills performance — start with 1 shard for small indices

## Common Errors

| Error | Cause | Fix |
|---|---|---|
| `cluster_block_exception` | Disk > 85%, cluster read-only | Free disk, reset with `PUT _cluster/settings {"persistent":{"cluster.blocks.read_only_allow_delete": null}}` |
| [REDACTED_SECRET] | Concurrent update | Use `retry_on_conflict: 3` or optimistic locking with `if_seq_no` |
| `circuit_breaker_exception` | Query uses too much memory | Reduce agg scope, add filters first |
| `index_not_found_exception` | Index doesn't exist | Create index or check name |
| `illegal_argument_exception: mapper [x] cannot be changed` | Changing field type after indexing | Reindex to new index |
| Mapping explosion | Dynamic mapping creating too many fields | Set `dynamic: "strict"` and explicit mappings |

## Performance Tips

- `_source: false` + `stored_fields` if you don't need full document
- `"profile": true` in query to see slow clauses
- Avoid leading wildcards `*term` — forces full scan, use `reverse` field instead
- Filter before scoring: `bool.filter` is cached, `bool.must` is not
- Disable `refresh_interval` during heavy indexing, re-enable after


path: references/python-client-9.md
content: # Elasticsearch Python Client 9.x (Read-Only Patterns)

**Note:** This reference covers read-only operations for querying and analytics.

## Install

```bash
pip install "elasticsearch>=9.0.0" python-dotenv
```

Latest stable: **9.1.3** (PyPI, Feb 2026). 9.3.0 released 2026-02-03 — check PyPI for availability.

## Connect

```python
from elasticsearch import Elasticsearch
from dotenv import load_dotenv
import os

load_dotenv()

es = Elasticsearch(
    os.getenv("ELASTICSEARCH_URL"),
    [REDACTED_SECRET]"ELASTICSEARCH_API_KEY")
)

info = es.info()
print(info["version"]["number"])  # "9.3.0"
```

## ⚠️ Breaking Change: No `body=` Parameter

The `body=` parameter was deprecated in 8.x and **removed in 9.x**.
Use keyword arguments directly instead.

```python
# ❌ Old (8.x) — raises TypeError in 9.x
es.search(index="my-index", body={"query": {"match_all": {}}})

# ✅ New (9.x) — keyword args
es.search(index="my-index", query={"match_all": {}})
es.search(index="my-index", query={"match": {"title": "broccoli"}}, size=20)
```

## Common Read-Only Operations — 9.x Syntax

### Search
```python
results = es.search(
    index="fresh-produce",
    query={"semantic": {"field": "semantic_text", "query": "healthy greens"}},
    size=10,
    source=["name", "category", "price"]
)

for hit in results["hits"]["hits"]:
    print(hit["_source"])
```

### Get Mapping (Inspect Index Structure)
```python
mapping = es.indices.get_mapping(index="fresh-produce")
print(mapping["fresh-produce"]["mappings"]["properties"])
```

### Aggregations
```python
results = es.search(
    index="fresh-produce",
    size=0,  # don't need hits, just aggs
    aggs={
        "by_category": {
            "terms": {"field": "category", "size": 10},
            "aggs": {
                "avg_price": {"avg": {"field": "price"}}
            }
        }
    }
)

for bucket in results["aggregations"]["by_category"]["buckets"]:
    print(f"{bucket['key']}: avg ${bucket['avg_price']['value']:.2f}")
```

### Check if Inference Endpoint Exists
```python
from elasticsearch import NotFoundError

try:
    endpoint = es.inference.get(inference_id="jina-embeddings-v3")
    print(f"Endpoint exists: {endpoint['service']}")
except NotFoundError:
    print("Endpoint not found")
```

## Type Hints (9.x)

```python
from elasticsearch import Elasticsearch
from typing import Dict, Any

def connect() -> Elasticsearch:
    return Elasticsearch(ES_URL, [REDACTED_SECRET])

def search_products(es: Elasticsearch, query: str) -> Dict[str, Any]:
    return es.search(index="products", query={"match": {"name": query}})
```

## Error Handling

```python
from elasticsearch import NotFoundError, BadRequestError, AuthenticationException

try:
    es.indices.get(index="my-index")
except NotFoundError:
    print("Index doesn't exist")
except AuthenticationException:
    print("Invalid API key")
except BadRequestError as e:
    print(f"Bad request: {e.error}")
```

## Environment Pattern

**For OpenClaw workspace:** `~/.openclaw/workspace-[name]/.env`
```bash
ELASTICSEARCH_URL=http://localhost:9200
ELASTICSEARCH_API_KEY=base64encodedkey==
JINA_API_KEY=jina_xxxxxxxxxx
```

**For standalone Python projects:** Create `.env` in your project root:
```python
from dotenv import load_dotenv
import os

load_dotenv()  # loads from .env in current directory

ES_URL    = os.getenv("ELASTICSEARCH_URL")
ES_APIKEY = os.getenv("ELASTICSEARCH_API_KEY")
JINA_KEY  = os.getenv("JINA_API_KEY")

# Validate before using
missing = [k for k, v in {"ES_URL": ES_URL, "ES_APIKEY": ES_APIKEY}.items() if not v]
if missing:
    raise ValueError(f"Missing env vars: {missing}")
```

Always add `.env` to `.gitignore`.

## Suppress SSL Warning (LibreSSL on macOS)

```python
import warnings
import urllib3
warnings.filterwarnings("ignore", category=urllib3.exceptions.NotOpenSSLWarning)
```

Common on macOS with system Python 3.9 — harmless but noisy.


path: references/semantic-search.md
content: # Semantic Search — JINA & semantic_text

## semantic_text Field Type (ES 8.11+, recommended)

The simplest way to add semantic search. ES handles embedding generation automatically
at index time and query time via the Inference API — no manual vector management needed.

```json
PUT my-index
{
  "mappings": {
    "properties": {
      "title":   { "type": "text" },
      "content": { "type": "text" },
      "semantic_content": {
        "type": "semantic_text",
        "inference_id": "my-inference-endpoint"
      }
    }
  }
}
```

Index a document — embedding generated automatically:
```json
POST my-index/_doc
{
  "title": "Fresh Broccoli",
  "content": "Nutritious green broccoli, rich in vitamins.",
  "semantic_content": "Fresh Broccoli Nutritious green broccoli, rich in vitamins. vegetables"
}
```

Query:
```json
POST my-index/_search
{
  "query": {
    "semantic": {
      "field": "semantic_content",
      "query": "healthy leafy greens"
    }
  }
}
```

## Inference Endpoints — Setup

### JINA v3 Inference Endpoint

```json
PUT _inference/text_embedding/jina-embeddings-v3
{
  "service": "jinaai",
  "service_settings": {
    "api_key": "jina_xxxxxx",
    "model_id": "jina-embeddings-v3"
  }
}
```

**Model details:**
- `jina-embeddings-v3` — 1024 dimensions, multilingual (100+ languages)
- Requires JINA API key: https://jina.ai/embeddings (free tier available)
- Dense vector embeddings via External Inference API
- ⚠️ Every query and indexing operation calls api.jina.ai

**Check existing inference endpoints:**
```bash
GET _inference
```

## semantic_text vs manual dense_vector

| | semantic_text | dense_vector (manual) |
|---|---|---|
| Embedding generation | Automatic | Manual (you call the API) |
| Complexity | Low | High |
| Flexibility | Less | More |
| Recommended | ✅ Yes, default choice | Only if you need custom control |

## Troubleshooting

- `inference_not_found` → inference endpoint doesn't exist, check with `GET _inference` or create it first
- Slow first query after idle → external API cold start, normal on free tier
- Embeddings not updating → re-index document, semantic_text does not auto-update on inference model change
- `unauthorized` → check JINA API key validity and quotas


path: references/vector-search.md
content: # Vector Search — kNN, dense_vector, Hybrid Search (RRF)

## dense_vector Mapping (ES 9.x)

```json
PUT my-index
{
  "mappings": {
    "properties": {
      "text": { "type": "text" },
      "embedding": {
        "type": "dense_vector",
        "dims": 1024,
        "index": true,
        "similarity": "cosine",
        "index_options": {
          "type": "bbq_hnsw",
          "m": 16,
          "ef_construction": 100,
          "rescore_vector": { "oversample": 3.0 }
        }
      }
    }
  }
}
```

### bbq_hnsw (ES 9.x — recommended)
- **bbq** = Binary Quantization — compresses vectors, 4-32x memory reduction
- `rescore_vector.oversample: 3.0` — fetches 3x candidates then re-ranks with full precision
- Better than plain `hnsw` for production: same recall, much less memory
- Use `similarity: "cosine"` for normalized embeddings (most models)

### Similarity options
| | Use when |
|---|---|
| `cosine` | Normalized vectors (most embedding models) — default choice |
| `dot_product` | Already normalized, slightly faster |
| `l2_norm` | Euclidean distance, image/spatial search |

## kNN Query

### On-the-fly inference (via inference endpoint)
```json
POST my-index/_search
{
  "knn": {
    "field": "embedding",
    "query_vector_builder": {
      "text_embedding": {
        "model_id": "jina-embeddings-v3",
        "model_text": "healthy leafy greens"
      }
    },
    "k": 10,
    "num_candidates": 100
  },
  "_source": ["title", "category", "price"]
}
```

### Pre-computed vector
```json
POST my-index/_search
{
  "knn": {
    "field": "embedding",
    "query_vector": [0.23, -0.87, 0.14, ...],
    "k": 10,
    "num_candidates": 100
  }
}
```

### Tuning
- `k` — number of results to return (final)
- `num_candidates` — candidates evaluated per shard before ranking (trade recall vs speed)
- Rule of thumb: `num_candidates` = 5-10x `k`, min 100
- For better recall: increase `num_candidates` (slower but more accurate)

## Hybrid Search — BM25 + kNN with RRF

Best of both worlds: exact keyword matching + semantic understanding.
RRF (Reciprocal Rank Fusion) merges rankings without needing score normalization.

```json
POST my-index/_search
{
  "sub_searches": [
    {
      "query": {
        "multi_match": {
          "query": "healthy leafy greens",
          "fields": ["title^3", "description^2", "category"]
        }
      }
    },
    {
      "knn": {
        "field": "embedding",
        "query_vector_builder": {
          "text_embedding": {
            "model_id": "jina-embeddings-v3",
            "model_text": "healthy leafy greens"
          }
        },
        "k": 10,
        "num_candidates": 100
      }
    }
  ],
  "rank": {
    "rrf": {
      "rank_constant": 60,
      "window_size": 100
    }
  }
}
```

### RRF Parameters
- `rank_constant` (default 60) — smoothing factor; higher = less aggressive re-ranking
- `window_size` (default 100) — number of top docs from each sub_search to merge
- No score normalization needed — RRF uses rank positions, not raw scores

### When to use hybrid vs pure semantic
| | Pure semantic | Hybrid (RRF) |
|---|---|---|
| Intent-based queries | ✅ Best | ✅ Good |
| Exact term/ID lookups | ❌ Can miss | ✅ Catches |
| Multilingual | ✅ | ✅ |
| Very small index (<100 docs) | Overkill | Overkill |
| Production default | Consider | ✅ Recommended |

## Hybrid with Filters

Add filters without affecting scoring:
```json
POST my-index/_search
{
  "sub_searches": [ ... ],
  "rank": { "rrf": {} },
  "post_filter": {
    "bool": {
      "filter": [
        { "term": { "on_sale": true } },
        { "range": { "price": { "lte": 20 } } }
      ]
    }
  }
}
```

## Ingest Pipeline for Auto-Embedding

Generate embeddings at index time without changing application code:

```json
PUT _ingest/pipeline/jina-embedding-pipeline
{
  "processors": [
    {
      "inference": {
        "model_id": "jina-embeddings-v3",
        "input_output": {
          "input_field": "text_for_embedding",
          "output_field": "embedding"
        }
      }
    }
  ]
}

POST my-index/_doc?pipeline=jina-embedding-pipeline
{
  "title": "Fresh Broccoli",
  "text_for_embedding": "Fresh Broccoli Nutritious green broccoli. vegetables"
}
```


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