Struct MongoDbVectorIndex

pub struct MongoDbVectorIndex<M: EmbeddingModel, C: Send + Sync> { /* private fields */ }

A vector index for a MongoDB collection.

Example

use rig_mongodb::{MongoDbVectorIndex, SearchParams};
use rig::{providers::openai, vector_store::VectorStoreIndex};

#[derive(serde::Deserialize, serde::Serialize, Debug)]
struct WordDefinition {
    #[serde(rename = "_id")]
    id: String,
    definition: String,
    embedding: Vec<f64>,
}

let mongodb_client = mongodb::Client::with_uri_str("mongodb://localhost:27017").await?; // <-- replace with your mongodb uri.
let openai_client = openai::Client::from_env();

let collection = mongodb_client.database("db").collection::<WordDefinition>(""); // <-- replace with your mongodb collection.

let model = openai_client.embedding_model(openai::TEXT_EMBEDDING_ADA_002); // <-- replace with your embedding model.
let index = MongoDbVectorIndex::new(
    collection,
    model,
    "vector_index", // <-- replace with the name of the index in your mongodb collection.
    SearchParams::new(), // <-- field name in `Document` that contains the embeddings.
)
.await?;

// Query the index
let definitions = index
    .top_n::<WordDefinition>("My boss says I zindle too much, what does that mean?", 1)
    .await?;

Implementations

impl<M: EmbeddingModel, C: Send + Sync> MongoDbVectorIndex<M, C>

pub async fn new( collection: Collection<C>, model: M, index_name: &str, search_params: SearchParams, ) -> Result<Self, VectorStoreError>

Create a new MongoDbVectorIndex.

The index (of type “vector”) must already exist for the MongoDB collection. See the MongoDB documentation for more information on creating indexes.

Trait Implementations

impl<M: EmbeddingModel + Sync + Send, C: Sync + Send> VectorStoreIndex for MongoDbVectorIndex<M, C>

async fn top_n<T: for<'a> Deserialize<'a> + Send>( &self, query: &str, n: usize, ) -> Result<Vec<(f64, String, T)>, VectorStoreError>

Implement the top_n method of the VectorStoreIndex trait for MongoDbVectorIndex.

async fn top_n_ids( &self, query: &str, n: usize, ) -> Result<Vec<(f64, String)>, VectorStoreError>

Implement the top_n_ids method of the VectorStoreIndex trait for MongoDbVectorIndex.