Delete Key

Deletes a specific embedding and its associated metadata from a vector store in the AI service. This operation is useful for removing obsolete or incorrect embeddings, ensuring that similarity searches and AI queries return only relevant results.

Source Code Example

Click to expand

use ahnlich_client_rs::ai::AiClient;
use ahnlich_client_rs::error::AhnlichError;
use ahnlich_types::ai::query::DelKey;
use ahnlich_types::ai::server::Del;
use ahnlich_types::keyval::StoreInput;
use ahnlich_types::keyval::store_input::Value;


#[tokio::main]
async fn main() -> Result<(), AhnlichError> {
    // Connect to server
    let ai_client = AiClient::new("http://127.0.0.1:1370".to_string())
        .await
        .expect(" Failed to connect AI client");


    // Define key to delete
    let params = DelKey {
        store: "Deven Kicks".to_string(),
        keys: vec![StoreInput {
            value: Some(Value::RawString("Nike Air Jordans".to_string())),
        }],
    };


    // Call delete
    let response: Del = ai_client.del_key(params, None).await?;
    println!(" Deleted key result: {:?}", response);


    Ok(())
}

Parameters

• params: DelKey — Specifies the store and the unique key of the embedding to remove.

• tracing_id: Option<String> — Optional trace parent ID for observability and distributed tracing.

Returns

• Ok(Del) — Confirmation that the embedding and metadata were successfully deleted.

• Err(AhnlichError) — Returned if the key does not exist, the store is unavailable, or the deletion fails.

Behavior (explains the code, brief)

• Wraps the DelKey parameters in a tonic::Request.

• Attaches tracing metadata if provided.

• Sends the deletion request to the AI service via RPC.

• Awaits the response and extracts the result.

• Returns a Del object indicating successful deletion.