Vector & Hybrid Search Guide

Covers the full lifecycle of vector or hybrid search with Elasticsearch — planning, data modeling, search implementation, and optimization. All API examples use SENSE syntax for Kibana Dev Tools.

Conversation flow — return to onboarding

This skill provides deep implementation detail for vector and hybrid search. It is not the main conversation driver.

After applying the guidance here, re-read /elasticsearch-onboarding to resume the structured onboarding playbook (Steps 1–7: intent → data → mapping → build → test → iterate). That playbook controls sequencing, the one-question-at-a-time rule, and the Dev Tools API-snippet workflow. If /elasticsearch-onboarding has not been loaded yet in this conversation, load it now — it is the primary conversation flow for all Elasticsearch search onboarding.

Decision: Embedding Strategy

Ask these routing questions first:

1. "Are you already generating embeddings?" → Yes → dense_vector path. Briefly offer semantic_text as a simpler alternative.
2. "What version of Elasticsearch?" → Below 8.15 → semantic_text unavailable, use dense_vector.

Option	When to Use
Built-in via EIS	Default for Cloud (Serverless or ECH) on 8.15+. No ML node cost. Jina v3 is the current default dense model for semantic_text.
Third-party (OpenAI, Cohere)	Existing model contract or specific model requirement.
Self-hosted	Custom fine-tuned models deployed on ML nodes.

Decision: Vector Field Type

Option	Field Type	When to Use
semantic_text	semantic_text	8.15+, no existing vectors. Default recommendation — auto chunking, auto embedding.
dense_vector	dense_vector	Bringing your own vectors, need dims/similarity control, or pre-8.15.

semantic_text Mapping (Default)

Minimal — works out of the box on Serverless (uses the platform default model, currently Jina):

PUT /my-index
{
  "mappings": {
    "properties": {
      "content": { "type": "semantic_text" },
      "title": { "type": "text" },
      "category": { "type": "keyword" }
    }
  }
}

With a specific inference endpoint:

PUT /my-index
{
  "mappings": {
    "properties": {
      "content": {
        "type": "semantic_text",
        "inference_id": "my-inference-endpoint"
      }
    }
  }
}

dense_vector Mapping

PUT /my-index
{
  "mappings": {
    "properties": {
      "content": { "type": "text" },
      "content_embedding": {
        "type": "dense_vector",
        "dims": 1536,
        "index": true,
        "similarity": "cosine"
      },
      "category": { "type": "keyword" }
    }
  }
}

Set dims to match the embedding model output (OpenAI text-embedding-3-small = 1536, Jina v3 = 1024, E5-small = 384).

Before generating inference endpoint config, check EIS docs for current model IDs. Jina v3 is the current default dense model for semantic_text; Jina v5-small is available for cost-sensitive workloads. Model IDs change regularly.

Decision: Search Type

Option	When to Use
Pure kNN	All queries are semantic/meaning-based, no exact term matching needed.
Hybrid (BM25 + kNN via RRF)	Users search with both keywords AND natural language. Default recommendation.
Semantic via semantic_text	Using semantic_text field — simplest semantic search.

Semantic Search (semantic_text)

POST /my-index/_search
{
  "retriever": {
    "standard": {
      "query": {
        "semantic": {
          "field": "content",
          "query": "how do I configure index mappings"
        }
      }
    }
  }
}

Pure kNN (dense_vector)

POST /my-index/_search
{
  "retriever": {
    "knn": {
      "field": "content_embedding",
      "query_vector": [0.1, 0.2, 0.3],
      "k": 10,
      "num_candidates": 100
    }
  }
}

Hybrid Search with RRF

POST /my-index/_search
{
  "retriever": {
    "rrf": {
      "retrievers": [
        {
          "standard": {
            "query": {
              "multi_match": {
                "query": "elasticsearch index mapping",
                "fields": ["title^2", "content"]
              }
            }
          }
        },
        {
          "knn": {
            "field": "content_embedding",
            "query_vector": [0.1, 0.2, 0.3],
            "k": 50,
            "num_candidates": 100
          }
        }
      ],
      "window_size": 100,
      "rank_constant": 60
    }
  }
}

Add filter clauses to both retrievers for filtered hybrid search.

Reranking

Start without reranking. Add text_similarity_reranker if relevance isn't good enough after tuning:

POST /my-index/_search
{
  "retriever": {
    "text_similarity_reranker": {
      "retriever": { "rrf": { "retrievers": [ ... ] } },
      "field": "content",
      "inference_id": "my-reranker-endpoint",
      "inference_text": "your query",
      "rank_window_size": 50
    }
  }
}

EIS provides managed rerankers (currently Jina Reranker v2 and v3). Check reranker docs for current setup.

Production Optimization

Quantization — reduces vector memory footprint:

Type	Memory Reduction	Recall Impact
hnsw	Baseline	Baseline
int8_hnsw	~4x	Minimal
int4_hnsw	~8x	Small
bbq_hnsw	~32x	Moderate

Shard sizing — target 10-50 GB per shard, max 200M docs per shard.

Common Follow-ups

Question	Answer
"Results aren't relevant"	Tune window_size and rank_constant in RRF. Run _rank_eval with test queries.
"Memory is too high"	Add int8_hnsw quantization to dense_vector mapping and reindex.
"How do I weight keyword vs semantic?"	Adjust RRF window_size — higher favors semantic, lower favors BM25.