Vector Search vs Traditional Search

Vector Search vs Traditional Search
Unveiling the Power of Vector Search 

ChatGPT prompts which could be useful 1 - What is Vector Search? 2 - Difference Between Traditional and Vector Search 3 - The Benefits of Vector Search 4 - Potential Disadvantages of Using Vector Search 5 -  When we shouldn't use vector search 6 - Elastic Search and Vector Search 6.1 - Implementing Vector Search in Elastic Search 7- Alternative of ES in vector search

From social media to e-commerce platforms, the digital landscape is full of data that needs to be searched and indexed efficiently. Traditional keyword-based search methods have been effective in some aspects, but they often fall short of providing relevant and accurate search results.

The search for relevance has led to the development of vector search, a powerful alternative that enables users to find what they need quickly and accurately.

In this article, we will explore the concept of vector search and its implementation in Elastic Search, a leading open-source search and analytics engine.

Vector search is an advanced search method that leverages machine learning models, specifically neural networks, to transform text, images, or other complex data types into numerical representations known as vectors.

Vectors are used to calculate the similarity or distance between data points.

It allows search engines to provide more relevant and accurate search results. Vector search has gained traction in recent years, thanks to its ability to handle semantic relationships between words and its resilience against common search challenges such as typos and synonyms.

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This table highlights the main differences between traditional search and vector search in Elastic Search, demonstrating the advantages of vector search in handling semantic relationships, context, and robustness to variations in text data.

Vector search in Elastic Search offers the ability to search and analyze a broader range of data types compared to traditional text search.

Some examples of data types that can be vector searched but are not possible with traditional text search in Elastic Search are:

  1. Image Data: By using deep learning models like Convolutional Neural Networks (CNNs), images can be converted into feature vectors that capture visual patterns and semantics. These vectors can be used to perform similarity searches and enable image-based retrieval systems.
  2. Audio Data: Audio data, such as music or speech, can be transformed into numerical vectors using techniques like Mel-frequency cepstral coefficients (MFCCs) or embeddings generated by deep learning models. This enables audio similarity searches and content-based audio retrieval.
  3. Video Data: Video data can be analyzed frame by frame or by extracting features through deep learning models like 3D CNNs or recurrent neural networks (RNNs). This creates vector representations of videos that can be searched to enable video content-based retrieval systems.
  4. Graph Data: Graphs can be represented as vectors using techniques like graph embeddings, which capture the structural and relational information of the graph. This allows similarity searches on graph data, enabling tasks like link prediction, node classification, and graph-based recommendation systems.
  5. Multimodal Data: In cases where data consists of multiple modalities (e.g., text, image, audio), vector search can be applied to create a unified representation of the data and perform similarity searches that consider all modalities.

These data types are difficult or impossible to handle using traditional text search in Elastic Search, as text search relies on keyword matching and does not account for the rich semantic information contained within these data types.

By using vector search, you can unlock the potential of these diverse data types and create more advanced search and retrieval systems.

  • Improved Relevance: Vector search provides more relevant search results by understanding the semantic relationships between words and phrases, rather than relying solely on keyword matches.
  • Handling Ambiguity: By considering the context in which words appear, vector search is better equipped to handle ambiguous queries, which can lead to more accurate search results.
  • Robustness to Typos: Vector search is less sensitive to typos or misspellings, as it relies on the broader context and meaning of words to determine relevance.
  • Scalability: Due to its numerical nature, vector search can easily be scaled to handle large amounts of data without sacrificing performance.

While vector search has many advantages over traditional search in Elastic Search, it also has some potential disadvantages:

  1. Computational Complexity: Vector search generally requires more computational resources, as it involves transforming data using machine learning models and calculating similarity or distance between vectors. This can lead to increased processing time and memory requirements compared to traditional text search.
  2. Model Training: To convert data into vectors, machine learning models need to be trained on relevant datasets. This process can be time-consuming and resource-intensive and may require domain-specific knowledge or expertise.
  3. Approximations: In order to improve search efficiency, approximate nearest neighbor (ANN) algorithms may be used to speed up vector search. While this can enhance performance, it may also introduce a trade-off between search accuracy and speed.
  4. Storage Overhead: Storing vector representations of data, in addition to the raw data, can result in increased storage requirements.
  5. The complexity of Implementation: Implementing vector search can be more complex than traditional search, as it requires understanding and working with machine learning models, vector representations, and additional search algorithms.

Despite these potential disadvantages, a vector search is still a powerful tool for enhancing search relevance and handling diverse data types. The key is to strike a balance between the benefits and drawbacks by carefully choosing the right approach for your specific use case and optimizing your implementation for efficiency and accuracy.

There are several scenarios where using vector search in Elastic Search might not be the most appropriate choice:

  1. Simple Queries and Exact Matches: If your use case involves simple queries where exact keyword matches are sufficient and semantic understanding is not necessary, traditional text search could be more efficient and easier to implement.
  2. Limited Computational Resources: Vector search can be computationally intensive due to the need for machine learning models and similarity calculations. If your system has limited resources or you need to prioritize low-latency search, traditional text search might be more suitable.
  3. No Access to Adequate Training Data: Vector search requires training machine learning models on representative datasets. If you do not have access to adequate training data or the data is insufficient to create meaningful vector representations, it may be better to use a traditional text search.
  4. Small Datasets: For small datasets, the benefits of using vector search may not outweigh the costs of implementing and maintaining it. Traditional text search might be more efficient and easier to manage in these cases.
  5. Lack of Expertise: Implementing vector search requires a certain level of expertise in machine learning, as well as an understanding of vector representations and search algorithms. If your team lacks the necessary skills and knowledge, it might be more effective to use traditional text search.

In these situations, using traditional text search in Elastic Search might be more appropriate and efficient. However, it is essential to evaluate the specific requirements of your use case and the trade-offs between traditional and vector search before making a decision.

Elastic Search is an open-source, distributed search and analytics engine built on top of Apache Lucene. It is well-suited for handling large volumes of data and offers many powerful features such as real-time search, distributed indexing, and advanced query capabilities.

Elastic Search has recently added support for vector search, which has opened up new possibilities for improving search relevance and efficiency.

GPT Prompt: "A step-by-step guide for doing vector search with Elastic Search"

Summary: The vectors can then be indexed in Elastic Search using a dense_vector field type. To perform a vector search, a script_score query can be used to calculate custom scores for each document based on cosine similarity between the query vector and document vectors.

curl -X GET "localhost:9200/my_vector_index/_search" -H 'Content-Type: application/json' - { "query": { "script_score": { "query": { "match_all": {} }, "script": { "source": "cosineSimilarity(params.query_vector, doc['my_vector_field']) + 1.0", "params": { "query_vector": [<QUERY_VECTOR_DATA>] } } } } }' d

Alternative of ES in vector search

  1. Pinecone -
  2. Weaviate -

Ref : This article is earlier published by Sanjay here. For more such article follow me on LinkedIn.