Create Predicate Index

Creates a predicate index in the AI service to optimize filtered embedding queries. Speeds up retrieval of embeddings based on metadata constraints, improving the performance of get_pred operations.

Source Code Example

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use ahnlich_client_rs::ai::AiClient;
use ahnlich_client_rs::error::AhnlichError;
use ahnlich_types::ai::query::CreatePredIndex;
use ahnlich_types::ai::server::CreateIndex;


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


    // Define which store and which predicates to index
    let params = CreatePredIndex {
        store: "Deven Kicks".to_string(),
        predicates: vec!["Brand".to_string(), "Vintage".to_string()],
    };


    // Call the API
    let response: CreateIndex = ai_client.create_pred_index(params, None).await?;


    println!(" Created predicate indexes: {:?}", response);


    Ok(())
}

Parameters

• params: CreatePredIndex — Specifies the store and the metadata fields to index for predicate-based queries.

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

Returns

• Ok(CreateIndex) — Confirmation that the predicate index was successfully created, along with index details.

• Err(AhnlichError) — Returned if index creation fails due to invalid parameters, store issues, or server errors.

Behavior (explains the code, brief)

• Wraps the CreatePredIndex input in a tonic::Request.

• Adds optional tracing metadata.

• Calls the AI/DB service’s create_pred_index RPC endpoint.

• Waits for the response and extracts the index creation result.

• Returns the newly created index information.