Introduction
Modern applications generate and consume enormous amounts of information—PDFs, Word documents, web pages, product catalogs, support tickets, database records, manuals, and knowledge-base articles.
Traditional database queries work well when users know the exact keywords they are looking for. But modern applications increasingly need to understand meaning and intent, not just exact words.
This is where Azure AI Search becomes extremely useful.
Azure AI Search is a managed search and retrieval service from Microsoft Azure that supports traditional full-text search, vector search, hybrid search, semantic ranking, and generative-AI/RAG scenarios. (Microsoft Learn)
A typical modern architecture looks like this:
User
|
v
Angular / Web / Mobile
|
v
ASP.NET Core API
|
+---------+---------+
| |
v v
Azure AI Search Azure OpenAI
| |
|<--- Context ------|
|
v
Search Results
|
+--------> LLM
|
v
Final AnswerThis article explains Azure AI Search from the fundamentals through an end-to-end ASP.NET Core + Azure AI Search + Azure OpenAI RAG application.
1. What Is Azure AI Search?
Azure AI Search is Microsoft's cloud-based search-as-a-service platform.
It allows applications to:
Store searchable documents
Search text
Search using keywords
Search by semantic meaning
Perform vector similarity searches
Perform hybrid searches
Apply filters
Sort and facet results
Re-rank search results using semantic ranking
Retrieve grounding information for RAG applications
Microsoft documentation currently describes Azure AI Search as supporting text, vector, and multimodal content for traditional and generative search scenarios. (Microsoft Learn)
Think of it as:
Traditional Database
|
| Exact data retrieval
v
SQL Query
Azure AI Search
|
| Intelligent information retrieval
v
Keyword + Semantic + Vector + Hybrid Search2. Why Do We Need Azure AI Search?
Suppose a company has:
100,000 PDF documents
50,000 Word documents
1 million support tickets
500,000 product descriptionsA user asks:
"How can I reset my corporate VPN password?"
A traditional SQL query might search for:
WHERE Description LIKE '%VPN%'
AND Description LIKE '%password%'But the user could ask:
"I forgot the credentials needed to connect remotely."
The words are completely different.
A semantic/vector search engine can recognize that:
"forgot credentials"is related to:
"reset VPN password"This is one of the major advantages of vector and semantic search.
3. Azure AI Search vs SQL Server
Azure AI Search does not replace SQL Server.
They solve different problems.
| SQL Server | Azure AI Search |
|---|---|
| Transactional data | Search/retrieval |
| INSERT/UPDATE/DELETE | Indexing/search |
| Relationships | Search indexes |
| Joins | Search queries |
| ACID transactions | Relevance ranking |
| Structured data | Text/vector content |
| Business transactions | Information retrieval |
A common architecture is:
SQL Server
|
| Product / Customer / Order data
|
v
ASP.NET Core
|
+------> SQL Server
|
+------> Azure AI Search4. Important Azure AI Search Concepts
Before implementing Azure AI Search, understand these concepts.
4.1 Search Service
The Azure AI Search service is the Azure resource that provides the search engine.
For example:
my-company-search.search.windows.netApplications communicate with this service through:
REST API
Azure SDK
.NET SDK
Python SDK
JavaScript/TypeScript SDK
Java SDK
Microsoft provides official SDKs and samples for these languages. (Microsoft Learn)
5. Search Index
A search index is similar conceptually to a database table, but it is designed specifically for search.
For example:
ProductIndexcould contain:
ProductId
ProductName
Description
Category
Price
Availability
DescriptionVectorExample:
{
"ProductId": "P1001",
"ProductName": "Laptop",
"Description": "Business laptop with 16GB RAM",
"Category": "Computer",
"Price": 75000
}6. Search Fields
Each index contains fields.
For example:
ProductId
ProductName
Description
Category
Price
DescriptionVectorDifferent fields can have different capabilities.
For example:
ProductName
searchable
Description
searchable
Category
filterable
Price
filterable
sortable
DescriptionVector
vector searchable7. Documents
A document represents one searchable item inside an index.
For example:
{
"id": "101",
"title": "Azure AI Search",
"content": "Azure AI Search provides intelligent search capabilities.",
"category": "Azure"
}Another document might represent:
PDF chunk
Product
Customer FAQ
Support ticket
Knowledge-base article
Web page8. Full-Text Search
Traditional full-text search searches words contained in documents.
For example:
Search:
Azure Service BusAzure AI Search looks for matching terms in searchable fields.
This approach is particularly useful for:
Product codes
Names
Exact terminology
IDs
Dates
Specialized technical terms
Microsoft notes that keyword search can be particularly useful where exact matching matters. (Microsoft Learn)
9. Vector Search
Vector search is one of the most important capabilities for modern AI applications.
An embedding model converts text into a numerical representation called a vector.
For example:
"How do I reset my password?"might become conceptually:
[0.021, -0.192, 0.442, ...]The vector represents semantic information rather than simply storing individual keywords.
The user's question is also converted into a vector.
Then Azure AI Search finds documents whose vectors are mathematically similar.
Microsoft describes vector search as useful for conceptual similarity and multilingual or multimodal scenarios. (Microsoft Learn)
10. Why Vector Search Is Powerful
Consider these two sentences:
Document:
"Employees can change their credentials from the security portal."
User:
"Where can I update my password?"There may be no exact keyword match for every word.
But semantically:
change credentials
≈
update passwordVector search can identify that relationship.
11. What Is an Embedding?
An embedding is a numerical representation of content.
For example:
Text
|
v
Embedding Model
|
v
VectorConceptually:
"Azure Functions"
|
v
[0.12, -0.44, 0.72, ...]The actual vector has many dimensions.
The application stores this vector in a vector field in Azure AI Search.
12. Hybrid Search
Hybrid search combines:
Keyword Search
+
Vector Searchin a single request.
Azure AI Search executes the text and vector searches and merges their results using Reciprocal Rank Fusion (RRF). (Microsoft Learn)
Conceptually:
User Query
|
+---------+---------+
| |
v v
Keyword Search Vector Search
| |
v v
Results A Results B
| |
+---------+---------+
|
v
RRF
|
v
Combined ResultsThis is often a strong choice for enterprise search.
13. Semantic Ranking
Semantic ranking provides another relevance layer.
The initial results can be reranked using Microsoft's language-understanding models.
For example:
Query:
"How can I work remotely?"
Result A:
"Remote working policy..."
Result B:
"Office parking policy..."
Result C:
"VPN remote access instructions..."Semantic ranking can identify which results are most relevant to the meaning of the query.
Microsoft describes semantic ranker as a secondary ranking stage over an initial BM25 or RRF result set. (Microsoft Learn)
14. Search Architecture
A modern Azure AI Search application can look like this:
Documents
|
+-------------+-------------+
| | |
v v v
PDF Database Website
| | |
+-------------+-------------+
|
v
Data Processing
|
v
Chunking / Cleaning
|
v
Embeddings
|
v
Azure AI Search
|
Search Index
|
v
User Query
|
+----------+----------+
| |
v v
Keyword Vector
Search Search
| |
+----------+----------+
|
v
RRF
|
v
Semantic Ranker
|
v
Top Results
|
v
Azure OpenAI
|
v
Final Response15. What Is RAG?
RAG means:
Retrieval-Augmented Generation
Instead of asking an LLM to answer only from its trained knowledge, we first retrieve relevant information from our own data.
The process is:
User Question
|
v
Azure AI Search
|
v
Relevant Documents
|
v
Prompt + Retrieved Context
|
v
Azure OpenAI
|
v
Final AnswerMicrosoft describes RAG as a pattern that grounds LLM responses in proprietary content. (Microsoft Learn)
16. Real-Time Example: Employee Knowledge Assistant
Let's build a realistic enterprise application.
Imagine a company has:
HR Policies
IT Policies
Leave Policies
Travel Policies
Insurance Documents
Employee Handbook
Security GuidelinesEmployees can ask:
"How many days of annual leave can I take?"
or:
"What is the process for claiming travel expenses?"
or:
"How do I reset my VPN password?"
Instead of manually searching hundreds of documents, the application retrieves the relevant information automatically.
17. Complete Architecture
Our solution will use:
Angular
|
v
ASP.NET Core Web API
|
+--------------------+
| |
v v
Azure AI Search Azure OpenAI
|
v
Search IndexDocuments:
Azure Blob Storage
|
v
Document Processing
|
v
Chunking
|
v
Embeddings
|
v
Azure AI Search18. Step 1 – Create Azure AI Search
In Azure Portal:
Azure Portal
|
v
Create a resource
|
v
Search serviceConfigure:
Subscription
Resource Group
Service Name
Region
Pricing TierAfter deployment, you will have an Azure AI Search service.
19. Step 2 – Create Azure OpenAI
Create an Azure OpenAI resource and deploy appropriate models for:
Chat / generation
+
EmbeddingsThe embedding model is used to convert content and queries into vectors.
The chat model generates the final response.
20. Step 3 – Store Documents
Suppose we have:
EmployeeHandbook.pdf
LeavePolicy.pdf
TravelPolicy.pdf
SecurityPolicy.pdfA common architecture is:
Azure Blob Storage
|
v
Document Processing
|
v
Azure AI SearchMicrosoft's .NET RAG tutorial similarly demonstrates a solution using Azure OpenAI, Azure AI Search, storage, and an application layer. (Microsoft Learn)
21. Step 4 – Chunk Documents
Large documents should normally be divided into smaller pieces.
For example:
LeavePolicy.pdf
|
v
Chunk 1
Introduction
Chunk 2
Annual Leave
Chunk 3
Sick Leave
Chunk 4
Approval Process
Chunk 5
Carry Forward PolicyWhy?
Because sending an entire 100-page document to an LLM for every question is inefficient.
Instead, retrieve only the relevant chunks.
22. Example Search Document
Our index could contain:
{
"id": "leave-001",
"title": "Annual Leave Policy",
"content": "Employees are entitled to annual leave according to company policy...",
"category": "HR",
"documentName": "LeavePolicy.pdf",
"contentVector": [ ... ]
}The important fields are:
id
title
content
category
documentName
contentVector23. Create a .NET Project
Create an ASP.NET Core Web API:
dotnet new webapi -n EnterpriseSearch.ApiInstall the Azure AI Search SDK:
dotnet add package Azure.Search.DocumentsThe official .NET SDK package is Azure.Search.Documents. (Microsoft Learn)
24. Configuration
appsettings.json:
{
"AzureSearch": {
"Endpoint": "https://YOUR-SEARCH-SERVICE.search.windows.net",
"IndexName": "employee-knowledge"
}
}For production, avoid storing secrets directly in configuration files.
Prefer:
Managed Identity
+
Microsoft Entra ID
+
Azure Key VaultMicrosoft's current .NET RAG tutorial demonstrates managed identities for passwordless service-to-service authentication. (Microsoft Learn)
25. Create Search Service Class
Example:
using Azure;
using Azure.Search.Documents;
using Azure.Search.Documents.Models;
public class SearchService
{
private readonly SearchClient _searchClient;
public SearchService(
IConfiguration configuration)
{
var endpoint =
configuration["AzureSearch:Endpoint"];
var indexName =
configuration["AzureSearch:IndexName"];
var credential =
new Azure.Identity.DefaultAzureCredential();
_searchClient =
new SearchClient(
new Uri(endpoint!),
indexName,
credential);
}
public async Task<List<SearchDocument>> SearchAsync(
string query)
{
var options = new SearchOptions
{
Size = 5
};
options.Select.Add("id");
options.Select.Add("title");
options.Select.Add("content");
options.Select.Add("documentName");
var response =
await _searchClient.SearchAsync<SearchDocument>(
query,
options);
var results = new List<SearchDocument>();
await foreach (var result in response.Value.GetResultsAsync())
{
results.Add(result.Document);
}
return results;
}
}26. Create the API Controller
[ApiController]
[Route("api/search")]
public class SearchController : ControllerBase
{
private readonly SearchService _searchService;
public SearchController(SearchService searchService)
{
_searchService = searchService;
}
[HttpGet]
public async Task<IActionResult> Search(
string query)
{
var results =
await _searchService.SearchAsync(query);
return Ok(results);
}
}Now the API can be called like:
GET /api/search?query=annual leave27. Example Response
The API could return:
[
{
"title": "Annual Leave Policy",
"content": "Employees are entitled to annual leave...",
"documentName": "LeavePolicy.pdf"
},
{
"title": "Employee Handbook",
"content": "Leave requests must be submitted...",
"documentName": "EmployeeHandbook.pdf"
}
]28. Adding Vector Search
For vector search, the index contains a vector field.
Conceptually:
content
contentVectorThe process is:
User Query
|
v
Embedding Model
|
v
Query Vector
|
v
Azure AI Search
|
v
Similar Document Vectors29. Hybrid Search Query
A hybrid query contains both:
search
+
vectorQueriesConceptually:
{
"search": "How do I apply for annual leave?",
"vectorQueries": [
{
"kind": "vector",
"vector": [ ... ],
"fields": "contentVector",
"k": 5
}
],
"top": 5
}Azure AI Search executes both searches and combines their results using RRF. (Microsoft Learn)
30. Semantic Hybrid Search
For high-quality retrieval, you can combine:
Keyword
+
Vector
+
Semantic RankingConceptually:
User Query
|
+----> Keyword Search
|
+----> Vector Search
|
v
RRF
|
v
Semantic Ranker
|
v
Best DocumentsThis is an especially useful architecture for RAG applications. Microsoft documentation highlights hybrid search with semantic ranking as a strong relevance strategy. (Microsoft Learn)
31. Complete RAG Flow
Let's walk through an actual question.
User asks:
"How many days of annual leave can I take?"
Step 1
Angular sends:
POST /api/chatRequest:
{
"question": "How many days of annual leave can I take?"
}Step 2
ASP.NET Core receives the question.
Step 3
The application sends the question to the retrieval layer.
Step 4
The query is converted into a vector.
Step 5
Azure AI Search performs:
Keyword Search
+
Vector SearchStep 6
Results are merged using RRF.
Step 7
Semantic ranking can further improve ordering.
Step 8
Top relevant chunks are returned.
Example:
Annual Leave Policy
-------------------
Employees are entitled to 20 days
of annual leave per calendar year.Step 9
The application builds a prompt:
Answer the user's question using
only the following company policy.
Context:
Employees are entitled to 20 days
of annual leave per calendar year.
Question:
How many days of annual leave can I take?Step 10
Azure OpenAI generates:
According to the Annual Leave Policy,
employees are entitled to 20 days of
annual leave per calendar year.32. Why RAG Is Better Than Sending Everything to the LLM
Without RAG:
User
|
v
LLM
|
v
Possible hallucinationWith RAG:
User
|
v
Azure AI Search
|
v
Company Documents
|
v
Relevant Context
|
v
LLM
|
v
Grounded AnswerRAG doesn't automatically guarantee that every answer is correct, but it gives the model relevant source material from your organization's data.
33. Adding Citations
A production enterprise application should ideally tell users where an answer came from.
For example:
According to the Annual Leave Policy,
employees are entitled to 20 days of annual leave.
Source:
LeavePolicy.pdf
Page 4Your index can store metadata such as:
documentName
pageNumber
chunkId
sourceUrl
departmentThen your API can return:
{
"answer": "Employees are entitled to 20 days of annual leave.",
"sources": [
{
"document": "LeavePolicy.pdf",
"page": 4
}
]
}This greatly improves trust and auditability.
34. Security in Azure AI Search
Enterprise search must consider authorization.
Suppose:
HR Documents
Finance Documents
Engineering DocumentsAn Engineering employee should not automatically retrieve confidential HR documents.
A production architecture should therefore consider:
User Identity
|
v
Authorization
|
v
Security Filter
|
v
Azure AI SearchMetadata can include:
department
userId
role
securityGroup
accessLevelThen search filters can restrict what users are allowed to retrieve.
35. Azure AI Search and Managed Identity
For production applications, avoid this:
API Key stored in appsettings.jsonPrefer:
ASP.NET Core
|
v
Managed Identity
|
v
Microsoft Entra ID
|
v
Azure AI SearchThis eliminates the need to distribute long-lived secrets between Azure services.
36. Common Real-Time Use Cases
Azure AI Search can be used for:
Enterprise Knowledge Search
Company Documents
|
v
AI Search
|
v
Employee AssistantE-Commerce
Products
|
v
AI Search
|
+--> Keyword
+--> Semantic
+--> VectorExample:
"Find lightweight laptops for programming."
Customer Support
Support Tickets
Knowledge Base
Product Manuals
|
v
Azure AI Search
|
v
Support CopilotLegal Document Search
Contracts
Policies
Agreements
|
v
Search + RAGHealthcare Knowledge Systems
For appropriate authorized environments:
Clinical documents
Research documents
Policies
|
v
SearchSensitive applications require strong access control, privacy, compliance, and human oversight.
37. Azure AI Search in Microservices Architecture
For a microservices application:
API Gateway
|
+--------------+--------------+
| | |
v v v
Product Service Order Service Customer Service
| | |
+--------------+--------------+
|
v
Search Service
|
v
Azure AI SearchI recommend keeping search concerns separate from transactional services.
For example:
Product Service
|
v
Product Database
Search Indexing Service
|
v
Azure AI SearchWhen product data changes:
Product Updated
|
v
Event / Message
|
v
Search Indexing Service
|
v
Azure AI SearchThis avoids tightly coupling every service to the search engine.
38. Event-Driven Indexing
For large systems:
Product Service
|
v
Azure Service Bus
|
v
Search Index Worker
|
v
Azure AI SearchFor example:
ProductUpdatedEventcould contain:
{
"productId": "P1001",
"operation": "Updated"
}The indexing worker then updates the corresponding Azure AI Search document.
39. Azure AI Search vs Elasticsearch
| Feature | Azure AI Search | Elasticsearch |
|---|---|---|
| Azure integration | Excellent | Good |
| Microsoft ecosystem | Excellent | Good |
| Vector search | Yes | Yes |
| Hybrid search | Yes | Yes |
| Semantic ranking | Yes | Different approach |
| Azure OpenAI integration | Excellent | Possible |
| Managed Azure service | Yes | Depends on offering |
| .NET integration | Excellent | Excellent |
If your application is already heavily invested in Azure, Azure AI Search is often a natural choice.
40. Azure AI Search vs Database LIKE
Bad approach for large-scale AI search:
SELECT *
FROM Documents
WHERE Content LIKE '%password%';Better:
Azure AI Search
|
+-- Full Text
+-- Vector
+-- Hybrid
+-- Semantic Ranking41. Common Mistakes
Mistake 1 – Indexing entire documents as one chunk
Instead:
Document
|
v
Meaningful chunksMistake 2 – Using only vector search
Vector search is powerful, but exact keywords can be important.
For example:
INC-45872A keyword search may be more useful than semantic similarity.
Therefore:
Keyword + Vectoris often preferable.
Mistake 3 – Sending too many search results to the LLM
Don't retrieve hundreds of chunks.
Instead:
Retrieve
|
v
Rank
|
v
Top relevant chunks
|
v
LLMMistake 4 – Ignoring security
Never assume:
"If the document is indexed,
every user can see it."Security trimming and authorization must be designed into the solution.
Mistake 5 – Storing secrets in source code
Avoid:
const string apiKey = "xxxxxxxx";Use:
Managed Identity
Azure Key Vault
Microsoft Entra ID42. Performance Optimization
Important considerations include:
Chunk Size
Avoid extremely large chunks.
Use logically meaningful sections.
Top K
Retrieve a controlled number of candidates.
For example:
Top 5
Top 10
Top 20depending on the workload.
Hybrid Search
Use hybrid retrieval when both exact matching and semantic matching matter.
Semantic Ranking
Use semantic ranking when better relevance justifies its additional cost/latency.
Filters
Apply metadata filters when possible.
For example:
department = 'IT'or:
documentType = 'Policy'Monitoring
Monitor:
Latency
Query volume
Failed queries
Search relevance
Token usage
LLM latency
Cost43. Production Architecture
A more complete enterprise implementation might look like:
Users
|
v
Azure Front Door
|
v
API Management
|
v
ASP.NET Core API
|
+-----------+-----------+
| |
v v
Azure AI Search Azure OpenAI
|
|
+-----+------+
| |
v v
Vector Index Text Index
|
v
Blob Storage
|
v
DocumentsSupporting services:
Azure Key Vault
Azure Monitor
Application Insights
Azure Service Bus
Microsoft Entra ID44. Monitoring
Use Azure monitoring tools to observe:
Search latency
Request count
Errors
Throttling
Application latency
AI model latency
Token usageApplication Insights can help trace:
HTTP Request
|
v
Search Query
|
v
Azure OpenAI
|
v
ResponseThis is especially valuable when debugging slow RAG applications.
45. How to Design a Production RAG Pipeline
A recommended pipeline is:
1. Collect documents
|
2. Extract text
|
3. Clean text
|
4. Split into chunks
|
5. Generate embeddings
|
6. Store chunks + vectors
|
7. Build search index
|
8. User asks question
|
9. Retrieve candidates
|
10. Hybrid search
|
11. Semantic reranking
|
12. Security filtering
|
13. Build context
|
14. Call Azure OpenAI
|
15. Return answer + sources46. Classic RAG vs Agentic Retrieval
Azure AI Search now also provides agentic retrieval capabilities.
Classic RAG generally follows:
Question
|
v
Search
|
v
Results
|
v
LLMAgentic retrieval can use an LLM to help understand and decompose more complex queries before retrieval.
Microsoft currently documents agentic retrieval as a newer retrieval approach, with some capabilities available in preview depending on the API/features used. (Microsoft Learn)
For a first production implementation, classic hybrid RAG is often easier to understand, test, and operate.
47. Recommended Technology Stack
For a Microsoft-based enterprise application:
Frontend
Angular
Backend
ASP.NET Core Web API
Search
Azure AI Search
LLM
Azure OpenAI
Embeddings
Azure OpenAI embedding model
Storage
Azure Blob Storage
Database
Azure SQL
Messaging
Azure Service Bus
Secrets
Azure Key Vault
Identity
Microsoft Entra ID
Monitoring
Application Insights
Azure Monitor
Deployment
Azure App Service / AKS48. Complete Request Flow
Let's summarize the complete request:
USER
|
v
Angular App
|
v
ASP.NET Core API
|
v
User Question
|
v
Query Processing
|
+--------+--------+
| |
v v
Keyword Search Vector Search
| |
+--------+--------+
|
v
RRF
|
v
Semantic Ranking
|
v
Security Filtering
|
v
Top Relevant Chunks
|
v
Prompt Builder
|
v
Azure OpenAI
|
v
Final Answer
|
v
Answer + Citations
|
v
USER49. Key Interview Questions
If you are preparing for a .NET/Azure interview, these are important questions.
Q1. What is Azure AI Search?
A managed Azure service for information retrieval supporting full-text, vector, hybrid, semantic, and generative-AI search scenarios.
Q2. What is vector search?
Searching for documents based on vector similarity rather than only exact keywords.
Q3. What is hybrid search?
Combining keyword/full-text and vector search in the same request and merging their rankings using RRF. (Microsoft Learn)
Q4. What is semantic ranking?
A second-stage relevance process that reranks an initial result set using language understanding models. (Microsoft Learn)
Q5. Why use Azure AI Search with Azure OpenAI?
Azure AI Search retrieves enterprise-specific information, while Azure OpenAI generates a natural-language response based on that retrieved context.
Q6. What is RAG?
Retrieval-Augmented Generation: retrieve relevant external knowledge and provide it to an LLM as context for generating an answer.
Q7. Why chunk documents?
To retrieve smaller, relevant pieces of information instead of sending entire documents to the LLM.
Q8. What is RRF?
Reciprocal Rank Fusion is used to combine rankings from multiple search result sets, including hybrid search results. (Microsoft Learn)
Q9. How do you secure Azure AI Search?
Use Microsoft Entra ID, managed identities, RBAC, and security-aware filtering/authorization appropriate to the application.
Q10. How would you integrate Azure AI Search into a .NET microservices architecture?
Use a dedicated search/indexing component, publish domain events through messaging when data changes, and update the search index asynchronously.
50. Final Takeaway
Azure AI Search is much more than a traditional search engine.
It can provide:
Traditional Search
+
Vector Search
+
Hybrid Search
+
Semantic Ranking
+
RAG Retrieval
+
Enterprise SecurityThe most important architecture to remember is:
YOUR DATA
|
v
Azure AI Search
|
+---------+---------+
| |
Keyword Search Vector Search
| |
+---------+---------+
|
RRF
|
Semantic Ranking
|
v
Relevant Context
|
v
Azure OpenAI
|
v
Grounded ResponseFor a .NET developer, a particularly powerful enterprise stack is:
Angular
↓
ASP.NET Core
↓
Azure AI Search
↓
Azure OpenAI
↓
Azure SQL / Blob Storage
↓
Azure Service Bus
↓
Key Vault + Managed Identity
↓
Application InsightsThis architecture can be used to build enterprise knowledge assistants, customer-support copilots, product search, document search, internal AI assistants, and RAG-based applications.
Microsoft also provides an official .NET RAG sample that combines Azure AI Search and Azure OpenAI, including document indexing and managed-identity-based authentication. (Microsoft Learn)
Official Microsoft References
In one sentence: Azure AI Search is the retrieval layer that helps your application find the right information, while Azure OpenAI is the generation layer that turns that information into a useful natural-language answer.
No comments:
Post a Comment