Elasticsearch Tutorial: How to Generate, Store and Search Embeddings

This tutorial aims to explore the process of generating, storing, and searching embeddings in the context of Elasticsearch, a prominent search engine that supports vector data.

In the realm of Machine Learning (ML), embeddings serve as a cornerstone for a variety of applications, from language processing and recommendations to more complex tasks like image or video analysis. By definition, an embedding is a vector, or an array of numbers, encoding real-world entities such as words, sentences, images, or videos in a way that preserves semantic relationships. For instance, embeddings for similar or related entities tend to lie closer in the vector space, offering a robust basis for comparison and analysis. This feature becomes particularly valuable in search applications, enabling searches that focus on concept similarity rather than exact keyword matches.

Introduction

This tutorial aims to explore the process of generating, storing, and searching embeddings in the context of Elasticsearch, a prominent search engine that supports vector data. Specifically, we will outline how to leverage Alibaba Cloud Elasticsearch as the implementation environment. Our journey will encompass embedding generation using available machine learning models, embedding storage within Elasticsearch's vector database, and the integration of vector and full-text searches for a comprehensive search solution.

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Generating Embeddings

Before we dive into Elasticsearch, let's start by generating embeddings. For this purpose, we'll use a commonly available model such as Google's Universal Sentence Encoder (USE) for sentence embeddings. However, note that the method can be adapted for other entities (like images) using appropriate models (e.g., ResNet for images).
To generate embeddings, you'd typically need to load the desired model using a library like TensorFlow or PyTorch. Here's a simplified example using TensorFlow Hub to generate embeddings for sentences:

import tensorflow_hub as hub
import numpy as np

# Load the model
embed = hub.load("https://tfhub.dev/google/universal-sentence-encoder/4")

# Define sentences
sentences = ["This is a tutorial on Elasticsearch.",
             "Embeddings are useful for semantic search."]

# Generate embeddings
sentence_embeddings = embed(sentences)

# Convert to numpy array for easier handling
sentence_embeddings_np = np.array(sentence_embeddings)

print(sentence_embeddings_np)

Storing Embeddings in Elasticsearch

Once you've generated the embeddings, the next step is storing them in Elasticsearch for future retrieval and search. Alibaba Cloud Elasticsearch provides a scalable and efficient environment for this purpose. Considering Elasticsearch's support for vector data (introduced in version 7.3 with the dense_vector type), we can directly store embeddings within an Elasticsearch index.
Here's how to define an index with a dense_vector field for storing sentence embeddings:

PUT /sentence_embeddings
{
  "mappings": {
    "properties": {
      "sentence": {
        "type": "text"
      },
      "embedding": {
        "type": "dense_vector",
        "dims": 512  // Ensure this matches the dimensions of your embeddings
      }
    }
  }
}

To insert embeddings into this index, simply structure your documents as follows:

POST /sentence_embeddings/_doc
{
  "sentence": "This is a tutorial on Elasticsearch.",
  "embedding": [/* Your embedding array here */]
}

Searching Embeddings with Elasticsearch

The unique advantage of storing embeddings in Elasticsearch is the capability to perform similarity searches. This involves finding documents with embeddings closest to a query vector. Here's a basic example using cosine similarity (note the cosineSimilarity function) to search for similar sentences:

GET /sentence_embeddings/_search
{
  "query": {
    "script_score": {
      "query": {
        "match_all": {}
      },
      "script": {
        "source": "cosineSimilarity(params.query_vector, 'embedding') + 1.0",
        "params": {
          "query_vector": [/* Your query embedding here */]
        }
      }
    }
  }
}

Combining Full-Text and Vector Searches

Elasticsearch excels not only in vector searches but also in traditional full-text searches. Combining these capabilities allows for powerful, hybrid search strategies. For instance, you can first filter documents based on keyword matches and subsequently rank these filtered results based on embedding similarity. This hybrid approach leverages both semantic context and keyword relevance, delivering a rich, nuanced search experience.
Here's a conceptual example of combining both search types:

GET /sentence_embeddings/_search
{
  "query": {
    "bool": {
      "must": {
        "match": {
          "sentence": "Elasticsearch tutorial"
        }
      },
      "should": {
        "script_score": {
          "query": {
            "match_all": {}
          },
          "script": {
            "source": "cosineSimilarity(params.query_vector, 'embedding') + 1.0",
            "params": {
              "query_vector": [/* Query embedding */]
            }
          }
        }
      }
    }
  }
}

Conclusion

This tutorial has covered the basics of generating, storing, and searching embeddings using Alibaba Cloud Elasticsearch. By understanding and leveraging embeddings, you can significantly enhance the capabilities of your search applications, moving beyond simple keyword matching to semantic search. This approach allows for a more nuanced and relevant discovery of content, be it text, images, or any other form of data that can be represented as a vector.

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Community

Elasticsearch Tutorial: How to Generate, Store and Search Embeddings

Introduction

Generating Embeddings

Storing Embeddings in Elasticsearch

Searching Embeddings with Elasticsearch

Combining Full-Text and Vector Searches

Conclusion

Start Your Journey with Elasticsearch on Alibaba Cloud for Free

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