rig/vector_store/
mod.rs

use futures::future::BoxFuture;
use serde::Deserialize;
use serde_json::Value;

use crate::embeddings::EmbeddingError;

pub mod in_memory_store;

#[derive(Debug, thiserror::Error)]
pub enum VectorStoreError {
    #[error("Embedding error: {0}")]
    EmbeddingError(#[from] EmbeddingError),

    /// Json error (e.g.: serialization, deserialization, etc.)
    #[error("Json error: {0}")]
    JsonError(#[from] serde_json::Error),

    #[error("Datastore error: {0}")]
    DatastoreError(#[from] Box<dyn std::error::Error + Send + Sync + 'static>),

    #[error("Missing Id: {0}")]
    MissingIdError(String),
}

/// Trait for vector store indexes
pub trait VectorStoreIndex: Send + Sync {
    /// Get the top n documents based on the distance to the given query.
    /// The result is a list of tuples of the form (score, id, document)
    fn top_n<T: for<'a> Deserialize<'a> + Send>(
        &self,
        query: &str,
        n: usize,
    ) -> impl std::future::Future<Output = Result<Vec<(f64, String, T)>, VectorStoreError>> + Send;

    /// Same as `top_n` but returns the document ids only.
    fn top_n_ids(
        &self,
        query: &str,
        n: usize,
    ) -> impl std::future::Future<Output = Result<Vec<(f64, String)>, VectorStoreError>> + Send;
}

pub type TopNResults = Result<Vec<(f64, String, Value)>, VectorStoreError>;

pub trait VectorStoreIndexDyn: Send + Sync {
    fn top_n<'a>(&'a self, query: &'a str, n: usize) -> BoxFuture<'a, TopNResults>;

    fn top_n_ids<'a>(
        &'a self,
        query: &'a str,
        n: usize,
    ) -> BoxFuture<'a, Result<Vec<(f64, String)>, VectorStoreError>>;
}

impl<I: VectorStoreIndex> VectorStoreIndexDyn for I {
    fn top_n<'a>(
        &'a self,
        query: &'a str,
        n: usize,
    ) -> BoxFuture<'a, Result<Vec<(f64, String, Value)>, VectorStoreError>> {
        Box::pin(async move {
            Ok(self
                .top_n::<serde_json::Value>(query, n)
                .await?
                .into_iter()
                .map(|(score, id, doc)| (score, id, prune_document(doc).unwrap_or_default()))
                .collect::<Vec<_>>())
        })
    }

    fn top_n_ids<'a>(
        &'a self,
        query: &'a str,
        n: usize,
    ) -> BoxFuture<'a, Result<Vec<(f64, String)>, VectorStoreError>> {
        Box::pin(self.top_n_ids(query, n))
    }
}

fn prune_document(document: serde_json::Value) -> Option<serde_json::Value> {
    match document {
        Value::Object(mut map) => {
            let new_map = map
                .iter_mut()
                .filter_map(|(key, value)| {
                    prune_document(value.take()).map(|value| (key.clone(), value))
                })
                .collect::<serde_json::Map<_, _>>();

            Some(Value::Object(new_map))
        }
        Value::Array(vec) if vec.len() > 400 => None,
        Value::Array(vec) => Some(Value::Array(
            vec.into_iter().filter_map(prune_document).collect(),
        )),
        Value::Number(num) => Some(Value::Number(num)),
        Value::String(s) => Some(Value::String(s)),
        Value::Bool(b) => Some(Value::Bool(b)),
        Value::Null => Some(Value::Null),
    }
}
rig/vector_store/mod.rs

rig/vector_store/
mod.rs
