rig-core 0.41.0

//! Vector store abstractions for semantic search and retrieval.
//!
//! # Core Traits
//!
//! - [`VectorStoreIndex`]: Query a vector store for similar documents.
//! - [`InsertDocuments`]: Insert documents and their embeddings.
//! - [`VectorStoreIndexDyn`]: Type-erased vector queries for runtime-defined retrieval policies.
//!
//! Use [`VectorSearchRequest`] to build queries. See [`request`] for filtering.
//!
//! Types implementing [`VectorStoreIndex`] automatically implement [`PortableTool`].

pub use request::VectorSearchRequest;
use reqwest::StatusCode;
use serde::{Deserialize, Serialize};
use serde_json::{Value, json};

use crate::{
    Embed, OneOrMany,
    embeddings::{Embedding, EmbeddingError},
    tool::PortableTool,
    vector_store::request::{Filter, FilterError, SearchFilter},
    wasm_compat::{WasmBoxedFuture, WasmCompatSend, WasmCompatSync},
};

pub mod builder;
pub mod in_memory_store;
pub mod lsh;
pub mod request;

/// Errors from vector store operations.
#[derive(Debug, thiserror::Error)]
#[non_exhaustive]
pub enum VectorStoreError {
    /// Embedding generation failed while preparing a vector query or insert.
    #[error("Embedding error: {0}")]
    EmbeddingError(#[from] EmbeddingError),

    /// JSON serialization or deserialization failed.
    #[error("Json error: {0}")]
    JsonError(#[from] serde_json::Error),

    #[cfg(not(target_family = "wasm"))]
    /// Backend-specific datastore error.
    #[error("Datastore error: {0}")]
    DatastoreError(#[from] Box<dyn std::error::Error + Send + Sync + 'static>),

    /// Filter construction or translation failed.
    #[error("Filter error: {0}")]
    FilterError(#[from] FilterError),

    #[cfg(target_family = "wasm")]
    /// Backend-specific datastore error.
    #[error("Datastore error: {0}")]
    DatastoreError(#[from] Box<dyn std::error::Error + 'static>),

    /// A document was missing an ID required by the backend.
    #[error("Missing Id: {0}")]
    MissingIdError(String),

    /// HTTP request failed for an external vector store service.
    #[error("HTTP request error: {0}")]
    ReqwestError(#[from] reqwest::Error),

    /// External vector store service returned an error response.
    #[error("External call to API returned an error. Error code: {0} Message: {1}")]
    ExternalAPIError(StatusCode, String),

    /// A vector search request builder received invalid input.
    #[error("Error while building VectorSearchRequest: {0}")]
    BuilderError(String),
}

/// Trait for inserting documents and embeddings into a vector store.
pub trait InsertDocuments: WasmCompatSend + WasmCompatSync {
    /// Insert precomputed embeddings for each document.
    fn insert_documents<Doc: Serialize + Embed + WasmCompatSend>(
        &self,
        documents: Vec<(Doc, OneOrMany<Embedding>)>,
    ) -> impl std::future::Future<Output = Result<(), VectorStoreError>> + WasmCompatSend;
}

/// Trait for querying a vector store by similarity.
pub trait VectorStoreIndex: WasmCompatSend + WasmCompatSync {
    /// The filter type for this backend.
    type Filter: SearchFilter + WasmCompatSend + WasmCompatSync;

    /// Returns the top N most similar documents as `(score, id, document)` tuples.
    fn top_n<T: for<'a> Deserialize<'a> + WasmCompatSend>(
        &self,
        req: VectorSearchRequest<Self::Filter>,
    ) -> impl std::future::Future<Output = Result<Vec<(f64, String, T)>, VectorStoreError>>
    + WasmCompatSend;

    /// Returns the top N most similar document IDs as `(score, id)` tuples.
    fn top_n_ids(
        &self,
        req: VectorSearchRequest<Self::Filter>,
    ) -> impl std::future::Future<Output = Result<Vec<(f64, String)>, VectorStoreError>> + WasmCompatSend;
}

/// Type-erased `top_n` result: `(score, id, document)` tuples as JSON values.
pub type TopNResults = Result<Vec<(f64, String, Value)>, VectorStoreError>;

/// Type-erased [`VectorStoreIndex`] for dynamic dispatch.
pub trait VectorStoreIndexDyn: WasmCompatSend + WasmCompatSync {
    /// Returns the top N documents for a JSON-serializable request.
    fn top_n<'a>(
        &'a self,
        req: VectorSearchRequest<Filter<serde_json::Value>>,
    ) -> WasmBoxedFuture<'a, TopNResults>;

    /// Returns only the top N document IDs for a JSON-serializable request.
    fn top_n_ids<'a>(
        &'a self,
        req: VectorSearchRequest<Filter<serde_json::Value>>,
    ) -> WasmBoxedFuture<'a, Result<Vec<(f64, String)>, VectorStoreError>>;
}

impl<I: VectorStoreIndex<Filter = F>, F> VectorStoreIndexDyn for I
where
    F: std::fmt::Debug
        + Clone
        + SearchFilter<Value = serde_json::Value>
        + WasmCompatSend
        + WasmCompatSync
        + Serialize
        + for<'de> Deserialize<'de>
        + 'static,
{
    fn top_n<'a>(
        &'a self,
        req: VectorSearchRequest<Filter<serde_json::Value>>,
    ) -> WasmBoxedFuture<'a, TopNResults> {
        let req = req.map_filter(Filter::interpret);

        Box::pin(async move {
            Ok(self
                .top_n::<serde_json::Value>(req)
                .await?
                .into_iter()
                .map(|(score, id, doc)| (score, id, prune_document(doc).unwrap_or_default()))
                .collect::<Vec<_>>())
        })
    }

    fn top_n_ids<'a>(
        &'a self,
        req: VectorSearchRequest<Filter<serde_json::Value>>,
    ) -> WasmBoxedFuture<'a, Result<Vec<(f64, String)>, VectorStoreError>> {
        let req = req.map_filter(Filter::interpret);

        Box::pin(self.top_n_ids(req))
    }
}

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),
    }
}

/// The output of vector store queries invoked via [`PortableTool`]
#[derive(Serialize, Deserialize, Debug)]
pub struct VectorStoreOutput {
    /// Similarity score returned by the vector store.
    pub score: f64,
    /// Document ID returned by the vector store.
    pub id: String,
    /// Serialized document payload.
    pub document: Value,
}

impl<T, F> PortableTool for T
where
    F: SearchFilter<Value = serde_json::Value>
        + WasmCompatSend
        + WasmCompatSync
        + for<'de> Deserialize<'de>,
    T: VectorStoreIndex<Filter = F>,
{
    const NAME: &'static str = "search_vector_store";
    type Error = VectorStoreError;
    type Args = VectorSearchRequest<F>;
    type Output = Vec<VectorStoreOutput>;

    fn description(&self) -> String {
        "Retrieves the most relevant documents from a vector store based on a query.".to_string()
    }

    fn parameters(&self) -> serde_json::Value {
        json!({
            "type": "object",
            "properties": {
                "query": {
                    "type": "string",
                    "description": "The query string to search for relevant documents in the vector store."
                },
                "samples": {
                    "type": "integer",
                    "description": "The maximum number of samples / documents to retrieve.",
                    "default": 5,
                    "minimum": 1
                },
                "threshold": {
                    "type": "number",
                    "description": "Similarity search threshold. If present, any result with a distance less than this may be omitted from the final result."
                }
            },
            "required": ["query", "samples"]
        })
    }

    async fn call(&self, args: Self::Args) -> Result<Self::Output, Self::Error> {
        let results = self.top_n(args).await?;
        Ok(results
            .into_iter()
            .map(|(score, id, document)| VectorStoreOutput {
                score,
                id,
                document,
            })
            .collect())
    }
}

/// Index strategy for the super::InMemoryVectorStore
#[derive(Clone, Debug, Default)]
pub enum IndexStrategy {
    /// Checks all documents in the vector store to find the most relevant documents.
    #[default]
    BruteForce,

    /// Uses LSH to find candidates then computes exact distances.
    LSH {
        /// Number of tables to use for LSH.
        num_tables: usize,
        /// Number of hyperplanes to use for LSH.
        num_hyperplanes: usize,
    },
}

#[cfg(test)]
mod tests {
    use std::sync::{Arc, Mutex};

    use super::*;
    use crate::vector_store::request::Filter;

    struct TestIndex {
        queries: Arc<Mutex<Vec<String>>>,
    }

    impl VectorStoreIndex for TestIndex {
        type Filter = Filter<serde_json::Value>;

        async fn top_n<T: for<'a> Deserialize<'a> + WasmCompatSend>(
            &self,
            req: VectorSearchRequest,
        ) -> Result<Vec<(f64, String, T)>, VectorStoreError> {
            self.queries
                .lock()
                .expect("query recorder lock")
                .push(req.query().to_string());
            let document = serde_json::from_value(json!({ "answer": 42 }))?;
            Ok(vec![(0.9, "doc-1".to_string(), document)])
        }

        async fn top_n_ids(
            &self,
            _req: VectorSearchRequest,
        ) -> Result<Vec<(f64, String)>, VectorStoreError> {
            Ok(vec![(0.9, "doc-1".to_string())])
        }
    }

    #[tokio::test]
    async fn vector_store_index_remains_a_tool() {
        let queries = Arc::new(Mutex::new(Vec::new()));
        let index = TestIndex {
            queries: queries.clone(),
        };
        let request = VectorSearchRequest::builder()
            .query("answer")
            .samples(1)
            .build();
        let output = <TestIndex as PortableTool>::call(&index, request)
            .await
            .expect("vector tool call should succeed");

        assert_eq!(<TestIndex as PortableTool>::NAME, "search_vector_store");
        assert_eq!(
            *queries.lock().expect("query recorder lock"),
            vec!["answer"]
        );
        assert_eq!(output.len(), 1);
        let result = output.first().expect("one vector result");
        assert_eq!(result.score, 0.9);
        assert_eq!(result.id, "doc-1");
        assert_eq!(result.document, json!({ "answer": 42 }));
    }
}