Traditional keyword-based search methods have long been the basis of enterprise search systems. However, as organizations generate large, diverse datasets, these systems struggle to return relevant results for exact queries, synonyms, and contextual semantics. OpenSearch, an open-source search and analytics suite forked from Elasticsearch and now part of the Linux Foundation, provides transformation capabilities through OpenSearch Vector Engine, enabling semantic, hybrid, and AI-enhanced search functionality. With detailed code snippets, deployment strategies, diagrams, and real-world use cases, this comprehensive guide covers the technical basics, practical applications, and strategic benefits of modernizing enterprise search by leveraging OpenSearch’s vector capabilities.
Vector search fundamentals: Elevating beyond keywords
In classical keyword matching, search engines rely on lexical analysis using pointers and inverse algorithms such as BM25 to rank documents based on keyword frequency and position. Although effective, these methods struggle to capture intent, measure semantic similarity, and handle multimodal data such as images and audio.
OpenSearch addresses these shortcomings with the k-nearest neighbors (k-NN) vector search extension, which can index and query high-dimensional vector representations from deep learning models, such as BERT (Bidirectional Encoder Representations from Transformers), Sentence Transformers, or domain-specific embeddings.
These embeddings encode semantic meaning, enabling queries to retrieve documents or records based on contextual proximity rather than exact word matches. For example, the terms “optimizing cloud infrastructure” and “improving cloud system performance” will cluster closely together in the vector space even though they are lexically distinct.
Setting up vector-enabled indexes
Setting up vector-enabled indexes in OpenSearch involves defining custom mappings that specify vector fields, their dimensionality, and search configurations. Here is an example of a configuration tuned for typical 768-dimensional text embeddings generated by transformers:
{
"settings": {
"index": {
"knn": true
}
},
"mappings": {
"properties": {
"text_embedding": {
"type": "knn_vector",
"dimension": 768,
"method": {
"name": "hnsw",
"space_type": "cosinesimil",
"engine": "faiss"
}
},
"title": { "type": "text" },
"content": { "type": "text" },
"metadata": { "type": "keyword" }
}
}
}
The Hierarchical Navigable Small World (HNSW) graph algorithm accelerates approximate nearest neighbour search, crucial for scalability in enterprise datasets with millions of vectors.
Embedding generation pipelines
Organizations develop efficient processes to generate and process embeddings, often integrating machine learning models into data processing frameworks such as Apache Kafka, Apache Spark, or custom batch jobs. Offline preprocessing extracts embeddings from text, images, or audio that is massively indexed by OpenSearch, along with metadata such as document IDs, timestamps or category tags.
Like Python and Java clients, OpenSearch provides APIs for inference and processing. For example, the Python code snippet used for creation and indexing includes documents using Hugging Face adapters and the opensearch-py client:
python
from transformers import AutoTokenizer, AutoModel
import torch
from opensearchpy import OpenSearch
# Initialize tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
model = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
client = OpenSearch(hosts=[{'host': 'localhost', 'port': 9200}])
def get_embedding(text):
inputs = tokenizer(text, return_tensors='pt', truncation=True)
outputs = model(**inputs)
embeddings = outputs.last_hidden_state.mean(dim=1)
return embeddings.detach().numpy()[0]
docs = [
{"id": "1", "title": "Cloud Optimization", "content": "Techniques to optimize cloud infrastructure"},
{"id": "2", "title": "Kubernetes Scaling", "content": "Best practices for scaling Kubernetes workloads"}
]
for doc in docs:
embedding = get_embedding(doc['content']).tolist()
client.index(index="enterprise_docs", id=doc['id'], body={
"title": doc['title'],
"content": doc['content'],
"text_embedding": embedding,
"metadata": "tech-doc"
})
Hybrid search: Combining lexical and semantic strengths
While vector search excels in capturing intent and semantics, lexical search maintains strength in exact matching and filtering. OpenSearch supports hybrid search that combines both modalities, improving recall and precision in enterprise search.
Hybrid search entails issuing concurrent lexical and vector queries, normalizing the scores, and fusing them with methods like Reciprocal Rank Fusion (RRF) or weighted averaging. For example, a SQL-like OpenSearch query combining BM25 and k-NN on the same index might appear as follows:
POST /enterprise_docs/_search
{
"query": {
"hybrid": {
"queries": [
{ "match": { "content": "cloud infrastructure optimization" }},
{ "knn": { "text_embedding": { "vector": [0.15, 0.32, ...], "k": 10 }}}
],
"normalization": "min_max",
"combination": "rrf"
}
}
}
Hybrid search pipelines often apply lexical filters before vector ranking to reduce search space, enhancing latency performance and relevance in large-scale deployments. Enterprises have reported 15–25% improvements in search precision and user satisfaction metrics by integrating hybrid models.
Use cases in enterprise search
- Knowledge management: Organizing product manuals, HR policies, and engineering documents for fast, context-aware retrieval.
- Customer support: Powering intelligent chatbots with retrieval-augmented generation (RAG) architectures that retrieve exact policy snippets or historical ticket information grounded in vector searches.
- E-commerce: Enhancing catalog search to handle natural language queries, synonyms, and recommendation systems using image and text embeddings.
- Security analytics: Correlating log data and threat intelligence for proactive incident detection.
RAG with OpenSearch
RAG combines OpenSearch’s vector retrieval with large language models (LLMs) to generate contextually accurate and up-to-date responses in AI-assisted applications. OpenSearch acts as the retrieval layer, fetching semantically relevant documents or snippets fed as contextual evidence into generative models like GPTs or custom transformers.
RAG solution architecture
The RAG query is embedded and passed to OpenSearch, which retrieves the top-k relevant documents using vector search. These documents provide factual context to the LLM, enhancing answer accuracy while minimizing hallucinations.
Python client integration example
python
from transformers import AutoTokenizer, AutoModel
from opensearchpy import OpenSearch
tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
model = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
client = OpenSearch(hosts=[{'host': 'localhost', 'port': 9200}])
def embed_query(query):
inputs = tokenizer(query, return_tensors='pt', truncation=True)
outputs = model(**inputs)
embedding = outputs.last_hidden_state.mean(dim=1).detach().numpy()[0]
return embedding.tolist()
def search_open_search(embedding, k=5):
response = client.search(
index='enterprise_docs',
body={
"size": k,
"query": {
"knn": {
"text_embedding": {
"vector": embedding,
"k": k
}
}
}
}
)
return [hit['_source'] for hit in response['hits']['hits']]
query = "Best practices for cloud system scaling"
embedding = embed_query(query)
results = search_open_search(embedding)
for doc in results:
print(doc['title'], ":", doc['content'])
Operationalizing OpenSearch Vector Engine:
The following sections give more details for it:
Deployment and scalability
Production-grade deployments should leverage:
- Sharding and replication: Manage indexes and shards to improve performance and fault tolerance..
- Security: Role-based access control (RBAC), encryption in transit, and auditing.
- Managed services: Amazon OpenSearch Service with serverless vector search capabilities, auto scaling, and cost optimization on AWS Graviton processors.
- Monitoring: You can monitor query response time, index health, and vector-related metrics using OpenSearch’s dashboards and plugins.
Kubernetes and DevOps integration
Helm charts and Terraform-based Infrastructure as Code promote repeatable, scalable installations in containerized environments.
The following is an example Helm snippet:
text
opensearch:
cluster:
name: enterprise-search-cluster
security:
enabled: true
plugins:
- opensearch-knn
Future roadmap
OpenSearch’s roadmap highlights powerful AI capabilities for personalized conversational search, deeper multimodal search (including images and audio), and integrated session memory modules to preserve context across extended user interactions. These enhancements provide a highly personalized real-time search experience that adapts to complex business needs.
Conclusion
The OpenSearch Vector Engine enables organizations to transcend the limitations of traditional keywords and deliver scalable, AI-driven, hybrid semantic search across vast, heterogeneous data spaces. From knowledge management to AI-driven RAG applications, organizations benefit from improved accuracy, user experience, and operational efficiency. Deploying OpenSearch’s vector technologies today puts organizations at the forefront of next-generation enterprise search innovation.
