Updated README to include the new EmbeddedDocumentStore stuff.

README.md

@@ -7,20 +7,25 @@
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-`CleverBird` is a Swift Package that provides a convenient way to interact with OpenAI's chat APIs and perform various tasks, including token counting and encoding. The package is designed to deliver a superior Developer Experience (DX) by making the chat thread the center of the interactions. While there are numerous Swift Packages available for interacting with OpenAI, `CleverBird` stands out due to its focus on simplicity and seamless integration of the handy `TokenEncoder` class. 
+`CleverBird` is a Swift Package that provides a convenient way to interact with OpenAI's chat APIs and perform various tasks, including token counting and encoding. The package is designed to deliver a superior Developer Experience (DX) by making the chat thread the center of the interactions.
+
+`CleverBird` includes support for document embeddings and similarity queries. This makes it a versatile tool for a broad range of applications, especially cases where chat prompts need enhanced contextual memory.
 
 `CleverBird` is focused narrowly on chat-based interactions, and making them awesome.
 
 Please note that `CleverBird` is an *unofficial* package, not provided by OpenAI itself.
 
 ## Features
 
+### Core Features
 - Asynchronous API calls with Swift's async/await syntax
-- Supports token counting and encoding with the `TokenEncoder` class
-- Allows customization of various parameters, such as temperature and penalties
 - Streamed responses for real-time generated content
 - Built-in token counting for usage limit calculations
 
+### Specialized Features
+- Token Encoding: Facilitates token counting and encoding through the `TokenEncoder` class.
+- Document Embedding and Similarity Queries: Utilize the `EmbeddedDocumentStore` class for managing and querying document similarities.
+
 ## Usage Instructions
 
 Import the `CleverBird` package:
@@ -162,6 +167,46 @@ Finally, call the `complete()` function to generate a response. If the assistant
 
 Please note that functions are only supported in non-streaming completions at this time.
 
+## Using Embeddings
+
+The `EmbeddedDocumentStore` class provides a convenient way to manage and query a collection of documents based on their similarity. This class allows you to:
+
+- Add documents to an internal store.
+- Generate embeddings for those documents using a specified model.
+- Query the store for similar documents to a given input document.
+
+First, add an instance of the `EmbeddedDocumentStore` to your code:
+
+```swift
+let openAIAPIConnection = OpenAIAPIConnection(apiKey: "your_api_key_here")
+let embeddedDocumentStore = EmbeddedDocumentStore(connection: connection)
+```
+
+You can add a single document or a batch of documents to the store.
+
+```swift
+let singleDocument = "My single document"
+try await embeddedDocumentStore.embedAndStore(singleDocument)
+
+let documentCollection = ["First document", "Second document", "Third document"]
+try await embeddedDocumentStore.embedAndStore(documentCollection)
+
+```
+
+You can query the store for documents that are similar to an input document.
+
+```swift
+let similarityResults = try await embeddedDocumentStore.queryDocumentSimilarity("Query text here")
+let mostSimilarResult = similarityResults.first?.document ?? "No result returned"
+```
+
+The store can be saved to and loaded from a file (represented in JSON format) for persistent storage.
+
+```swift
+embeddedDocumentStore.save(to: fileURL)
+embeddedDocumentStore.load(from: fileURL)
+```
+
 ## License
 
 `CleverBird` was written by B.T. Franklin ([@btfranklin](https://github.com/btfranklin)) from 2023 onward and is licensed under the [MIT](https://opensource.org/licenses/MIT) license. See [LICENSE.md](LICENSE.md).