Opensearch Knn

OpenSearch k-NN

What Changed at 2.13+

OpenSearch 2.13 through 2.17 made the k-NN plugin competitive with purpose-built stores:

• Lucene engine supports HNSW with filter-while-search (efficient filtering).
• Faiss engine added IVF + PQ + binary + int8 quantization.
• Neural search plugin: server-side embedding via external models (SageMaker, Bedrock).
• Radial search: return all neighbors within a max distance.
• Hybrid search plugin: BM25 + kNN with configurable score combiners.

Engines: nmslib vs faiss vs lucene

Default picks:

• Small + heavily filtered workloads: lucene.
• Large, quantization-friendly: faiss.
• Existing nmslib indexes: migrate to lucene or faiss.

Index Mapping

PUT /docs
{
  "settings": {
    "index": {
      "knn": true,
      "knn.algo_param.ef_search": 100
    }
  },
  "mappings": {
    "properties": {
      "embedding": {
        "type": "knn_vector",
        "dimension": 1024,
        "data_type": "float",
        "space_type": "cosinesimil",
        "method": {
          "name": "hnsw",
          "engine": "lucene",
          "parameters": {
            "m": 24,
            "ef_construction": 200
          }
        }
      },
      "tenant_id": {"type": "keyword"},
      "source":    {"type": "keyword"},
      "created_at":{"type": "date"},
      "title":     {"type": "text"},
      "body":      {"type": "text"}
    }
  }
}

Rule of thumb: m 16-32, ef_construction 100-400, ef_search 50-200. Larger values trade recall for latency/memory.

faiss with int8 Scalar Quantization

"method": {
  "name": "hnsw",
  "engine": "faiss",
  "space_type": "l2",
  "parameters": {
    "m": 16,
    "ef_construction": 200,
    "encoder": {
      "name": "sq",
      "parameters": {"type": "fp16"}
    }
  }
}

fp16 cuts memory 2x with negligible recall loss. Use int8 for 4x reduction with small recall tradeoff.

faiss with IVF + PQ (billion-scale)

"method": {
  "name": "ivf",
  "engine": "faiss",
  "space_type": "l2",
  "parameters": {
    "nlist": 2048,         // ceil(4 * sqrt(num_vectors))
    "nprobes": 16,
    "encoder": {
      "name": "pq",
      "parameters": {"m": 32, "code_size": 8}
    }
  }
}

IVF_PQ compresses ~8-32x with 2-10% recall loss. Always pair with oversampling + rescoring if possible.

Bulk Ingest

# pip install opensearch-py
from opensearchpy import OpenSearch, helpers

client = OpenSearch(
    hosts=[{"host": "search-xxx.us-east-1.es.amazonaws.com", "port": 443}],
    http_auth=(user, pwd),
    use_ssl=True, verify_certs=True,
)

def gen():
    for d in docs:
        yield {
            "_index": "docs",
            "_id": d["id"],
            "_source": {
                "embedding": d["vec"],
                "tenant_id": d["tenant_id"],
                "source": d["source"],
                "title": d["title"],
                "body": d["body"],
                "created_at": d["created_at"],
            },
        }

helpers.bulk(client, gen(), chunk_size=500, max_retries=3)

Refresh off during ingest for throughput:

client.indices.put_settings(index="docs", body={"index": {"refresh_interval": "-1"}})
# ... bulk load ...
client.indices.put_settings(index="docs", body={"index": {"refresh_interval": "1s"}})
client.indices.forcemerge(index="docs", max_num_segments=1)

forcemerge after bulk load builds a single HNSW segment — big query-time speedup.

k-NN Query

body = {
    "size": 10,
    "query": {
        "knn": {
            "embedding": {
                "vector": q_vec.tolist(),
                "k": 10,
                "filter": {
                    "bool": {
                        "must": [
                            {"term": {"tenant_id": "acme"}},
                            {"terms": {"source": ["kb", "faq"]}},
                        ],
                        "must_not": [
                            {"term": {"archived": True}},
                        ],
                    },
                },
            },
        },
    },
}
result = client.search(index="docs", body=body)

Lucene engine evaluates the filter during graph traversal (pre-filter). Faiss filters post by default — switch to method_parameters: {ef_search: ...} and accept post-filter trim, or use filter_mode: "pre_filter" if available.

Radial Search (max distance)

body = {
    "query": {
        "knn": {
            "embedding": {
                "vector": q_vec.tolist(),
                "max_distance": 0.3,     # cosine: 1 - similarity
                "filter": {"term": {"tenant_id": "acme"}},
            },
        },
    },
}

Returns all neighbors under the threshold. Useful for dedup, novelty detection, and "give me everything similar" semantics rather than top-k.

Neural Search (Server-Side Embedding)

OpenSearch's neural plugin embeds queries and documents via external models. Register a model connector once, then queries carry text instead of vectors.

POST /_plugins/_ml/models/_register
{
  "name": "cohere-embed-multilingual-v3",
  "function_name": "remote",
  "connector_id": "<your connector id>"
}

POST /_plugins/_ml/models/<model_id>/_deploy

Create an ingest pipeline that fills the embedding field from body:

PUT /_ingest/pipeline/embed-pipeline
{
  "processors": [{
    "text_embedding": {
      "model_id": "<model_id>",
      "field_map": {"body": "embedding"}
    }
  }]
}

Attach pipeline to the index via index.default_pipeline = embed-pipeline. Inserts supply body; OpenSearch fills embedding automatically.

Query by text:

GET /docs/_search
{
  "query": {
    "neural": {
      "embedding": {
        "query_text": "how to refresh an oauth token",
        "model_id": "<model_id>",
        "k": 10
      }
    }
  }
}

Neural search removes app-side embedding — one less service to run.

Hybrid (BM25 + kNN) with Score Combiner

POST /_search/pipeline/hybrid-pipeline
{
  "description": "Hybrid BM25 + kNN",
  "phase_results_processors": [{
    "normalization-processor": {
      "normalization": {"technique": "min_max"},
      "combination": {"technique": "arithmetic_mean", "parameters": {"weights": [0.4, 0.6]}}
    }
  }]
}

GET /docs/_search?search_pipeline=hybrid-pipeline
{
  "query": {
    "hybrid": {
      "queries": [
        {"match": {"body": "oauth refresh token"}},
        {"knn": {"embedding": {"vector": q_vec, "k": 50}}}
      ]
    }
  },
  "size": 10
}

Techniques include min_max and l2 normalization, and arithmetic_mean, geometric_mean, harmonic_mean combiners. Tune weights on a gold set.

Memory Estimation

HNSW memory (per shard):

bytes_per_vector = 4 * dim + 8 * M
total = num_vectors * bytes_per_vector * (1 + overhead)

Example: 10M vectors at 1024-d with M=16: 10^7 * (4*1024 + 8*16) = 10^7 * 4224 = 42 GB per shard

Plan ram.percentage.knn accordingly (the k-NN circuit breaker defaults to 50% of JVM heap). With quantization (fp16 or int8), halve or quarter the number.

Sizing Guidelines

Replica shards double memory — factor in HA cost.

Anti-Patterns

Production Checklist

• Engine chosen per workload: lucene (filters) / faiss (scale)
• Quantization tuned (fp16 baseline; int8 / PQ at scale)
• m, ef_construction, ef_search benchmarked on gold set
• Bulk ingest with refresh_interval=-1 + forcemerge
• Hybrid search pipeline with normalization + weights
• Neural search connector + pipeline configured (if used)
• Aliases fronting the index for zero-downtime reindex
• Circuit breaker (knn.memory.circuit_breaker.limit) sized
• Snapshot repository configured (S3) with rolling schedule
• Recall and p95 latency dashboards per shard
• Multi-AZ replica shards for HA