What are vector embeddings? A complete guide [2025]

How to choose the best model for semantic search

What is a vector database? What you need to know [2025]

Hybrid search combines keyword and semantic search, giving users the best of both worlds and control over the level of contextual depth they need. 

This type of information retrieval (IR) is especially critical in enterprise search, e-commerce, and knowledge management systems, where some inputs require deep contextual understanding while others demand precise keyword matching.

A clear advantage of hybrid search is its ability to handle data with less computational costs. 

This is thanks to the lexical matching system, which uses considerably less power than semantic algorithms that may rely on Large Language Models (LLMs), Convolutional Neural Networks (CNNs), and other energy-consuming models. 

Therefore, these systems can be tweaked for higher performance while being a cost-effective alternative to pure semantic search systems.

However, implementing hybrid search requires strategic planning. Users unfamiliar with semantic weighting may find it confusing, leading to frustration or disengagement. 

In the following piece, we will take a deep look into the importance of hybrid search, how to implement it, and in which situations it is the preferred solution.

## What is hybrid search?

Hybrid search systems combine keyword-based retrieval (sparse vector methodology) with semantic search systems ([dense vector embeddings](https://meilisearch.org.cn/blog/what-are-vector-embeddings)) to optimize precision and contextual relevance.

For a visual explanation of the terms and technologies used in the hybrid search, let’s take a look at the schema below:

![What is hybrid search.png](https://unable-actionable-car.media.strapiapp.com/What_is_hybrid_search_111362249c.png)

Semantic search relies on dense vectors, requiring both the search query and target data to be embedded using Machine Learning (ML) models. Some methods, like neural search, leverage Deep Neural Networks (DNNs) to generate rich contextual insights for embedding, retrieval, and ranking. Vector search is another type of semantic search that uses embedding models to create dense vectors, ML algorithms such as Approximate Nearest Neighbors (ANN) for information retrieval, and cosine similarity search for ranking.

On the other hand, keyword search relies on sparse vectors generated through algorithms. For instance, our full-text search relies on a vector of documents associated with each word in the dataset. When a search query is entered, the system preprocesses the input by extracting individual words and matching them against the sparse vector values of the documents. These are then ranked based on keyword relevance using [recursive bucket scoring.](https://meilisearch.org.cn/blog/from-ranking-to-scoring)

![Prepocessing.png](https://unable-actionable-car.media.strapiapp.com/Prepocessing_80a5f32b48.png)

### Dense vectors

The dense vectors are extensive and can have hundreds or thousands of floats (numerical representations) to identify a single document. They represent the similarity of objects in a vector space and can have the following shape:

```json
dense_vector = [0.8, 0.4, 0.2, 0.7, 0.9, 0.1, … ]
```

Dense vectors are usually multidimensional and don’t have zero values because they are created continuously to capture the complete information of the document or query.

### Sparse vectors

In full-text search, sparse vectors come in the form of documents associated with each word in a dataset. This vector representation forms the foundation for ranking and retrieving relevant and accurate results. 

Hybrid search combines the strengths of dense and sparse vector retrieval to enhance search scores. It first gathers matches from both methods and then refines the final output by globally ordering the results based on relevance. 

In the next chapter, we’ll explore each step of the hybrid search process.

## How does hybrid search work?

Hybrid search works by leveraging the semantic capabilities of dense vectors and the exact matching and accuracy of sparse vectors. The outputs retrieved from both methods are then blended to deliver more relevant search outcomes. 

![How does hybrid search work.png](https://unable-actionable-car.media.strapiapp.com/How_does_hybrid_search_work_3224ecf2f4.png)

By looking at the schematic above,  we can structure the hybrid search workflow into distinct steps, allowing both semantic and keyword searches to operate in parallel:

### Data cleansing & preprocessing

- **Keyword Search**: Requires robust data cleaning (e.g., Meilisearch includes native support for stop words, though manual configuration is required for full-text seatch) to ensure accurate term matching.
- **Semantic Search**: Benefits from noise reduction and strategic text segmentation (chunking) to improve the quality of document embeddings.

### Embedding: dense and sparse representations

- **Semantic**: Models like Bidirectional Encoder Representations from Transformers (BERT) and Global Vectors (GloVe) transform documents into dense vectors, capturing nuanced contextual meanings.
- **Keywords**: Some algorithms use frequency-based scoring, while others use neural networks to generate sparse embeddings. 

### Retrieval mechanisms of dense and sparse vectors

- **Semantic Retrieval**: Utilizes algorithms like Approximate Nearest Neighbor (ANN) and K-Nearest Neighbor (KNN) to search within the dense vector space efficiently.
- **Keyword Retrieval**: Directly matches query terms with document vectors.

### Ensemble retrieval

- **Hybrid search**: The final step involves combining results from both retrieval methods to determine the most relevant ones. 

The hybrid search process can be tweaked to assign importance to one type of result over another. If contextual meaning outweighs lexical matching, the system prioritizes outputs from the semantic search. Otherwise, it prioritizes keyword matching. This feature is available and easily controlled in Meilisearch’s [hybrid search setup](https://meilisearch.org.cn/solutions/hybrid-search).

## What is a hybrid search engine example?

Companies have adopted hybrid search engines to improve the accuracy and relevance of search results. One of the most advanced hybrid search systems is Google Search, which combines multiple search techniques and algorithms to deliver precise and contextually relevant results.

Google integrates both keyword-based search and machine learning models to interpret user queries, rank web pages, and present the most relevant information. Currently, they utilize [Vertex AI Embedding](https://cloud.google.com/vertex-ai/docs/start/install-sdk) models to generate dense vectors that capture semantic meaning while simultaneously creating sparse vectors for keyword-based retrieval.

To configure search results, Google merges output from semantic and keyword-based searches using Reciprocal Rank Fusion (RRF), as detailed in their official [notebook](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/embeddings/hybrid-search.ipynb).

As of January 2025, Google Search maintains an [89.79% market share](https://gs.statcounter.com/search-engine-market-share), continuing to dominate the search landscape. While AI-driven chatbot search features have begun gaining traction, they still fall short of Google's accuracy and real-time information retrieval capabilities.

## What are the benefits of hybrid search?

Hybrid search provides several advantages over standalone keyword-based or semantic search methods. Some of these benefits include:

1. **Enhanced search accuracy and relevance**: Hybrid search provides high-quality results by combining exact matches with semantic understanding. This level of accuracy ultimately retains users and reduces the bounce rate.
2. **Improved user experience**: The system can deliver meaningful content even if users enter inaccurate terms or vague keywords. This ease of retrieving information allows designers to create engaging search elements. Just ask [CarbonGraph](https://meilisearch.org.cn/customers?usecase=Hybrid+search): "_We migrated to Meilisearch from Pinecone to consolidate our search service [...] The setup of the OpenAI embedder was very straightforward, and we love that embeddings are created automatically using the contents of a search document_."
3. **Cost-effective implementation**: Lexical matching in hybrid search reduces memory usage compared to pure semantic search engines. This is crucial for lowering cloud costs related to storage and computational demand, especially since keyword search algorithms do not depend on GPUs.
4. **Increased search speed**: Opinly, a company that allows you to monitor your competitors' websites, adopted hybrid search technologies to enhance the quality and contextual accuracy of its search results.
5. **Personalization and adaptability**: Hybrid search systems can be configured to dynamically adjust the weight of keywords and semantic relevance or provide the user with control over it. The [NFSA collection](https://www.collection.nfsa.gov.au/) has this option available in their search engine, very similar to Meilisearch's hybrid search.

Hybrid search provides significant advantages across various business domains, offering speed, robustness, and efficiency. However, it is not always the optimal solution for every search application. The next chapter will explore its limitations and when alternative approaches may be more suitable.

## What are the drawbacks of hybrid search?

While hybrid search offers the best of both keyword-based and semantic search, it also comes with challenges that can impact implementation, performance, and user experience. Below are some key drawbacks to consider when adopting a hybrid search approach:

1. **Increased complexity in implementation**: Hybrid search requires integrating multiple search algorithms (e.g., semantic search using dense embeddings). This integration can be technically complex and requires deep technical understanding.
2. **Difficulty in balancing keyword precision and context**: Over-relying on one method may diminish the benefits of the other (ex., more semantic power than keyword precision). If a good balance is not achieved, this could lead to a bad user experience and increased bounce rates.
3. **Bad user experience**: If users can adjust semantic weight, the interface should be intuitive or designed for an audience familiar with the terminology. Otherwise, it may lead to confusion and increase the risk of user drop-off. According to this [Toptotal report](https://www.collection.nfsa.gov.au/), 88% of users are less likely to return after a bad user experience.

Despite these challenges, hybrid search remains a powerful tool when applied correctly.

## When should you use hybrid search?

Hybrid search isn't the optimal solution in all scenarios. In cases where data is highly structured — such as product inventories or specific academic research — precision is key, and similar-sounding terms with distinct meanings must be strictly differentiated. Hybrid search truly excels in the following examples:

1. **E-commerce platforms**: Online retailers like Amazon implement hybrid search to enhance product discovery. When customers input vague queries, the system leverages keyword matching and semantic analysis to present relevant products. You can see it in action below; the user searched for “bottle that keeps drinks cold” and received results about thermoses.
 
![image9.png](https://unable-actionable-car.media.strapiapp.com/image9_3b6346806d.png)

2. **Enterprise knowledge bases**: Organizations often maintain extensive repositories of documents, manuals, and communications. Hybrid search enables employees to retrieve pertinent information efficiently and increase productivity.
3. **Streaming services**: Platforms such as Netflix utilize hybrid search to help users find content, whether they search by specific titles or describe themes.
4. **Marketplaces**: Hybrid search in e-commerce can improve search accuracy, handle complex queries, and increase product discovery, leading to increased sales. 

Now let’s see how you can seamlessly introduce hybrid search systems in your project or workflow with [Meilisearch](https://cloud.meilisearch.com/login#navbar).

## How can you implement hybrid search?

Implementing a hybrid search requires a vector store solution. Several languages and AI frameworks can be used for implementation, but Python with Langchain is often a good stack to start building efficiently.

The AI-enhanced hybrid search from [Meilisearch](https://meilisearch.org.cn/blog/introducing-hybrid-search) allows third-party embedding models and control over the semantic weight of the outputs, allowing a deeper semantic understanding of the user inputs.

To start using Meilisearch's hybrid search features, you must create an account and gain access to the API keys and [cloud platform](https://meilisearch.org.cn/cloud). You can register for free and enjoy a 14-day trial.

![image6.png](https://unable-actionable-car.media.strapiapp.com/image6_9891a63d13.png)

After registering, you can create a new project and use the vector store to add and index documents, run queries, monitor analytics, and more.

In the settings tab, you'll find an option called Embedders, where you can enhance your hybrid search capabilities by integrating any embedding model of your choice. Below is an example of the OpenAI embedding model added to the list of embedders.

![image8.png](https://unable-actionable-car.media.strapiapp.com/image8_78aeaabd0b.png)

After adding a model, you can jump to the search preview tab and control the semantic weight directly there — you’re using hybrid search!

![image1.png](https://unable-actionable-car.media.strapiapp.com/image1_a6dc1af35b.png)

To integrate the search engine into your workflow, use Meilisearch’s API, available on the main page of the cloud dashboard. Here’s a Python script to query and retrieve results:

```json
import meilisearch


client = meilisearch.Client(
    '<meilisearch_server_url>',
    '<master_token>')
query = "Give me a book about a post-apocalyptic world"
results = client.index('books').search(query, opt_params={
  'hybrid': {
    'semanticRatio': 0.7,
    'embedder': 'openai'
  },
  'limit':4
})


for result in results['hits']:
    print(result['metadata']['text'])
```
To be able to run the code without issues, you first need to have the Meilisearch package installed:

```json
pip install meilisearch
```
Next, you need an index — a collection of documents you've added to Meilisearch Cloud ( 'books' in this example). Additionally, you’ll need an embedding model.

The results of the Python script are the following:

```json
{"id": 15, "title": "The Road", "description": "A father and his young son journey through post-apocalyptic America, fighting for survival while holding onto their humanity.", "genre": "Post-Apocalyptic"}
{"id": 6, "title": "1984", "description": "A dystopian social science fiction novel that follows Winston Smith and his rebellion against the totalitarian government that controls their society.", "genre": "Dystopian Fiction"}
{"id": 18, "title": "The Handmaid's Tale", "description": "In a dystopian future, a woman is forced to live as a concubine under a fundamentalist theocratic dictatorship.", "genre": "Dystopian Fiction"}
{"id": 19, "title": "Snow Crash", "description": "A pizza delivery driver and hacker investigates a dangerous computer virus that can affect human minds in both virtual and real worlds.", "genre": "Cyberpunk"}

```
Start building today by effortlessly uploading your documents to [Meilisearch Cloud](https://meilisearch.org.cn/cloud). Seamlessly integrate the hybrid search capabilities into your infrastructure using [Python](https://meilisearch.org.cn/docs/reference/api/search#example-22) and other supported languages, ensuring scalability and fast results.

## How does hybrid search compare to other search types?

Hybrid search combines two key approaches: semantic search and keyword search. However, semantic search is a broader term for methods that retrieve contextual or semantic meaning, including vector search and neural search. Let's explore the differences between all these search types:

| **Hybrid search** | **Vector search** | **Semantic search** | **Keyword search** | **Neural search** |
| --- | --- | --- | --- | --- |
| Combines dense and sparse vector representations to enhance search accuracy and contextual relevance. | Uses dense vector embeddings and algorithms like ANN to retrieve semantically relevant results. | A broader term for search technologies that use dense vectors to obtain contextual or semantic outputs. | Uses technologies that create sparse vectors for accurate lexical matching. | This technology uses Deep Neural Networks (DNNs) for making dense vectors and supports different data types. |

Now, let's examine how hybrid search differs from each of the other search methods individually.

### What is the difference between hybrid search and vector search?

Hybrid search enhances vector search by incorporating keyword matching for improved accuracy. As a form of semantic search, vector search relies on an embedder to generate dense vectors and retrieval algorithms like ANN and KNN to identify relevant results. By merging these with sparse vector outputs, hybrid search optimizes retrieval using techniques such as RFF.

### What is the difference between semantic search and hybrid search?

Hybrid search is a combination of semantic and keyword searches. The quality of the hybrid search system response highly depends on the embedder used for the semantic search. The better the embedder and the retrieval algorithm applied on the dense vectors, the better the semantical or contextual response of the hybrid search. In addition, the hybrid search uses keyword matching for lexical accuracy.

### What is the difference between keyword search and hybrid search?

Hybrid search utilizes keyword search to deliver precise lexical results. Keyword search relies on algorithms to generate sparse vectors from queries and documents, enabling fast and accurate retrieval. However, it lacks semantic understanding, so hybrid search integrates semantic search technologies to enhance search relevance and context.

### What is the difference between hybrid search and neural search?

Neural search, a type of semantic search, can be integrated into a hybrid search system. It leverages deep neural networks (DNNs) to deliver highly contextual results and supports various data input types. In a hybrid setup, neural search enhances typo tolerance while maintaining accuracy through lexical matching when users enter precise terms.

## Hybrid search gives you the best of both worlds

Hybrid search delivers exceptional accuracy and contextual relevance, but implementing it can be complex. It involves selecting the right vector database, choosing optimal embedding models, and fine-tuning outputs from both dense and sparse vectors.

[Meilisearch](https://meilisearch.org.cn/cloud) simplifies this process by providing an intuitive platform backed by insightful tutorials. On this platform, you can effortlessly upload datasets, experiment with embeddings, fine-tune semantic relevance, and access advanced metrics like analytics and monetization.





Hybrid Search 101: how it works and why It's important

Neural search is redefining the future of information retrieval, pushing beyond traditional keyword and vector search approaches. While vector search applies machine learning (ML) to map semantic relationships between user queries and data embeddings, neural search leverages deep neural networks (DNNs) to optimize the entire search pipeline, from indexing and semantic analysis to ranking mechanisms.

This advancement enables neural search systems to continuously learn and refine results, outperforming conventional search methods in accuracy and adaptability. More importantly, they support multimodal data retrieval, allowing seamless search across text, images, audio, and even complex 3D datasets, critical for AI-driven applications and next-gen user experiences.

However, adoption comes with challenges. Neural search models often function as ‘black boxes,’ raising concerns about interpretability, and their high computational requirements can drive up infrastructure costs.

This guide explains the fundamentals of neural search, how it differs from existing search systems, and its strategic advantages. It also explores practical applications and potential trade-offs, helping you assess whether neural search aligns with your company’s AI roadmap.

## What is neural search?

Neural search, or neural information retrieval, leverages DNNs to interpret the contextual meaning behind search queries, enabling more accurate results than traditional keyword-matching systems and vector search. Instead of relying on exact terms, it transforms the user’s input and data into dense vector embeddings—numerical representations that capture semantic relationships. This process is similar to vector search systems, but instead of using ML models like approximate nearest neighbor (ANN) and a ranking algorithm, the neural search relies entirely on DNNs for the complete process.

A practical example involves querying a film database. A search like ‘Recommend a drama exploring modern parenting challenges’ would return a ranked list of movies aligned with the query’s intent, prioritizing contextual relevance over isolated keywords. The ranking is refined by the DNN itself, which needs to understand concepts like family dynamics and contemporary parenting struggles.

Here’s an example from Meilisearch: 

![image1.png](https://unable-actionable-car.media.strapiapp.com/image1_4431fc4739.png)

You can also try this for yourself by [checking out our demo](https://wheretowatch.meilisearch.com/). 

A commonly misunderstood term is often confused with neural search. In the next chapter, we’ll look into the key distinctions between neural search and neural architecture search (NAS).

## How does neural search differ from neural architecture search?

Neural search and NAS are often confused but serve different purposes. Neural search enhances information retrieval by understanding semantic meaning, while NAS focuses on automating the design of neural network architecture to optimize model performance for specific tasks.

NAS leverages techniques like recurrent neural networks and reinforcement learning (RL) to generate new language models, visual models, and overall neural architecture, eliminating manual experimentation and accelerating model development. In contrast, neural search applies deep learning to improve search relevance across text, images, sound, and other structured data types, transforming how systems retrieve and rank results.

Despite their differences, these technologies can intersect. For instance, you could use NAS to create a highly specialized artificial neural network tailored to your neural search. Let’s get a deeper understanding of how the latter works in the next chapter.

## How does neural search work?

Neural search enhances the search experience with DNNs using two main mechanisms: indexing and embedding. These are integrated in the following steps:

- **Data encoding and indexing**: The DNN analyzes documents (e.g., movie descriptions) to generate vector embeddings. These dense numerical representations capture semantic contexts, such as themes, relationships, and emotions, and are properly indexed in a vector database.
- **Query Encoding**: The user’s input (e.g., ‘Recommend a drama about modern parenting’) is converted into an embedding using the same DNN. This ensures the query and data share a unified semantic framework.
- **Semantic matching & ranking**: Unlike vector search systems that rely on external algorithms (ANN, cosine similarity) the DNN directly compares query and document embeddings. It evaluates learned relationships (e.g., linking ‘parenting challenges’ to films about complex family dynamics) to rank results by contextual alignment.

Neural searches can process various data inputs, depending on their types and activation functions, including text, images, audio, and 3D information. The embedding model adapts based on the input type. For instance, a convolutional neural network (CNN) is used for images, while large language models like BERT, are employed for text.

## What are some examples of neural search use?

Neural search can be applied to a wide range of use cases that require the processing of different types of data inputs and constant learning and optimization. Here are two simple examples of its potential applications:

1. **Optimize ranking results**: Jina AI offers a superior ranking solution based on neural search. It takes the initial outputs from a search query and reevaluates them to ensure they align more closely with the user's intent. [Jina’s AI Reranker](https://jina.ai/reranker) natural language processing model is available as open-source and can be accessed through Jina’s API.
2. **Search tools with images**: The German company [PROFISHOP](https://www.profishop.de/) uses a search engine powered by CNNs, allowing users to submit an image as input and fetch related content from a large industrial, durable, and consumer goods database.
3. **Retrieve ML models**: [Hugging Face](https://hugging-face.cn/chat/) has a large repository of models, including image-to-image, image-to-text, text-to-text, and more. Finding the appropriate model was initially challenging. However, the platform has since implemented a [hybrid search system](https://meilisearch.org.cn/blog/hugging-face-democratizes-ai) that combines keyword matching with contextual understanding to enhance model discovery.
48. **Improve e-commerce**: [Google Shopping](https://shopping.google.com/) has integrated artificial intelligence (AI) to enhance its search capabilities, providing users with more relevant and personalized product recommendations. In addition, Google uses generative AI to summarize key factors during the search.

The neural search's ability to integrate RL for constant learning, accept different data types, and allow GenAI integration makes this technology extremely versatile and applicable to countless applications while ensuring accurate, personalized, and evolving results.

## What are the benefits of neural search?

Neural search systems offer several key benefits. Let’s take a look at some of them:

- **Increased engagement & reduced bounce rates**: Users expect almost real-time, precise, and relevant search results. When they don’t find what they need, [bounce rates surge](https://www.allaboutai.com/ai-seo/average-bounce-rate-guide/), and potential customers may never return. An optimal bounce rate is usually below 50%.
- **Enhanced productivity & efficiency**: Instead of manually searching through vast documentation or using inefficient keyword searches, neural search enables employees to find exact information instantly.
- **Multimodal search capabilities**: Neural search processes various data formats, including text, images, and audio. This versatility allows new use cases, particularly in e-commerce. According to [Think With Google](https://www.thinkwithgoogle.com/marketing-strategies/search/online-shopping-image-statistics/), 50% of online shoppers say images directly influence purchasing decisions.
- **Revenue growth**: Search is an important yet undervalued feature in e-commerce. [Bloomreach](https://www.bloomreach.com/) reports that while only 15% of B2C customers use site search, this small group drives 45% of online revenue.
- **Personalization & continuous learning**: Neural search systems can adapt by learning from user interactions and refining results based on behavior, clicks, and historical preferences. This dynamic personalization enhances user experience, improves retention, and drives long-term customer loyalty.

While the applications and benefits seem tempting, this is still a new field, so there is plenty of room for optimization. In the next section, we’ll explore some of this technology's challenges and limitations.

## What are the drawbacks of neural search?

The key drawbacks of neural search are listed below:

- **High computational cost and complexity**: DNNs require substantial computational resources for training and operation. This led to the development of GPUs with enhanced parallel processing power, which can be expensive. For instance, the newest model from NVIDIA (which has a [90% share](https://www.ft.com/content/03f66c13-3531-4ff2-b348-f17dac588c93) of the GPU market) is currently priced at [$2,529](https://www.tomshardware.com/news/lowest-gpu-prices).
- **Data dependency**: Neural search systems use high-quality, labeled training data to function properly. Without it, the system risks producing biased or skewed results. Designing the required metrics for the system before data ingestion is key to delivering better search functionality.
- **Lack of interpretability**: DNNs often function as 'black boxes,' learning complex patterns that can be difficult to understand. Due to this lack of transparency, debugging unexpected or odd results is challenging. This is why logs and [monitoring tools](https://meilisearch.org.cn/blog/monitoring-metrics-release) are important once the search system is deployed. 
- **Model maintenance and engineering**: To stay effective, DNNs require frequent retraining to adapt to language and user behavior changes, increasing operational overhead. Additionally, the infrastructure and expertise required to maintain neural systems can be too expensive for smaller organizations. AI engineers are among the most in-demand jobs according to [LinkedIn’s 2025 report](https://www.linkedin.com/pulse/linkedin-jobs-rise-2025-25-fastest-growing-us-linkedin-news-gryie/) with an average annual salary of [$120,000 globally](https://artsmart.ai/blog/how-much-do-ai-engineers-make/), which is 30% higher than other engineering roles.

While technological advances are gradually reducing the computational demands of these models, these challenges must still be carefully considered when deciding whether to implement a neural search system.

## How does neural search compare to other types of search?

Neural search is a form of semantic search, similar to vector search, but it uses DNNs to handle indexing, encoding, and ranking. Other search types include keyword search and hybrid search. Here’s how they differ:

| **Neural search** | **Vector search** | **Keyword search** | **Hybrid search** |
| --- | --- | --- | --- |
| Leverages DNNs to embed and get the most relevant results based on semantic relationships. | Uses ML algorithms such as ANN to find semantic relationships. | Outputs content based on the keywords used in the query and doesn’t understand the context. | Combines both keyword search and neural search. |

### Neural search vs. vector search

Both [vector search](https://meilisearch.org.cn/solutions/vector-search) and neural search retrieve data by matching query vectors to embeddings, making them forms of semantic search. Although neural search might be interpreted as a type of vector search, the latter typically refers to a method that leverages ML models like ANN for contextual retrieval and uses cosine similarity for ranking. In contrast, neural search bypasses these steps, relying entirely on DNNs throughout the search process.

### Neural search vs. keyword search

Keyword search relies solely on matching literal terms in search queries to those in a dataset, ignoring context or meaning. For example, a keyword search for 'best smartphone' won’t return results mentioning 'top mobile devices' unless those exact words are present, whereas neural search recognizes their semantic equivalence.

### Neural search vs. hybrid search

[Hybrid search](https://meilisearch.org.cn/solutions/hybrid-search) combines the contextual intelligence of DNNs with the speed and simplicity of keyword matching, optimizing for precision and efficiency. It uses keyword search to rapidly filter exact term matches while leveraging neural techniques for semantic understanding, ensuring relevance even when queries or data require deeper interpretation. [Meilisearch](https://meilisearch.org.cn/) gives you control over your hybrid search engine, enabling you to adjust the semantic context as needed.

## What is the future of neural search?

The fast evolution of AI, specifically in DNNs and NAS, will bring tremendous advances to neural search and its use-case scenarios. NAS, which automates the design of DNN architecture, enables the creation of highly specialized models that optimize accuracy, speed, and resource efficiency for specific tasks. These new and more powerful models have transformative applications in different industries:

- **Reshaping e-commerce**: Neural search engines can retrieve the most relevant products based on the context provided by the customer’s query. Already, giants like Google are using this technology to enhance searches — [Google Shopping](https://www.voguebusiness.com/story/technology/google-shakes-up-shopping-with-generative-ai) now includes an AI-generated brief containing what it thinks are the top three choices and why. The AI brief also includes tips on what to look for when shopping for a particular product, which is sourced from the web and summarized by AI. 
- **Education & skill development**: Learning platforms can use neural search to deliver materials tailored to an individual’s progress, learning style, and career goals. Imagine an AI tutor that dynamically assembles courses from videos, papers, and exercises. [Three in five](https://www.aiprm.com/ai-in-education-statistics/) (60%) teachers claim to have integrated AI into their teaching habits.
- **Healthcare transformation**: Neural search engines can potentially analyze patient histories, symptoms, and genomic data to identify personalized treatment options or clinical trials. This could significantly reduce the time patients wait for an accurate diagnosis and treatment. [Deep6 AI](https://deep6.ai/) is an example of this, as it uses neural search techniques to sift through massive amounts of data to match patients to clinical trials. 

Another emerging technology is Generative AI, which can go hand-in-hand with neural searches and be applied in different formats (text, image, sound, video):

- **Design & creativity**: Architects, for example, could use search engines by providing sketches and verbal descriptions (e.g., 'sustainable office building with natural light'), prompting the system to generate 3D models that integrate principles from existing designs and environmental data.
- **Dynamic content creation**: Media platforms could leverage generative AI to auto-generate videos, articles, or music playlists by retrieving and delivering content aligned with the user’s preferences, location, or mood.

These technologies will enhance information retrieval and actively influence human decision-making, creativity, and problem-solving as they evolve.

## Unlock next-generation neural search with Meilisearch

Neural search systems are state-of-the-art in delivering contextual results, but they may not be the ideal choice for every situation. This is why a hybrid search system can be handy, especially when it offers control over the semantic ratio and ranking outcomes. 



Neural search: Definition, how it works, benefits and more

What is semantic search? How it works, use cases & more

10 of the best Algolia alternatives [With features, pricing & more]

Enterprise search: a comprehensive guide

When your AI application starts hemorrhaging $500 daily in OpenAI API calls and users are drumming their fingers waiting 2 seconds for responses, semantic caching isn't just a nice-to-have—it's a lifeline.

While traditional caches match queries word-for-word, semantic caching understands the deeper meaning.

In this guide, you'll discover how to implement semantic caching that slashes response times and API costs. Whether you're building a chatbot, search engine, or AI-powered application, these strategies will help you create lightning-fast, cost-effective semantic search that keeps users happy and your infrastructure lean. You'll learn how to:

- Reduce response times dramatically
- Achieve massive API cost savings
- Handle similar questions without repeatedly querying language models

## Introduction to semantic caching

Semantic caching revolutionizes how we store and retrieve information in modern applications. Traditional caching relies on exact matches, while semantic caching understands the meaning behind queries. This allows systems to recognize and respond to similar questions, even if they're worded differently.

This technique is valuable for AI-powered applications. Processing similar queries repeatedly can be both time-consuming and expensive.

### What is semantic caching

Semantic caching is a sophisticated data retrieval technique. It stores both the data and the contextual meaning of queries and their results. When a user asks a question, the system analyzes its semantic meaning rather than looking for an exact match.

For example, "What's the weather in New York?" and "Tell me New York's forecast" can pull from the same cached response, saving processing time and resources.

### Comparing semantic caching vs traditional caching

![Semantic Cache](https://unable-actionable-car.media.strapiapp.com/how_to_cache_semantic_search_1996414fc7.avif)

Traditional caching and semantic caching serve different purposes in modern applications. Here's a comparison:

- **Traditional caching**: Relies on exact matches and specific keys to retrieve data. Think of it as using a filing cabinet where you need the exact file name.
- **Semantic caching**: Understands context and meaning. It's like having a smart assistant who can find related information even when you ask in different ways.

### Advantages of semantic caching

Semantic caching offers several advantages for real-world applications:

- **Reduced API costs** by avoiding redundant queries.
- **Faster response times** for similar questions.
- **More consistent answers** across related queries.
- **Better scalability** for AI-powered applications.

### When to choose semantic caching

Consider semantic caching for your application if:

- **Users might ask the same question in different ways.**
- Response consistency is crucial.
- API costs need to be optimized.
- Real-time response is essential.

## Core concepts of semantic caching

Understanding the fundamental components and applications of semantic caching is crucial for successful implementation. Let's explore how these elements work together to create efficient caching systems that enhance your application's performance.

### Key components of semantic caching systems

Several critical components form the foundation of semantic caching.

- [**Embedding model**](https://meilisearch.org.cn/blog/what-are-vector-embeddings): Converts queries into vector representations capturing their meaning.
- [**Vector database**](https://meilisearch.org.cn/blog/what-is-a-vector-database): Stores these vectors, allowing for quick similarity searches for new queries.
- **Cache layer**: Sits between your application and underlying systems, managing query-response pairs and their semantic meanings. It decides whether to serve cached content or process new requests.
- **Vector search mechanism**: Evaluates incoming queries against stored embeddings using similarity thresholds to determine cache hits.

### Semantic cache in RAG

**Retrieval-Augmented Generation (RAG)** systems use semantic caching to improve performance and reduce costs. When a user submits a query, the system first checks the semantic cache for similar questions before going through the full retrieval and generation process. This is especially effective for handling common queries in customer support or knowledge base applications.

The integration process involves three main steps:

1. Embed the incoming query into a vector representation.
2. Search the cache for semantically similar queries.
3. If a match is found, return the cached response immediately. If not, proceed with the traditional RAG pipeline and store the new result for future use.

By understanding these core concepts and components, you can effectively implement and optimize semantic caching in your AI applications.

## Implementing semantic caching for an e-commerce site

Semantic caching can significantly enhance how e-commerce platforms handle product searches and customer queries. By adopting a smart caching mechanism, businesses can boost search performance and improve user experience.

![how to cache semantic search](https://unable-actionable-car.media.strapiapp.com/how_to_cache_semantic_search_ba174395a9.avif)

We will go through these key steps:

1. Configure embedding model by initializing OpenAI embeddings and Meilisearch client with appropriate connection parameters.
2. Create a vector store with custom configuration, specifying index name, embedding dimensions, and text key for efficient semantic search storage.
3. [Implement semantic search](https://meilisearch.org.cn/docs/guides/ai/openai) function to convert queries into vector embeddings and perform similarity-based retrieval from the vector store.
4. Develop cache management logic to handle cache hits and misses, including fallback mechanisms for database searches and optional cache updates.

### Setting up the semantic caching infrastructure

Building a semantic caching system requires careful planning and the right tools. Here's a step-by-step guide to implementation.

#### Choosing core technologies

Start by [selecting the technologies for your system](https://meilisearch.org.cn/blog/choosing-the-best-model-for-semantic-search). **Meilisearch is an excellent vector search engine** for semantic caching. For this example, we'll use Python with OpenAI embeddings to create a context-aware caching solution.

#### Installing required dependencies

Set up your project environment by installing the necessary libraries:

```bash
pip install meilisearch langchain openai python-dotenv
```

This command installs essential tools like Meilisearch for vector storage and LangChain for embedding management.

#### Configuring the embedding model

The core of semantic caching is converting queries into meaningful vector representations. Below is a sample configuration using OpenAI embeddings:

```python
from langchain.embeddings.openai import OpenAIEmbeddings
import meilisearch

# Set up OpenAI embeddings
embeddings = OpenAIEmbeddings()

# Initialize Meilisearch client
client = meilisearch.Client(
    url="your_meilisearch_url",
    api_key="your_api_key"
)
```

#### Creating a vector store for semantic caching

Setting up a vector store is crucial for implementing an effective semantic cache. Here's a comprehensive approach to creating a vector store using Meilisearch and OpenAI embeddings:

```python
# Create vector store with custom configuration
vector_store = Meilisearch(
    client=client,
    embedding=embeddings,
    index_name="semantic_cache",
    text_key="content"
)

# Optional: Define embedder configuration
embedder_config = {
    "custom": {
        "source": "userProvided",
        "dimensions": 1536  # OpenAI embedding dimensions
    }
}
```

This setup allows you to create a robust semantic cache that can efficiently store and retrieve contextually similar queries and their responses.

### Implementing the semantic cache mechanism

The key to semantic caching is creating a **retrieval system that understands query intent**.

#### Creating a semantic search function

Here’s an example of a semantic search function:

```python
def semantic_cache(query, k=3):
    # Convert query to vector embedding
    query_embedding = embeddings.embed_query(query)
    
    # Perform similarity search in Meilisearch
    results = vector_store.similarity_search(
        query=query,
        k=k
    )
    
    return results
```

This function:

- Converts the query into a vector representation.
- Searches for semantically similar results in the vector store.
- Returns the most relevant matches.

#### Handling cache hits and misses

A robust caching strategy must handle both cache hits and misses effectively:

```python
def process_product_search(query):
    # Check the semantic cache
    cached_results = semantic_cache(query)
    
    if cached_results:
        # Cache hit: return cached results
        return {
            "source": "cache",
            "results": cached_results
        }
    else:
        # Cache miss: perform full database search
        full_results = perform_database_search(query)
        
        # Optionally, store new results in cache
        update_semantic_cache(query, full_results)
        
        return {
            "source": "database",
            "results": full_results
        }
```

### Performance optimization techniques

To ensure efficient semantic caching, consider these strategies:

- **Set a similarity threshold** (e.g., 0.85–0.95) to filter results.
- Limit cache size to prevent memory issues.
- Use time-to-live (TTL) to refresh outdated cache entries.
- Regularly update your embedding model for better accuracy.

### Testing your semantic cache

Testing is critical to ensure reliability. Test scenarios should include:

- Exact match queries.
- Semantically similar queries.
- Completely new queries.
- Edge cases with minimal context.

**Thorough testing ensures your system performs well across diverse search patterns.**

Meilisearch’s fast vector search capabilities make semantic caching not only feasible but also highly efficient for e-commerce platforms.

## Adding semantic caching to RAG system

Retrieval-Augmented Generation (RAG) systems can greatly benefit from semantic caching. This integration **improves efficiency, reduces costs, and enhances responsiveness** in AI applications.

### Understanding RAG and semantic caching integration

Semantic caching in RAG systems focuses on storing and retrieving query responses based on their meaning, not just exact text matches. This allows AI applications to quickly access previously generated responses while maintaining contextual relevance.

Key steps in integrating semantic caching into a RAG workflow include:

1. **Embedding Transformation**: Convert user queries into vector representations that capture semantic meaning. Use embedding models like BERT or OpenAI's embedding APIs for this transformation.

2. **Cache Lookup Mechanism**: Use a vector database for fast, similarity-based searches. **Meilisearch is a strong choice** for its efficient semantic search capabilities, enabling quick matches between incoming queries and cached responses.

### Implementing the semantic caching layer

Implementing a semantic caching layer requires creating a specialized class that manages query embeddings, similarity checks, and cached response retrieval. This code provides a practical approach to efficiently storing and accessing semantically similar query responses.

```python
class RAGSemanticCache:
    def __init__(self, embedding_model, vector_store):
        self.embedding_model = embedding_model
        self.vector_store = vector_store
        self.similarity_threshold = 0.85

    def retrieve_cached_response(self, query):
        query_embedding = self.embedding_model.embed_query(query)
        similar_results = self.vector_store.similarity_search(
            query_embedding, 
            threshold=self.similarity_threshold
        )
        return similar_results[0] if similar_results else None
```

This class:

- Embeds queries into vectors.
- Searches for semantically similar results in the vector store.
- Returns a cached response if it meets the similarity threshold.

<Callout intent="note">
	
	To ensure your semantic caching layer performs efficiently, apply these strategies:
	
	- **Dynamic Similarity Thresholds**: Adjust thresholds based on your domain and use case.
	- **Cache Maintenance**: Use expiration and refresh mechanisms to keep responses relevant.
	- **Multi-Turn Context Tracking**: For conversational systems, include conversation history in cache retrieval to maintain context.
	
	
</Callout>

### Practical implementation example

Here’s how semantic caching can be integrated into a RAG workflow:

```python
def rag_with_semantic_cache(query, rag_system, semantic_cache):
    # Check semantic cache first
    cached_response = semantic_cache.retrieve_cached_response(query)
    
    if cached_response:
        return cached_response
    
    # If no cached response, use traditional RAG
    retrieved_docs = rag_system.retrieve_documents(query)
    generated_response = rag_system.generate_response(query, retrieved_docs)
    
    # Store the new response in the semantic cache
    semantic_cache.store_response(query, generated_response)
    
    return generated_response
```

This function:

- Checks the semantic cache for a response.
- Falls back to the RAG system if no cached response exists.
- Stores new responses in the cache for future use.

### Benefits for different applications

Semantic caching is especially useful in:

- Customer support chatbots.
- Enterprise knowledge management systems.
- Educational platforms.
- Research and analytics tools.

## Optimization strategies

Once your semantic caching system is running, focus on optimization to ensure it continues to deliver maximum value. Let's explore key strategies to maintain and improve your system's performance over time.

### Ensuring accuracy & consistency

Maintaining high accuracy in semantic caching requires regular monitoring and updates.

- **Implement version control** for cached responses to track changes and ensure users receive the most current information.
- **Invalidate or update related cache entries** when source data updates to prevent serving outdated information.
- **Cache consistency** becomes particularly important in distributed systems. Use a publish-subscribe system to propagate updates across your infrastructure, ensuring all cache nodes remain synchronized.
- **Regular validation of cached responses** helps maintain quality. Set up automated checks to verify cached responses still match their original context and meaning. Periodically compare cached responses against fresh generations to identify any drift in accuracy.

### Improving performance and efficiency

**Performance optimization** starts with careful monitoring of key metrics. Track cache hit rates, response times, and system resource usage to identify bottlenecks and optimization opportunities. Use dashboards to **visualize these metrics** and establish baseline performance expectations.

Consider these advanced optimization techniques:

- **Predictive caching** for frequently accessed queries.
- **Tiered caching** with different storage solutions for hot and cold data.
- **Optimize vector similarity calculations** for faster retrieval.
- **Compress embeddings** while maintaining accuracy.

Regular performance audits help identify improvement areas. Compare your system's performance against benchmarks and adjust your caching strategy accordingly. Document performance improvements and their impact on user experience and operational costs.

### Common pitfalls and how to avoid them

**Over-aggressive caching** can lead to memory issues and degraded performance. Start with conservative cache sizes and grow based on actual usage patterns rather than theoretical maximums.

**Poor similarity threshold selection** represents another challenge. Setting thresholds too low leads to false positives and incorrect responses. Setting them too high results in unnecessary cache misses. Monitor your threshold settings and adjust based on user feedback and performance metrics.

Watch out for these additional pitfalls:

- **Neglecting cache maintenance and cleanup**.
- **Failing to handle edge cases** in query processing.
- **Ignoring the impact of cache invalidation** on system performance.
- **Not implementing proper error handling** and fallback mechanisms.

**Focusing on these optimization strategies** ensures your semantic caching system remains efficient and effective, providing consistent performance improvements over time.

## Semantic caching: the path to high performance search

**Implementing semantic caching for your search system is a powerful way to boost performance, reduce costs, and enhance user experience.**

By following this guide's systematic approach to setting up semantic caching—from infrastructure assessment to optimization strategies—you'll be well-equipped to handle growing query volumes while maintaining fast response times.

<Callout intent="tip">
	
	No search engine expertise required! [Get started with Meilisearch Cloud](https://meilisearch.org.cn/get-started) and experience an intuitive platform that makes implementing powerful search effortless, even for developers new to search technologies.
	
	
</Callout>

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