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📘 My Notes: Creating Index and Embedding Documents in Elastic VectorDB

These are my notes on how to create a vector-capable index in Elasticsearch and embed documents using Sentence Transformers (or similar tools) to support hybrid and semantic search.

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🧠 What Are Embeddings, Vectors, and a VectorDB?

In natural language processing (NLP), an embedding is a numerical representation of text.

A Vector Database (VectorDB) is a special kind of database optimized to store and search high-dimensional vectors. Instead of matching exact words, it performs approximate nearest neighbor (ANN) search, which allows retrieval of the most semantically similar vectors based on distance functions (like cosine similarity).

Elasticsearch (version 8+) supports native vector search, so it can act as a fully functional VectorDB. It lets you:

• Store dense_vector fields alongside metadata (like timestamp, environment)

• Run hybrid queries using both vector similarity and filters (e.g., time ranges, terms)

• Use it with traditional search pipelines and REST APIs Sentence Transformers convert phrases like “CPU usage is high” into a high-dimensional vector (e.g., 384 dimensions). These vectors capture semantic meaning.

• Text with similar meanings will have vectors that are closer in the vector space.

• This allows semantic search, even when exact keywords don’t match.

In Elasticsearch, we store these vectors in a dense_vector field. When a user enters a query, it’s embedded using the same model, and Elasticsearch compares it with stored vectors using cosine similarity or other distance metrics.

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🔧 Step-by-Step Overview

📌 1. Create a Dense Vector Index in Elasticsearch

What is dense_vector?

The dense_vector field type in Elasticsearch is used to store high-dimensional numerical arrays (vectors). These vectors are typically generated from machine learning models like transformers and are used to represent the semantic meaning of text.

• Each document gets a dense_vector that captures the meaning of its summary.
• Elasticsearch can compute similarity between vectors using functions like cosine similarity.
• Vectors are usually 384 to 1024 dimensions depending on the model.

Elasticsearch uses Approximate Nearest Neighbor (ANN) search internally to find similar vectors quickly — making it suitable for semantic and hybrid search applications.

You must define the number of dimensions (e.g., "dims": 384) when creating the index. You cannot change this after index creation.

To enable vector search in Elasticsearch, we must first create an index with the appropriate field mappings. This includes a dense_vector field where each document’s embedding will be stored.

• summary: A plain text field to describe the document in natural language.
• embedding: A dense vector (list of 384 floats) representing the summary.
• timestamp: A date field for filtering documents by time.
• environment: A keyword field to identify production, staging, etc.

You can create the index using the following API call (use Kibana Dev Tools, curl, or Elasticsearch Python client):

PUT kibana-metrics-vector-1
{
  "mappings": {
    "properties": {
      "summary": { "type": "text" },
      "embedding": { "type": "dense_vector", "dims": 384 },
      "timestamp": { "type": "date" },
      "environment": { "type": "keyword" }
    }
  }
}

📌 Note: The dense_vector field requires Elasticsearch 8+ with vector capabilities enabled. If you are using Elastic Cloud, this is already supported.

Use Kibana Dev Tools or curl to run this request. This sets up the index to accept document summaries and their corresponding dense vector embeddings.

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📝 2. Generate Embeddings and Index Documents

Once the vector index is created, the next step is to populate it with documents that include both metadata and semantic vector embeddings.

Steps Involved:

Flattening the Source Document:

Metric data often comes in nested structures (e.g., system.cpu.load).

These must be flattened into key-value pairs for easier summarization.

Generating a Summary:

Construct a human-readable string that summarizes the document.

For example: "CPU: 45.2%, Memory: 95.5%, Disk: 96.0%".

This text captures the core meaning of the metrics in natural language.

Vectorizing the Summary:

Use a text embedding model (such as Sentence Transformers) to convert the summary into a high-dimensional vector (e.g., 384 dimensions).

This vector encodes the semantic meaning of the summary.

Building the Document:

Combine the summary, embedding vector, and metadata (timestamp, environment) into a single JSON object.

Indexing into Elasticsearch:

Insert the JSON object into the previously created vector index.

Use the same document _id as the source if you want to link it back to the original metric index.

This process ensures that your vector index contains semantically rich, searchable summaries that can be queried using natural language and filtered with structured fields like time or environment.

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🔄 Flowchart Diagram

[1] Fetch Raw Metrics Document (from kibana-metrics index)
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         v
[2] Flatten Nested JSON Fields
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         v
[3] Generate Human-Readable Summary (e.g., "CPU: 45.2%, Memory: 95.5%")
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         v
[4] Compute Text Embedding Vector (using external embedding service)
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         v
[5] Construct Document with:
     - summary (text)
     - embedding (dense_vector)
     - timestamp, environment
         |
         v
[6] Index into Elasticsearch Vector Index (kibana-metrics-vector-1)
         |
         v
[7] Document Ready for Semantic + Filtered Query

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✅ Summary

• Create index with dense_vector field
• Flatten documents and build readable summaries
• Use SentenceTransformer to embed
• Store _id, summary, timestamp, environment, and vector in Elasticsearch

You can now run semantic or hybrid queries against this index using script_score and filters.

Let me know if you want to extend this to real-time ingestion or REST API integration!