Introduction
Organizations generate enormous amounts of information every day. Documentation, troubleshooting guides, standard operating procedures, internal policies, technical specifications, support articles, and project documentation are often distributed across multiple systems.
As knowledge repositories grow, finding the right information becomes increasingly difficult. Employees frequently spend significant time searching through documents, browsing intranet portals, or asking colleagues for answers that already exist somewhere in the organization.
Traditional keyword-based search systems often struggle when users phrase questions differently from the wording found in documents. This leads to poor search results, duplicated work, and reduced productivity.
An AI-powered knowledge base search system solves this challenge by combining semantic search, vector embeddings, and Large Language Models (LLMs) to understand user intent and deliver highly relevant answers.
In this article, you'll learn how to build an intelligent knowledge base search platform using ASP.NET Core and Azure AI Search.
Why Traditional Search Often Fails
Traditional search engines rely primarily on keyword matching.
For example, a user may search:
How do I reset my account password?
But the documentation might contain:
Credential Recovery Process
Although both refer to the same concept, keyword matching may fail to connect them.
Common limitations include:
Exact keyword dependency
Poor understanding of context
Difficulty handling synonyms
Limited relevance ranking
Weak support for natural language questions
Semantic search addresses these limitations.
What Is Azure AI Search?
Microsoft Azure AI Search is a cloud-based search platform that supports:
Full-text search
Semantic search
Vector search
Hybrid search
Document indexing
AI enrichment
These capabilities make it well suited for building modern AI-powered search experiences.
Typical workflow:
Documents
|
v
Azure AI Search
|
v
Semantic Retrieval
|
v
AI Response
Understanding the Architecture
A knowledge base assistant typically includes:
Knowledge Sources
Document Processing Layer
Azure AI Search
ASP.NET Core API
AI Model
User Interface
Architecture:
Knowledge Sources
|
v
Document Indexing
|
v
Azure AI Search
|
v
ASP.NET Core API
|
v
AI Assistant
This architecture enables intelligent information retrieval.
Identifying Knowledge Sources
Knowledge may come from various systems.
Examples include:
PDF documents
Internal wikis
SharePoint sites
Markdown files
Support articles
Technical documentation
Employee handbooks
Example document:
Password Reset Policy
Employees must use the Identity
Portal to reset credentials.
These documents become searchable content.
Creating a Knowledge Document Model
Define a model representing indexed content.
public class KnowledgeDocument
{
public string Id { get; set; }
= string.Empty;
public string Title { get; set; }
= string.Empty;
public string Content { get; set; }
= string.Empty;
public string Category
{
get;
set;
} = string.Empty;
}
This model forms the foundation of the search index.
Setting Up Azure AI Search
Install the SDK:
dotnet add package
Azure.Search.Documents
Create a client:
using Azure.Search.Documents;
var client =
new SearchClient(
endpoint,
indexName,
credential);
The client provides access to indexing and search capabilities.
Indexing Knowledge Base Content
Documents must be indexed before they can be searched.
Example:
await client.UploadDocumentsAsync(
documents);
Once indexed, content becomes available for semantic retrieval.
Understanding Vector Search
Vector search uses embeddings rather than keywords.
Example:
Reset Password
and
Recover Account Access
generate similar vector representations.
Workflow:
Document
|
v
Embedding Model
|
v
Vector Storage
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