add model and embedding customization on runtime

README.md

@@ -132,6 +132,8 @@ const runtime = new BgentRuntime({
   evaluators: [
     /* your custom evaluators */
   ],
+  model: "gpt-3.5-turbo", // whatever model you want to use
+  embeddingModel: "text-embedding-3-large", // whatever model you want to use
 });
 ```
 

docs/docs/classes/BgentRuntime.md

@@ -24,6 +24,7 @@ Creates an instance of BgentRuntime.
 | `opts` | `Object` | The options for configuring the BgentRuntime. |
 | `opts.actions?` | [`Action`](../interfaces/Action.md)[] | Optional custom actions. |
 | `opts.debugMode?` | `boolean` | If true, debug messages will be logged. |
+| `opts.embeddingModel?` | `string` | The model to use for embedding. |
 | `opts.evaluators?` | [`Evaluator`](../interfaces/Evaluator.md)[] | Optional custom evaluators. |
 | `opts.model?` | `string` | The model to use for completion. |
 | `opts.providers?` | [`Provider`](../interfaces/Provider.md)[] | Optional context providers. |
@@ -62,6 +63,14 @@ Store and recall descriptions of users based on conversations.
 
 ___
 
+### embeddingModel
+
+• **embeddingModel**: `string` = `"text-embedding-3-large"`
+
+The model to use for embedding.
+
+___
+
 ### evaluators
 
 • **evaluators**: [`Evaluator`](../interfaces/Evaluator.md)[] = `[]`

docs/docs/index.md

@@ -140,6 +140,8 @@ const runtime = new BgentRuntime({
   evaluators: [
     /* your custom evaluators */
   ],
+  model: "gpt-3.5-turbo", // whatever model you want to use
+  embeddingModel: "text-embedding-3-large", // whatever model you want to use
 });
 ```
 

src/lib/runtime.ts

@@ -85,6 +85,11 @@ export class BgentRuntime {
    */
   model = "gpt-3.5-turbo-0125";
 
+  /**
+   * The model to use for embedding.
+   */
+  embeddingModel = "text-embedding-3-large";
+
   /**
    * Store messages that are sent and received by the agent.
    */
@@ -129,6 +134,7 @@ export class BgentRuntime {
    * @param opts.evaluators - Optional custom evaluators.
    * @param opts.providers - Optional context providers.
    * @param opts.model - The model to use for completion.
+   * @param opts.embeddingModel - The model to use for embedding.
    */
   constructor(opts: {
     recentMessageCount?: number; // number of messages to hold in the recent message cache
@@ -140,13 +146,15 @@ export class BgentRuntime {
     evaluators?: Evaluator[]; // Optional custom evaluators
     providers?: Provider[];
     model?: string; // The model to use for completion
+    embeddingModel?: string; // The model to use for embedding
   }) {
     this.#recentMessageCount =
       opts.recentMessageCount ?? this.#recentMessageCount;
     this.debugMode = opts.debugMode ?? false;
     this.supabase = opts.supabase;
     this.serverUrl = opts.serverUrl ?? this.serverUrl;
     this.model = opts.model ?? this.model;
+    this.embeddingModel = opts.embeddingModel ?? this.embeddingModel;
     if (!this.serverUrl) {
       console.warn("No serverUrl provided, defaulting to localhost");
     }
@@ -269,7 +277,7 @@ export class BgentRuntime {
    * @returns The embedding of the input.
    */
   async embed(input: string) {
-    const embeddingModel = "text-embedding-3-large";
+    const embeddingModel = this.embeddingModel;
     const requestOptions = {
       method: "POST",
       headers: {