add Semantic Search doc and other docs improvements

Author: crpietschmann

docs/docs/concepts/index.md

@@ -2,7 +2,7 @@
 title: Concepts
 description: Understand the core concepts behind SharpVector, from vector similarity to embedding strategies and in-memory architecture.
 ---
-# Concepts
+# :octicons-light-bulb-24: Concepts
 
 ## What is a Vector Database?
 

docs/docs/embeddings/index.md

@@ -1,4 +1,7 @@
-# Embeddings
+---
+title: Embeddings
+---
+# :fontawesome-solid-square-binary: Embeddings
 
 `Build5Nines.SharpVector` includes the following support for using AI Models to generate the text embeddings for the vector database instead of generating them locally. The use of an AI Embeddings model can greatly increase the quality of the semantic search.
 

docs/docs/get-started/data-management/index.md

@@ -2,7 +2,7 @@
 title: Data Management
 
 ---
-# Data Management
+# :material-database-edit-outline: Data Management
 
 Since `Build5Nines.SharpVector` is a database, it also has data management methods available. These methods enable you to add, remove, and update the text documents that are vectorized and indexed within the semantic database.
 

docs/docs/get-started/index.md

@@ -2,7 +2,7 @@
 title: Get Started
 description: Get up and running with SharpVector in minutes. Learn how to install, initialize, and begin storing and searching vectorized text data.
 ---
-# Get Started
+# :octicons-rocket-24: Get Started
 
 It's really easy to get started with using `Build5Nines.SharpVector`. Simply follow the below steps.
 

docs/docs/get-started/metadata/index.md

@@ -1,7 +1,7 @@
 ---
 title: Metadata
 ---
-# Metadata
+# :material-database-cog-outline: Metadata
 
 The `Build5Nines.SharpVector` vector database enables semantic search for `Text` that is stored in the database. Being able to semantically search text is an extremely useful way to lookup more information related to the text. For this purpose, `Metadata` is stored alongside the `Text` within the vector database. This way, when `Text` is found when performing a semantic search, then the matching `Metadata` is also retrieved.
 

docs/docs/get-started/search/index.md

@@ -0,0 +1,106 @@
+---
+title: Semantic Search
+---
+# :material-file-search: Semantic Search
+
+Once text items and their associated metadata have been added to the vector database, the database can be used for semantic search to find matching text items for a given query.
+
+The `BasicMemoryVectorDatabase` and `MemoryVectorDatabase<>` classes both contain `.Search` and `.SearchAsync` methods that can be used to perform semantic search on the database:
+
+=== "Sync"
+
+    ```csharp
+    var query = "some text to search";
+    var results = vdb.Search(query);
+    ```
+
+=== "Async"
+
+    ```csharp
+    var query = "some text to search";
+    var results = await vdb.SearchAsync(query);
+    ```
+
+## Metadata Filters
+
+The `.Search` and `.SearchAsync` methods also include the ability to pre-filter the search results based on a boolean evaluation of the `Metadata` for the text item. This check is run before the vector similarity search is performed, and can help increase search performance on large datasets.
+
+Here are a couple examples of using the `filter` parameter to perform `Metadata` filtering when performing semantic searches:
+
+=== "Sync"
+
+    ```csharp
+    var vdb = new BasicMemoryVectorDatabase();
+
+    // load text and metadata into database
+
+    var query = "some text to search";
+    var results = vdb.Search(
+        query,
+        filter: (metadata) => {
+            // perform some operation to check metadata
+            // return true or false
+            return metadata.Contains("B59");
+        }
+    );
+    ```
+
+=== "Async"
+
+    ```csharp
+    var vdb = new MemoryVectorDatabase<Person>();
+
+    // load text and metadata into database
+
+    var query = "some text to search";
+    var results = vdb.SearchAsync(
+        query,
+        filter: async (metadata) => {
+            // perform some operation to check metadata
+            // return true or false
+            return metadata.LastName == "Pietschmann";
+        }
+    );
+    ```
+
+!!! info "OpenAI and Ollama Support"
+
+    This functionality works the same with both [:simple-openai: OpenAI and :simple-ollama: Ollama supported vector databases](../../embeddings/index.md) too.
+
+## Paging
+
+The `.Search` and `.SearchAsync` methods also include the ability to perform paging on the text items returned from the semantic search. This is performed after the similarity search and the `filter` has been applied to the search results. This is done using the optional `pageCount` and `pageIndex` paramters.
+
+Here are a couple examples of using the `pageCount` and `pageIndex` parameters to perform paging with the semantic search results:
+
+=== "Sync"
+
+    ```csharp
+    var vdb = new BasicMemoryVectorDatabase();
+
+    // load text and metadata into database
+
+    var query = "some text to search";
+    var results = vdb.Search(
+        query,
+        pageIndex: 0, // return first page of results (default: 0)
+        pageCount: 6  // limit length of this page of results (default: unlimited)
+    );
+    ```
+
+=== "Async"
+
+    ```csharp
+    var vdb = new MemoryVectorDatabase<Person>();
+
+    // load text and metadata into database
+
+    var query = "some text to search";
+    var results = vdb.SearchAsync(
+        query,
+        pageIndex: 0, // return first page of results (default: 0)
+        pageCount: 6  // limit length of this page of results (default: unlimited)
+    );
+    ```
+
+The `pageIndex` and `pageIndex` paramters are optional, and can be used individually or together.

docs/docs/index.md

@@ -2,7 +2,7 @@
 title: Discover
 description: The lightweight, in-memory, semantic search, text vector database for .NET that powers intelligent search and recommendation features.
 ---
-# Discover Build5Nines.SharpVector
+# :fontawesome-regular-compass: Discover Build5Nines.SharpVector
 
 **Build5Nines.SharpVector** is the lightweight, in-memory, semantic search, text vector database built for .NET applications. It enables fast and flexible vector-based similarity search for text data — ideal for search engines, recommendation systems, semantic analysis, and AI-enhanced features.
 

docs/docs/license/index.md

@@ -4,7 +4,7 @@ description: Review the MIT license terms for using and contributing to the Shar
 date: 2025-04-13
 ---
 
-# License
+# :octicons-file-badge-24: License
 
 ```text
 MIT License

docs/docs/persistence/index.md

@@ -1,4 +1,7 @@
-# Data Persistence
+---
+title: Data Persistence
+---
+# :octicons-file-24: Data Persistence
 
 The `Build5Nines.SharpVector` library provides easy-to-use methods for saving a memory-based vector database to a file or stream and loading it again later. This is particularly useful for caching indexed content between runs, deploying pre-built vector stores, or shipping databases with your application.
 

docs/docs/resources/index.md

@@ -3,7 +3,7 @@ title: Resources
 description: Dive deeper with curated resources, links, and tools for working with vector databases, semantic search, and SharpVector.
 ---
 
-# Resources
+# :octicons-link-24: Resources
 
 ## Tutorials
 

docs/docs/samples/index.md

@@ -3,7 +3,7 @@ title: Samples
 description: Explore real-world code samples to see SharpVector in action. Build search engines, intelligent note apps, and more.
 ---
 
-# Samples
+# :material-run-fast: Samples
 
 ## Sample Console App
 

docs/docs/text-chunking/index.md

@@ -2,7 +2,7 @@
 title: Text Chunking
 description: Learn how to break large documents into smaller chunks to improve vector search relevance and optimize embedding performance.
 ---
-# Text Chunking
+# :material-script-text: Text Chunking
 
 **Text chunking** is the process of breaking up large documents into smaller segments ("chunks") before embedding and storing them in a vector database. This allows for more accurate semantic search and improves performance in applications that deal with large bodies of text.
 
@@ -124,7 +124,10 @@ The `TextDataLoader<TKey, TValue>` class can be used to load documents into the
     }
     ```
 
-> 🧠 Tip: Use chunking method and size that best aligns with your content type and retrieval goals.
+!!! info "Optimization Tip"
+    Use chunking method and size that best aligns with your content type and retrieval goals. For larger documents, text chunking will be required to get the best semantic search results returned from the vector database.
+
+    Some experimentation on your data set may be required to find the text chunking strategy that works best for your solution.
 
 ---
 

docs/mkdocs.yml

@@ -64,7 +64,7 @@ markdown_extensions:
   - md_in_html
   - toc:
       permalink: true
-      toc_depth: 3
+      toc_depth: 2
   - pymdownx.critic
   - pymdownx.caret
   - pymdownx.keys
@@ -142,18 +142,10 @@ nav:
       - Prerequisites: get-started/#prerequisites
       - Install Nuget Package: get-started/#install-nuget-package
       - Basic Example: get-started/#basic-example
-      - Metadata:
-          - get-started/metadata/index.md
-          - Adding Metadata: get-started/metadata/#adding-metadata
-          - JSON and String Metadata: get-started/metadata/#json-and-string-metadata
-          - Custom Metadata Type: get-started/metadata/#custom-metadata-type
-
-      - Data Management:
-          - get-started/data-management/index.md
-          - Get Text Item Id: get-started/data-management/#get-text-item-id
-          - Get Item By Id: get-started/data-management/#get
-          - Update Item: get-started/data-management/#update
-          - Delete Item: get-started/data-management/#delete
+      - Metadata: get-started/metadata/index.md
+      - Semantic search: get-started/search/index.md
+      - Data Management: get-started/data-management/index.md
+          
   - Concepts:
       - concepts/index.md
       - What is a Vector Database?: concepts/#what-is-a-vector-database

src/SharpVectorTest/VectorDatabaseTests.cs

@@ -1070,7 +1070,7 @@ public async Task BasicMemoryVectorDatabase_SearchAsync_01()
     [TestMethod]
     public async Task BasicMemoryVectorDatabase_SearchAsync_02()
     {
-        var vdb = new BasicMemoryVectorDatabase();
+        var vdb = new MemoryVectorDatabase<string>();
 
         // // Load Vector Database with some sample text
         vdb.AddText("The 👑 King", "metadata1");