Semantic Search In AWS OpenSearch: A HybridSearch Guide

by Alex Johnson 56 views

In today's data-driven world, efficient and accurate search capabilities are crucial. Semantic search takes search functionality to the next level by understanding the intent and context behind queries, rather than just matching keywords. This article delves into implementing semantic search for AWS OpenSearch resources using HybridSearch, focusing on creating a vector index and updating the ingest pipeline for optimal performance.

Understanding Semantic Search and HybridSearch

Semantic search represents a significant advancement over traditional keyword-based search methods. Instead of merely looking for literal matches, it leverages natural language processing (NLP) and machine learning (ML) techniques to interpret the meaning of search queries. This allows users to find information even when they don't know the exact keywords or phrases used in the documents.

HybridSearch is an approach that combines the strengths of both keyword-based search and semantic search. By integrating these two methods, users can achieve more comprehensive and relevant search results. This involves creating vector embeddings of text data, which capture the semantic meaning of words and phrases, and then using these embeddings to find documents that are semantically similar to the search query.

Implementing semantic search involves several key steps:

  1. Creating a Vector Index: This involves configuring OpenSearch to store vector embeddings, which are numerical representations of text data.
  2. Updating the Ingest Pipeline: This ensures that text data is transformed into vector embeddings during the data ingestion process.
  3. Querying the Index: This involves using the vector embeddings to find documents that are semantically similar to the search query.

Step 1: Index Configuration for Semantic Search in OpenSearch

Configuring the index correctly is paramount for efficient semantic search. This involves creating a vector index in OpenSearch that is optimized for storing and searching vector embeddings. The vector index will contain the numerical representations of your text data, allowing OpenSearch to perform semantic similarity searches efficiently.

Creating the Index with Vector Embedding Field

The first step is to create an index with a field of type knn_vector. This field will store the vector embeddings. The dimension of the vector field should match the output dimension of the text embedding model you are using. For example, if you are using a model that produces 768-dimensional embeddings, the dimension of the knn_vector field should be set to 768.

Here’s an example of how to create an index with a knn_vector field:

PUT /my-nlp-index
{
  "settings": {
    "index.knn": true,
    "default_pipeline": "nlp-ingest-pipeline"
  },
  "mappings": {
    "properties": {
      "id": { "type": "text" },
      "passage_embedding": {
        "type": "knn_vector",
        "dimension": 768,
        "space_type": "l2"
      },
      "passage_text": { "type": "text" }
    }
  }
}

In this example:

  • index.knn: true enables k-NN (k-Nearest Neighbors) search, which is essential for vector search.
  • default_pipeline: "nlp-ingest-pipeline" specifies the ingest pipeline to use for processing documents before indexing.
  • passage_embedding: is the knn_vector field that will store the vector embeddings.
  • dimension: is set to 768, matching the output dimension of the text embedding model.
  • space_type: "l2" specifies the distance metric to use for k-NN search (L2 distance, also known as Euclidean distance).

Optimizing Settings for Vector Search

In addition to creating the knn_vector field, you should also optimize the index settings for vector search. This includes setting the index.knn parameter to true and configuring other settings such as the number of shards and replicas to match your data volume and performance requirements.

  • index.knn: true Enables k-NN search, which is a prerequisite for vector search. This setting tells OpenSearch to use the k-NN engine for searching the vector fields.
  • Number of Shards and Replicas The number of shards determines how the index is split across multiple nodes, while the number of replicas determines how many copies of each shard are created. Proper configuration of these settings is crucial for scalability and fault tolerance. For vector search, it’s often beneficial to increase the number of shards to improve query parallelism.

Importance of Field Mappings

Correct field mappings are essential for ensuring that data is indexed and searched correctly. In the context of semantic search, you need to map the text fields to be embedded and the knn_vector field to store the embeddings. The field mappings define the data type and properties of each field in the index. For example, the passage_embedding field is mapped as a knn_vector with a specific dimension and space type.

Step 2: Updating the Ingest Pipeline for Semantic Search

An ingest pipeline in OpenSearch is a series of processors that transform documents before they are indexed. For semantic search, the ingest pipeline plays a crucial role in converting text data into vector embeddings. This involves using the text_embedding processor to generate embeddings from specified text fields and store them in the knn_vector field.

Developing an Ingest Pipeline with the text_embedding Processor

The text_embedding processor is a key component for implementing semantic search. It allows you to transform text fields into vector embeddings during data ingestion. This processor integrates with a text embedding model, which is responsible for generating the embeddings.

Here’s an example of how to create an ingest pipeline using the text_embedding processor:

PUT /_ingest/pipeline/nlp-ingest-pipeline
{
  "description": "Ingest pipeline for generating text embeddings",
  "processors": [
    {
      "text_embedding": {
        "model_id": "your-model-id",
        "field_map": {
          "passage_text": "passage_embedding"
        }
      }
    }
  ]
}

In this example:

  • model_id: Specifies the ID of the deployed text embedding model. You need to deploy a model in OpenSearch before you can use it in the ingest pipeline.
  • field_map: Defines the mapping between input fields and output fields. In this case, the passage_text field is mapped to the passage_embedding field. This means that the text from the passage_text field will be used to generate the embedding, and the resulting embedding will be stored in the passage_embedding field.

Defining the field_map for Input and Output Fields

The field_map is a crucial part of the text_embedding processor configuration. It specifies which input fields should be used to generate embeddings and which output fields should store the resulting embeddings. This mapping ensures that the text data is correctly transformed into vector embeddings and stored in the appropriate fields.

For example, if you have a document with fields title, description, and content, you can configure the field_map to generate embeddings for each of these fields:

"field_map": {
  "title": "title_embedding",
  "description": "description_embedding",
  "content": "content_embedding"
}

In this case, the embeddings for the title field will be stored in the title_embedding field, the embeddings for the description field will be stored in the description_embedding field, and the embeddings for the content field will be stored in the content_embedding field.

Processing and Storing Documents in the k-NN Index

Once the ingest pipeline is configured, you can use it to process and store documents in the k-NN index. When a document is ingested, the pipeline will automatically generate vector embeddings for the specified text fields and store them in the knn_vector fields. This allows you to perform semantic search queries on the indexed data.

To use the ingest pipeline, you can specify the pipeline parameter when indexing a document:

POST /my-nlp-index/_doc?pipeline=nlp-ingest-pipeline
{
  "id": "1",
  "passage_text": "This is a sample passage for semantic search."
}

In this example, the document will be processed by the nlp-ingest-pipeline before being indexed. The pipeline will generate a vector embedding for the passage_text field and store it in the passage_embedding field.

Handling Long Texts with Text Chunking

When dealing with long texts, it’s often beneficial to use text chunking. Text chunking involves breaking down long texts into smaller chunks, generating embeddings for each chunk, and then storing these embeddings in the index. This can improve the accuracy and performance of semantic search, especially when dealing with very long documents.

OpenSearch provides several techniques for text chunking, such as using a character-based chunker or a sentence-based chunker. The choice of chunking technique depends on the specific requirements of your application.

Acceptance Criteria for Semantic Search Implementation

To ensure that the semantic search implementation meets the required standards, several acceptance criteria should be considered. These criteria cover index configuration, validation, and performance.

User Acceptance Criteria

  • Index Configuration:

    • Given the volvo-os-index, when the index template is applied, then it includes:
      • A vector field with dimensions matching the hosted model (e.g., 768).
      • A normalized_text field for processed text.
      • Settings optimized for vector search (e.g., "index.knn": true).

  • Validation & Performance:

    • Given a sample query (e.g., "eco-friendly trucks"), when semantic search is executed, then:
      • Results include documents with relevant embeddings (verified via explain API).
      • Query latency is ≤150ms in the local environment.
      • Normalization errors (e.g., unprocessed accents) are logged and handled.

Technical Details: Implementing Vector Embedding Fields and Updating Ingest Pipeline

Implementing Vector Embedding Fields in Mapping Properties

Implement fields for title, description, and text for being Vector Embeddings in mapping properties and settings. Ensure compatibility with the embedding pipeline and model dimensions.

Updating Ingest Pipeline for Semantic Search

  • Develop an ingest pipeline utilizing the text_embedding processor to transform specified text fields into vector embeddings during data ingestion.
  • Define the field_map in the processor to specify input fields for generating embeddings and output fields for storing them.
  • Use the ingest pipeline to process and store documents in the k-NN index, generating vector embeddings for specified text fields during ingestion.

Step 3: Querying the Index for Semantic Search Results

Once the index is configured and the data is ingested, the next step is to perform semantic search queries. This involves converting the search query into a vector embedding and then searching the index for documents with similar embeddings. OpenSearch provides several query types for vector search, such as the knn query and the script_score query.

Converting Search Queries into Vector Embeddings

To perform semantic search, the search query needs to be converted into a vector embedding. This can be done using the same text embedding model that was used to generate the embeddings for the documents in the index. The query embedding represents the semantic meaning of the search query, allowing OpenSearch to find documents that are semantically similar.

Here’s an example of how to generate a query embedding using the text_embedding processor:

POST /_ingest/pipeline/_simulate
{
  "pipeline": {
    "processors": [
      {
        "text_embedding": {
          "model_id": "your-model-id",
          "field_map": {
            "query_text": "query_embedding"
          }
        }
      }
    ]
  },
  "docs": [
    {
      "_source": {
        "query_text": "eco-friendly trucks"
      }
    }
  ]
}

In this example, the _simulate endpoint is used to simulate the execution of the ingest pipeline. The pipeline generates a vector embedding for the query_text field, which contains the search query. The resulting embedding can then be used in a vector search query.

Using the knn Query for Vector Search

The knn query is a specialized query type for performing k-NN search on vector fields. It allows you to find the k-nearest neighbors to a given query vector. This query type is highly efficient for vector search and is the recommended approach for most semantic search applications.

Here’s an example of how to use the knn query:

GET /my-nlp-index/_search
{
  "query": {
    "knn": {
      "passage_embedding": {
        "vector": [0.1, 0.2, ..., 0.768],
        "k": 10
      }
    }
  }
}

In this example:

  • passage_embedding: is the knn_vector field to search.
  • vector: is the query vector (the embedding of the search query).
  • k: is the number of nearest neighbors to return.

Validating Results with the Explain API

To ensure that the semantic search is working correctly, it’s important to validate the results. The Explain API in OpenSearch can be used to understand how a query was executed and why certain documents were returned. This can help you identify any issues with the index configuration, ingest pipeline, or query formulation.

Here’s an example of how to use the Explain API:

GET /my-nlp-index/_explain/1
{
  "query": {
    "knn": {
      "passage_embedding": {
        "vector": [0.1, 0.2, ..., 0.768],
        "k": 10
      }
    }
  }
}

In this example, the Explain API is used to explain why document with ID 1 was returned for the given query. The response will include details about the scoring process, the distance between the query vector and the document vector, and other relevant information.

Monitoring and Handling Normalization Errors

Normalization errors, such as unprocessed accents or special characters, can affect the accuracy of semantic search. It’s important to monitor for these errors and handle them appropriately. This can involve implementing preprocessing steps in the ingest pipeline to normalize the text data before generating embeddings.

Logging and monitoring tools can be used to track normalization errors. When an error is detected, it should be logged and investigated. Common techniques for handling normalization errors include removing accents, converting text to lowercase, and removing special characters.

Conclusion

Implementing semantic search with HybridSearch in AWS OpenSearch requires careful planning and configuration. By creating a vector index, updating the ingest pipeline, and using appropriate query types, you can build a powerful search solution that understands the meaning behind user queries. Remember to validate your implementation using the Explain API and monitor for normalization errors to ensure optimal performance. This approach greatly enhances the ability to retrieve context-aware results, improving the overall search experience.

For further reading on OpenSearch and semantic search, consider exploring the official OpenSearch documentation on Vector Search. This resource provides comprehensive information on vector search concepts, implementation details, and best practices.