Method vector_search_impl

nodedb-client/src/remote/client/vector.rs:20–63

(
        &self,
        collection: &str,
        query: &[f32],
        k: usize,
        filter: Option<&MetadataFilter>,
    )

impl NodeDbRemote {
    pub(super) async fn vector_search_impl(
        &self,
        collection: &str,
        query: &[f32],
        k: usize,
        filter: Option<&MetadataFilter>,
    ) -> NodeDbResult<Vec<SearchResult>> {
        let sql = build_vector_search_sql(collection, query, k, filter)?;

        let (columns, rows) = self.query_raw(&sql, &[]).await?;

        // The DSL path returns JSON in a single "result" column.
        if columns.len() == 1 && columns[0] == "result" {
            if let Some(row) = rows.first()
                && let Some(nodedb_types::value::Value::String(json_text)) = row.first()
            {
                return parse_vector_search_json(json_text);
            }
            return Ok(Vec::new());
        }

        // Structured result set: id, distance columns.
        let mut results = Vec::with_capacity(rows.len());
        let id_idx = columns.iter().position(|c| c == "id").unwrap_or(0);
        let dist_idx = columns.iter().position(|c| c == "distance").unwrap_or(1);

        for row in &rows {
            let id = row
                .get(id_idx)
                .and_then(|v| v.as_str())
                .unwrap_or("")
                .to_string();
            let distance = row.get(dist_idx).and_then(|v| v.as_f64()).unwrap_or(0.0) as f32;

            results.push(SearchResult {
                id,
                node_id: None,
                distance,
                metadata: HashMap::new(),
            });
        }

        Ok(results)
    }

    pub(super) async fn vector_insert_field_impl(
        &self,