Merge pull request #1782 from SciPhi-AI/feature/centralized-limit-cal…

…c-and-chunk-limits

Feature/centralized limit calc and chunk limits

js/sdk/src/v3/clients/users.ts

@@ -323,6 +323,7 @@ export class UsersClient {
     name?: string;
     bio?: string;
     profilePicture?: string;
+    metadata?: Record<string, string | null>;
   }): Promise<WrappedUserResponse> {
     const data = {
       ...(options.email && { email: options.email }),
@@ -332,6 +333,7 @@ export class UsersClient {
       ...(options.profilePicture && {
         profile_picture: options.profilePicture,
       }),
+      ...(options.metadata && { metadata: options.metadata }),
     };
 
     return this.client.makeRequest("POST", `users/${options.id}`, {
@@ -515,26 +517,4 @@ export class UsersClient {
     return this.client.makeRequest("GET", `users/${options.id}/limits`);
   }
 
-
-  /**
-   * **Patch metadata** for a user using a Stripe-like approach.
-   *
-   * The `metadata` parameter merges existing metadata with new keys and values:
-   * - `metadata[key] = "some value"` => sets or updates the key
-   * - `metadata[key] = ""` => removes the key
-   * - empty `{}` => removes all metadata keys
-   *
-   * @param id The user ID to patch
-   * @param metadata Partial metadata updates
-   * @returns WrappedUserResponse
-   */
-  @feature("users.patchMetadata")
-  async patchMetadata(options: {
-    id: string;
-    metadata: Record<string, string | null>;
-  }): Promise<WrappedUserResponse> {
-    return this.client.makeRequest("PATCH", `users/${options.id}/metadata`, {
-      data: options.metadata,
-    });
-  }
 }

llms.txt

@@ -11001,7 +11001,7 @@ Below is a sample `r2r.toml` with essential configurations:
 ```toml
 [app]
 default_max_documents_per_user = 100
-default_max_chunks_per_user = 100000
+default_max_chunks_per_user = 10000
 default_max_collections_per_user = 10
 
 [agent]

py/core/configs/r2r_azure.toml

@@ -21,7 +21,7 @@ batch_size = 256
 
 [embedding]
 provider = "litellm"
-base_model = "openai/text-embedding-3-small" # continue with `openai` for embeddings, due to server rate limit on azure
+base_model = "azure/text-embedding-3-small"
 base_dimension = 512
 
 [file]