Pipeline

Creates a new AI pipeline for batching multiple operations in the AI client. Pipelines allow you to queue multiple requests (e.g., set, get_sim_n, get_key) and execute them in sequence, ensuring consistent ordering and efficient handling of AI service calls. This is particularly useful for bulk processing or workflows that require multiple embeddings or queries to be executed together.

Source Code Example

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use ahnlich_client_rs::ai::{AiClient, AiPipeline};
use ahnlich_client_rs::error::AhnlichError;
use tokio;


#[tokio::main]
async fn main() -> Result<(), AhnlichError> {
    // Replace with your AI server address
    let addr = "127.0.0.1:1370".to_string();


    // Initialize the client
    let client = AiClient::new(addr).await?;


    // Create a pipeline
    let mut pipeline = client.pipeline(None);


    // Add some example queries
    pipeline.ping();      // Ping the server
    pipeline.list_stores(); // List existing stores


    // Execute the pipeline
    let response = pipeline.exec().await?;


    println!("Pipeline response: {:#?}", response);


    Ok(())
}

Parameters

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

Returns

• AiPipeline — A new pipeline instance that can queue AI client operations and execute them sequentially.

Behavior (explains the code, brief)

• Initializes an empty queries vector to hold pending operations.

• Clones the AI client for use within the pipeline.

• Attaches optional tracing metadata for observability.

• Returns a fully initialized AiPipeline object ready for queuing operations.