The Evolution of Technical Content Search

As organizations continue to embrace AI-driven solutions to manage technical content, the need for more sophisticated search capabilities has never been more pressing. Traditional keyword-based search engines, though useful in many contexts, often fall short when it comes to retrieving relevant, nuanced information from complex documentation. This is where semantic search comes into play, offering a revolutionary approach that aligns more closely with how users naturally ask questions and seek information.

In our previous blog post, we explored how Retrieval-Augmented Generation (RAG) enhances AI-generated responses by combining retrieval-based methods with generative models. In this blog we will delve into the mechanics and benefits of semantic search, which serves as a critical component in optimizing RAG's effectiveness. By understanding the difference between keyword search and semantic search, and how we've implemented this advanced technology at Zoomin, you'll gain insight into how semantic search can significantly improve the relevance and accuracy of AI-driven responses in technical content.

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What Is the Difference Between Keyword Search to Semantic Search?

The difference between keyword search and semantic search lies in how each approach interprets and processes user queries.

Keyword Search

This traditional method relies on matching the exact words in the query with those in the indexed documentation. For example, a user searching for "Product installation" would receive results containing those exact words, regardless of the broader context or intent behind the query.

The mechanism behind keyword search is relatively straightforward. The search engine indexes all documents based on the words they contain and retrieves results based on how closely the words in the query match the indexed words.

Semantic Search

In contrast, semantic search goes beyond literal word matches to understand the meaning and intent behind the query. For example, if a user asks, "How do I install the application?" a semantic search engine would interpret the intent (installing a product) and retrieve results that provide relevant instructions, even if the exact phrase isn't present in the documents.

Semantic search uses Natural Language Processing (NLP) to understand the context, synonyms, and relationships between words. It then matches the query with the most contextually relevant content, rather than just the closest word matches.

For example, if the query is "How do I set up the application?", a semantic search engine would recognize that "set up" is synonymous with "install", and that "application" could relate to a specific product, retrieving documentation on that specific product's installation process.

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‍Why Semantic Search Is Crucial for Technical Content Retrieval 

In the realm of technical content, precision and relevance are paramount. Users often search for very specific, nuanced information to solve complex problems or perform critical tasks. Traditional keyword search engines can struggle in this task, leading to user frustration and inefficiency. Here’s why semantic search is a game-changer:

• Enhanced Relevance: Semantic search improves the relevance of search results by understanding the intent behind the query, not just the words used. This leads to more accurate and contextually appropriate answers.
• Context Awareness: In technical content, the same term can have different meanings depending on the context. Semantic search engines can discern these nuances, delivering results that are tailored to the specific context of the user’s query.
• Improved User Experience: By providing more relevant and accurate search results, semantic search enhances the overall user experience, reducing the time users spend searching for information and increasing their satisfaction with the results.
• Support for Natural Language Queries: Users are increasingly accustomed to using natural language queries, thanks to the prevalence of GPT assistants and advanced search engines. Semantic search aligns with this trend, making it easier for users to find the information they need using conversational language.

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How We Implemented Semantic Search at Zoomin

To implement semantic search at Zoomin, we built a robust infrastructure that relies on key technologies like embeddings, chunking, and vector search using K-Nearest Neighbors (KNN).

• Embedding: This is a dense vector representation of words, phrases, or entire documents, created by an LLM (Large Language Model). Embeddings capture the semantic meaning of text, allowing the search engine to compare and match the meanings of different texts.
• Chunking: Instead of indexing entire documents, we break down technical content documents into smaller chunks. This allows the search engine to pinpoint the most relevant sections of the document, rather than just identifying a general match.
• Vector Search Using K-Nearest Neighbors: When a user submits a query, the search engine creates an embedding for the query and then uses KNN to find the closest matches within the vector space. This process enables the engine to find the most semantically similar content, even if the exact words don’t match.

The Implementation Process

1. Defining the Goal

Our goal was to enhance the relevance of search results for natural language queries, improving the performance of our RAG-based AI applications — Zoomin GPT Search and Zoomin Conversational GPT. By integrating semantic search, we aimed to ensure that retrieved content is contextually relevant, not just keyword-matched. This optimization directly strengthens the RAG process, leading to more accurate and tailored AI-generated responses.

2. Building the Infrastructure

To integrate semantic search into Zoomin’s capabilities, we built an infrastructure that allowed us to create and store embeddings. Each topic in our technical documentation is indexed as multiple chunks, with an embedding generated for each chunk using an embedding model. These embeddings are stored in a vector database, which allows for efficient retrieval based on semantic similarity.

When a user submits a query, the system generates an embedding for the query using the same embedding model that is used to create the chunks’ embeddings. This embedding is then compared against the stored embeddings using KNN, which identifies the most semantically relevant chunks of content.

3. Testing and Optimization

To ensure that our semantic search implementation was effective, we created test sets consisting of both keyword and natural language queries. For each query, we identified the most relevant topic in the existing documentation, and tested various search methods to determine which one returned the most accurate results.

We compared the following methods:

• Keyword search as-is: The traditional method we’ve used so far. 
• Keyword search with extracted keywords: This method involves first extracting key terms from the user's query and then performing a search based on those terms.
• Hybrid search: A combination of keyword search and vector search.
• Vector search: Purely relying on the vector-based similarity.

The Results: Our testing revealed that the hybrid search method provided the best relevance for both keyword and natural language queries. On average, relevance improved by 73% for natural language queries and 43% for keyword queries, demonstrating a significant enhancement over traditional search methods.

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‍How You Can Enhance RAG with Semantic Search Using Zoomin’s Platform and AI Applications

Semantic search isn’t just a standalone feature. It plays a crucial role in enhancing RAG within our AI applications. By improving the relevance and accuracy of the content retrieved during the initial phase of RAG, semantic search ensures that the generative models work with the most contextually appropriate information. This leads to more accurate, context-aware, and useful AI-generated responses.

At Zoomin, we are rolling out this enhanced semantic search capability in our Zoomin GPT Search and Zoomin Conversational GPT applications. Early feedback from customers has been overwhelmingly positive, with users noting significant improvements in the relevance and usefulness of search results and AI responses.

More than using Zoomin’s AI applications, customers can leverage our Platform solution to create their own AI applications using RAG, powered by Zoomin’s semantic search capabilities. By utilizing our platform, organizations can build custom AI solutions that harness the power of semantic search to retrieve the most relevant technical content, ensuring that their AI applications deliver precise and context-aware responses. This flexibility allows our customers to tailor their AI tools to meet their specific needs, providing a powerful way to enhance their technical content management and user experience.

Conclusion: A New Era of Technical Content Search

We’ve explored how semantic search transforms the way technical content is retrieved and utilized in AI applications. By understanding and implementing semantic search, organizations can significantly improve the relevance and accuracy of search results, leading to a better user experience and more effective technical content management.

Stay tuned for the final post on this series, where we’ll explore the future of RAG in technical documentation with the introduction of knowledge graphs using GraphRAG. This cutting-edge approach promises to further revolutionize how AI interacts with technical content, pushing the boundaries of what’s possible in documentation management.

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