TL;DR
- AWS OpenSearch can act as a vector database, enabling semantic search using vector embeddings instead of keyword-only matching.
- Data ingestion: Transforms raw content into embeddings through processing, chunking, and indexing, making it searchable at scale.
- Data retrieval: Uses vector similarity search to return the most relevant results, powering use cases like RAG and semantic search.
Introduction
Search is everywhere, from product discovery on websites to dashboards, log analytics, and internal tools. Behind many of these systems is AWS OpenSearch, a fully managed service designed to build scalable search and analytics solutions.
Modern search systems go far beyond simple keyword matching. Use cases such as semantic search, document similarity, recommendations, and Retrieval-Augmented Generation (RAG) rely on comparing meaning rather than exact text. This is where vector databases become essential.
AWS OpenSearch is widely used as a vector database. This enables similarity search over vector embeddings, allowing applications to retrieve results based on semantic relevance rather than exact text matches.
At a high level, this process consists of two main steps:
- Data ingestion, where data is indexed into OpenSearch
- Data retrieval, where indexed data is searched, retrieved, or analyzed
In this blog, we’ll walk through the theory behind:
- What a vector database is
- How OpenSearch stores vector data
- How data ingestion and data retrieval work in OpenSearch
What Is a Vector Database?
derstanding vector databases, it is important to understand embeddings. Embeddings are numerical representations of real-world objects that machine learning (ML) and artificial intelligence (AI) systems use to capture semantic meaning [1]. In the context of text, embeddings encode meaning, context, and relationships between words and sentences.
A vector database is a system designed to store, manage, and search these vector embeddings. Instead of depending exclusively on keyword matching, vector databases enable semantic search, where results are returned based on meaning rather than exact terms.
This makes vector databases especially useful for use cases such as:
- Document querying
- Question-answering systems
- Retrieval-Augmented Generation (RAG)
How Is Data Stored in OpenSearch
In contrast to traditional relational databases that use rows and columns, OpenSearch stores data as JSON documents inside an index.s
When using OpenSearch as a vector database, documents typically contain:
- The main text or content
- Metadata (IDs, source, timestamps, etc.)
- A vector embedding
Here’s a simple example:
{“id”: “doc_01”,“content”: “AWS OpenSearch can be used as a vector database”,“embedding”: [0.12, -0.30, 0.85, …], “source”: “blog”}
The embedding field enables OpenSearch to perform vector-based similarity search.
Now let’s explore how data ingestion works in OpenSearch.
What Is Data Ingestion in OpenSearch?
Data ingestion is the process of sending data from a source (such as a document, web page, or other data sources) into an OpenSearch index, so it can be searched and used later. An index is conceptually similar to a table in a relational database, as it stores a collection of related documents.
When OpenSearch is used as a vector database, ingestion generally involves two primary components:
- The primary content (text, documents, or descriptions)
- Vector embeddings
Ingestion flow typically includes:
- Data processing
- Chunking
- Generating embeddings using an embedding model
- Storing embeddings alongside the initial content
Let’s look at each of them.
1. Data Processing:
- The ingestion process begins by loading or extracting raw data from its source, such as documents, web pages, or internal knowledge bases.
- Since most of the documents are too large to be represented by a single embedding, they are split into smaller chunks.
2. Chunking:
- Chunking involves splitting large documents into smaller, semantically meaningful text segments.
- Context is preserved by keeping the sequence of sentences together and overlapping some content between chunks.
- Each chunk becomes an independent document that can be indexed and retrieved on its own.
- Once the data is chunked, each chunk is converted into a vector embedding using an embedding model.
3. Embedding Generation:
- Embeddings are generated outside OpenSearch.
- The same embedding model must be used for both ingestion and querying.
- Each embedding model has a fixed dimensionality that must stay uniform throughout the system.
- Each chunk of content has a corresponding vector representation.
- After embeddings are generated, the data is sent to OpenSearch for indexing.
4. Indexing and Mapping in OpenSearch
- The original content is stored as text fields.
- Embeddings are stored in vector fields.
- Metadata such as IDs, source, or timestamps is added.
- Mappings ensure fields are indexed correctly for vector search.
Once indexed, OpenSearch is ready to support search, analytics, and AI-powered workflows. We can now move to data retrieval.
What Is Data Retrieval in OpenSearch?
Data retrieval is the process of querying OpenSearch to find relevant documents based on a user’s search request. While ingestion prepares data for search, retrieval is where that data is actively used.
An index in OpenSearch acts as the source for retrieval, containing the documents that have already been processed. The way retrieval works depends on the approach of the search being performed. Some common retrieval approaches include:
- etrieval: In traditional search systems, retrieval is primarily based on Keyword matching, where documents are matched against query terms. This approach works well for exact or structured queries, but it struggles when the query wording differs from the document’s text, and the intent is semantic rather than literal.
- Semantic Retrieval: Vector-based retrieval solves these issues by focusing on semantic similarity rather than exact term matches.
Retrieval flow typically includes:
- Query processing
- Similarity search
- Top k results
- Optional Filtering
Let’s look at each step in detail.
1. Query Processing
- The retrieval process begins when a user submits a query, often in natural language.
- To enable semantic retrieval, the user’s query is converted into a vector embedding using the same embedding model that was used during ingestion.
- Once the query embedding is generated, OpenSearch performs a vector similarity search.
2. Vector Similarity Search
- The query vector is compared against stored embeddings.
- Similarity scores such as cosine similarity, dot product, or Euclidean distance are applied to calculate relevance scores.
- Documents are ranked based on semantic closeness to the query.
3. Top-K Result
- After similarity scores are calculated, OpenSearch returns the top-K most relevant documents.
- This limits results to the most relevant matches.
- Improves performance and response time.
4. Optional Filtering
Each returned result includes:
- The document content
- Metadata
- A relevance score based on similarity
In practice, vector retrieval is combined with metadata-based filtering. This step further refines results further based on the user’s needs.
Once retrieval is complete, the results can be used in multiple ways, including:
- Displaying results to users
- Feeding context into Retrieval-Augmented Generation (RAG) pipelines
At this stage, OpenSearch acts as the retrieval engine, supplying high-quality, relevant data to applications.
CONCLUSION:
AWS OpenSearch functions as a robust foundation for AI-driven applications. In this blog, we explored the theoretical foundations of using OpenSearch as a vector database, how data is stored, how ingestion converts raw content into searchable embeddings, and how retrieval leverages vector similarity to surface relevant results. Understanding this lifecycle is essential before designing and deploying real-world applications.
Pavitra Kadiyala is an AI and Data Engineer at Anblicks, specializing in AI with experience in Python, AWS, and Generative AI frameworks. She has hands-on experience building chatbots and GenAI products and holds a Master’s degree in Artificial Intelligence. Her work focuses on creating impactful, real-world AI solutions to address complex challenges.
