raphtory 0.17.0

raphtory, a temporal graph library
Documentation 
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use super::{
    entity_db::{EdgeDb, NodeDb},
    storage::{db_path, VectorMeta},
};
use crate::{
    db::api::view::{internal::IntoDynamic, StaticGraphViewOps},
    errors::{GraphError, GraphResult},
    prelude::GraphViewOps,
    vectors::{
        cache::CachedEmbeddingModel,
        embeddings::compute_embeddings,
        entity_db::EntityDb,
        template::DocumentTemplate,
        vector_collection::{
            lancedb::LanceDb, CollectionPath, VectorCollection, VectorCollectionFactory,
        },
        vectorised_graph::VectorisedGraph,
    },
};
use async_trait::async_trait;
use std::{path::Path, sync::Arc};
use tracing::info;

#[async_trait]
pub trait Vectorisable<G: StaticGraphViewOps> {
    /// Create a VectorisedGraph from the current graph
    ///
    /// # Arguments:
    ///   * model - the embedding function to translate documents to embeddings
    ///   * template - the template to use to translate entities into documents
    ///   * verbose - whether or not to print logs reporting the progress
    ///
    /// # Returns:
    ///   A VectorisedGraph with all the documents/embeddings computed and with an initial empty selection
    async fn vectorise(
        &self,
        model: CachedEmbeddingModel,
        template: DocumentTemplate,
        path: Option<&Path>,
        verbose: bool,
    ) -> GraphResult<VectorisedGraph<G>>;
}

#[async_trait]
impl<G: StaticGraphViewOps + IntoDynamic + Send> Vectorisable<G> for G {
    async fn vectorise(
        &self,
        model: CachedEmbeddingModel,
        template: DocumentTemplate,
        path: Option<&Path>,
        verbose: bool,
    ) -> GraphResult<VectorisedGraph<G>> {
        let db_path = path
            .map(|path| Ok::<CollectionPath, std::io::Error>(Arc::new(db_path(path))))
            .unwrap_or_else(|| Ok(Arc::new(tempfile::tempdir()?)))?;
        let factory = LanceDb;
        let dim = model.dim().ok_or_else(|| GraphError::UnresolvedModel)?;
        if verbose {
            info!("computing embeddings for nodes");
        }
        let nodes = self.nodes();
        let node_docs = nodes
            .iter()
            .filter_map(|node| template.node(node).map(|doc| (node.node.0 as u64, doc)));
        let node_vectors = compute_embeddings(node_docs, &model);
        let node_db = NodeDb(
            factory
                .new_collection(db_path.clone(), "nodes", dim)
                .await?,
        );
        node_db.insert_vector_stream(node_vectors).await.unwrap();
        node_db.create_or_update_index().await?;

        if verbose {
            info!("computing embeddings for edges");
        }
        let edges = self.edges();
        let edge_docs = edges.iter().filter_map(|edge| {
            template
                .edge(edge)
                .map(|doc| (edge.edge.pid().0 as u64, doc))
        });
        let edge_vectors = compute_embeddings(edge_docs, &model);
        let edge_db = EdgeDb(factory.new_collection(db_path, "edges", dim).await?);
        edge_db.insert_vector_stream(edge_vectors).await.unwrap();
        edge_db.create_or_update_index().await?;

        if let Some(path) = path {
            let meta = VectorMeta {
                template: template.clone(),
                model: model.model.clone(),
            };
            meta.write_to_path(path)?;
        }

        Ok(VectorisedGraph {
            source_graph: self.clone(),
            template,
            model,
            node_db,
            edge_db,
        })
    }
}