Retrieval API Reference

API reference for all retrieval classes, protocols, and utilities. For guides, see Retrieval and Advanced Retrieval.

Protocols

class Retriever(Protocol):
    def retrieve(self, query: QueryBundle, top_k: int = 10) -> list[ContextItem]: ...

class AsyncRetriever(Protocol):
    async def aretrieve(self, query: QueryBundle, top_k: int = 10) -> list[ContextItem]: ...

class Reranker(Protocol):
    def rerank(self, query: QueryBundle, items: list[ContextItem], top_k: int = 10) -> list[ContextItem]: ...

class AsyncReranker(Protocol):
    async def arerank(self, query: QueryBundle, items: list[ContextItem], top_k: int = 10) -> list[ContextItem]: ...

class TokenLevelEncoder(Protocol):
    def encode_tokens(self, text: str) -> list[list[float]]: ...

Core Retrievers

DenseRetriever

Embedding-based retrieval via a VectorStore backend.

DenseRetriever(
    vector_store: VectorStore,
    context_store: ContextStore,
    embed_fn: Callable[[str], list[float]] | None = None,
    tokenizer: Tokenizer | None = None,
)

Parameter	Type	Default	Description
`vector_store`	`VectorStore`	--	Embedding storage and search backend.
`context_store`	`ContextStore`	--	Resolves item IDs to `ContextItem`.
`embed_fn`	`Callable[[str], list[float]] \| None`	`None`	Text embedding function. Required for `index()`.
`tokenizer`	`Tokenizer \| None`	`None`	Token counter. Defaults to built-in.

Method	Signature	Description
`index`	`(items: list[ContextItem]) -> int`	Index items. Returns count. Raises `RetrieverError` if `embed_fn` is `None`.
`retrieve`	`(query: QueryBundle, top_k: int = 10) -> list[ContextItem]`	Retrieve by similarity. Uses `query.embedding` if set, else calls `embed_fn`.

SparseRetriever

BM25-based retrieval. Requires rank-bm25.

SparseRetriever(
    tokenize_fn: Callable[[str], list[str]] | None = None,
    tokenizer: Tokenizer | None = None,
)

Method	Signature	Description
`index`	`(items: list[ContextItem]) -> int`	Build BM25 index. Raises `RetrieverError` if `rank-bm25` missing.
`retrieve`	`(query: QueryBundle, top_k: int = 10) -> list[ContextItem]`	Retrieve by BM25 score (normalized to `[0, 1]`).

HybridRetriever

Combines multiple retrievers with Reciprocal Rank Fusion.

HybridRetriever(
    retrievers: list[Retriever],
    rrf_k: int = 60,
    weights: list[float] | None = None,
)

Method	Signature	Description
`retrieve`	`(query: QueryBundle, top_k: int = 10) -> list[ContextItem]`	Fuse results with RRF. Skips failed sub-retrievers. Raises `RetrieverError` if all fail.

Rerankers

ScoreReranker

Simple reranker with process() interface (not the Reranker protocol).

ScoreReranker(score_fn: Callable[[str, str], float], top_k: int | None = None)

Method	Signature	Description
`process`	`(items: list[ContextItem], query: QueryBundle \| None = None) -> list[ContextItem]`	Rescore and sort. Returns input unchanged if `query` is `None`.

CrossEncoderReranker

Cross-encoder scoring. Implements Reranker.

CrossEncoderReranker(score_fn: Callable[[str, str], float], top_k: int = 10)

Method	Signature	Description
`rerank`	`(query: QueryBundle, items: list[ContextItem], top_k: int = 10) -> list[ContextItem]`	Score each item and return top-k.

CohereReranker

Batch reranking via API callback. Implements Reranker.

CohereReranker(
    rerank_fn: Callable[[str, list[str], int], list[tuple[int, float]]],
    top_k: int = 10,
)

The rerank_fn takes (query, documents, top_k) and returns (index, score) tuples.

Method	Signature	Description
`rerank`	`(query: QueryBundle, items: list[ContextItem], top_k: int = 10) -> list[ContextItem]`	Batch-rerank via callback.

FlashRankReranker

Local reranking via flashrank. Model is lazily loaded.

FlashRankReranker(model_name: str = "ms-marco-MiniLM-L-12-v2", top_k: int = 10)

Method	Signature	Description
`rerank`	`(query: QueryBundle, items: list[ContextItem], top_k: int = 10) -> list[ContextItem]`	Rerank using flashrank. Raises `RetrieverError` if not installed.

RoundRobinReranker

Round-robin merge and score-based re-sort.

RoundRobinReranker(top_k: int = 10)

Method	Signature	Description
`rerank`	`(query: QueryBundle, items: list[ContextItem], top_k: int = 10) -> list[ContextItem]`	Re-sort by existing score descending.
`rerank_multiple`	`(query: QueryBundle, result_sets: list[list[ContextItem]], top_k: int \| None = None) -> list[ContextItem]`	Merge result sets round-robin, deduplicating by ID.

RerankerPipeline

Chains multiple rerankers sequentially. Final top_k applied at the end.

RerankerPipeline(rerankers: list[Reranker], top_k: int = 10)

Method	Signature	Description
`rerank`	`(query: QueryBundle, items: list[ContextItem], top_k: int = 10) -> list[ContextItem]`	Pass items through each reranker in sequence.

Async Retrievers

AsyncDenseRetriever

Async embedding-based retriever with cosine similarity.

AsyncDenseRetriever(
    embed_fn: Callable[[str], Awaitable[list[float]]],
    similarity_fn: Callable[[list[float], list[float]], float] | None = None,
)

Method	Signature	Description
`index`	`(items: list[ContextItem]) -> None`	Store pre-embedded items (need `"embedding"` in metadata).
`aindex`	`async (items: list[ContextItem]) -> None`	Embed and store items via `embed_fn`.
`aretrieve`	`async (query: QueryBundle, top_k: int = 10) -> list[ContextItem]`	Retrieve by cosine similarity.

AsyncHybridRetriever

Concurrent fan-out with RRF fusion via asyncio.gather.

AsyncHybridRetriever(
    retrievers: list[AsyncDenseRetriever],
    weights: list[float] | None = None,
    k: int = 60,
)

Method	Signature	Description
`aretrieve`	`async (query: QueryBundle, top_k: int = 10) -> list[ContextItem]`	Fan out to all retrievers and fuse with RRF.

AsyncCrossEncoderReranker

Async cross-encoder scoring. Scores all items concurrently.

AsyncCrossEncoderReranker(score_fn: Callable[[str, str], Awaitable[float]])

Method	Signature	Description
`arerank`	`async (query: QueryBundle, items: list[ContextItem], top_k: int = 10) -> list[ContextItem]`	Score concurrently and return top-k.

AsyncCohereReranker

Async batch reranker via callback.

AsyncCohereReranker(rerank_fn: Callable[[str, list[str], int], Awaitable[list[int]]])

Method	Signature	Description
`arerank`	`async (query: QueryBundle, items: list[ContextItem], top_k: int = 10) -> list[ContextItem]`	Batch-rerank via async callback.

Query Routing

KeywordRouter

Routes queries by keyword matching. First match wins.

KeywordRouter(routes: dict[str, list[str]], default: str, *, case_sensitive: bool = False)

Method	Signature	Description
`route`	`(query: QueryBundle) -> str`	Return route name for first keyword match, or `default`.

CallbackRouter

Routes using a user-provided callback.

CallbackRouter(callback: Callable[[QueryBundle], str | None], default: str = "default")

Method	Signature	Description
`route`	`(query: QueryBundle) -> str`	Call callback. Returns `default` if callback returns `None`.

MetadataRouter

Routes based on a metadata field in the query.

MetadataRouter(metadata_key: str = "route", default: str = "default")

Method	Signature	Description
`route`	`(query: QueryBundle) -> str`	Read `query.metadata[metadata_key]`. Returns `default` if missing.

RoutedRetriever

Delegates to named retrievers based on routing.

RoutedRetriever(
    router: KeywordRouter | CallbackRouter | MetadataRouter,
    retrievers: dict[str, Retriever],
    default_retriever: str | None = None,
)

Method	Signature	Description
`retrieve`	`(query: QueryBundle, top_k: int = 10) -> list[ContextItem]`	Route and delegate. Raises `RetrieverError` if route unknown and no default.

CrossModalEncoder

Encodes content from multiple modalities into a shared vector space.

CrossModalEncoder(encoders: dict[str, Callable[[Any], list[float]]])

Member	Signature	Description
`encode`	`(content: Any, modality: str) -> list[float]`	Encode using named modality. Raises `ValueError` if unknown.
`modalities`	`@property -> list[str]`	Sorted list of registered modality names.

SharedSpaceRetriever

Cross-modal retriever in a shared embedding space.

SharedSpaceRetriever(
    encoder: CrossModalEncoder,
    query_modality: str = "text",
    similarity_fn: Callable[[list[float], list[float]], float] | None = None,
)

Method	Signature	Description
`index`	`(items: list[ContextItem], modality: str \| None = None) -> None`	Embed and store items. Uses `metadata["modality"]` if `modality` is `None`.
`retrieve`	`(query: QueryBundle, top_k: int = 10) -> list[ContextItem]`	Retrieve by similarity in shared space.

Late Interaction

MaxSimScorer

ColBERT-style MaxSim scoring over per-token embeddings.

MaxSimScorer()

Method	Signature	Description
`score`	`(query_tokens: list[list[float]], doc_tokens: list[list[float]]) -> float`	Sum of per-query-token maximum cosine similarities.

LateInteractionScorer

Configurable wrapper for token-level scoring. Defaults to MaxSim.

LateInteractionScorer(
    score_fn: Callable[[list[list[float]], list[list[float]]], float] | None = None,
)

Method	Signature	Description
`score`	`(query_tokens: list[list[float]], doc_tokens: list[list[float]]) -> float`	Delegate to configured scoring function.

LateInteractionRetriever

Two-stage retriever: first-stage candidate generation + token-level re-scoring.

LateInteractionRetriever(
    first_stage: Retriever,
    encoder: TokenLevelEncoder,
    scorer: LateInteractionScorer | None = None,
    first_stage_k: int = 100,
)

Method	Signature	Description
`retrieve`	`(query: QueryBundle, top_k: int = 10) -> list[ContextItem]`	Generate candidates then re-score with token-level similarity.

Memory Retrieval

ScoredMemoryRetriever

Multi-signal retriever combining recency, relevance, and importance.

ScoredMemoryRetriever(
    store: MemoryEntryStore,
    embed_fn: Callable[[str], list[float]] | None = None,
    vector_store: VectorStore | None = None,
    decay: MemoryDecay | None = None,
    alpha: float = 0.3,
    beta: float = 0.5,
    gamma: float = 0.2,
)

Parameter	Type	Default	Description
`store`	`MemoryEntryStore`	--	Backing store for entries.
`embed_fn`	`Callable \| None`	`None`	Embedding function for relevance.
`vector_store`	`VectorStore \| None`	`None`	Vector index for relevance scoring.
`decay`	`MemoryDecay \| None`	`None`	Custom decay. Defaults to 7-day half-life.
`alpha` / `beta` / `gamma`	`float`	`0.3` / `0.5` / `0.2`	Recency / relevance / importance weights.

Method	Signature	Description
`retrieve`	`(query: str, top_k: int = 5, *, user_id: str \| None, memory_type: str \| None) -> list[MemoryEntry]`	Retrieve by composite score with optional filters.
`add_entry`	`(entry: MemoryEntry) -> None`	Add entry and optionally index embedding.
`as_retriever`	`() -> MemoryRetrieverAdapter`	Return `Retriever`-protocol adapter for pipeline use.

MemoryRetrieverAdapter

Bridges ScoredMemoryRetriever to the Retriever protocol.

MemoryRetrieverAdapter(retriever: ScoredMemoryRetriever)

Method	Signature	Description
`retrieve`	`(query: QueryBundle, top_k: int = 10) -> list[ContextItem]`	Convert `MemoryEntry` to `ContextItem` with `source=MEMORY`, `priority=7`.

Utility Functions

rrf_fuse

Standalone Reciprocal Rank Fusion for combining ranked lists.

def rrf_fuse(
    ranked_lists: list[list[ContextItem]],
    weights: list[float] | None = None,
    k: int = 60,
    top_k: int | None = None,
) -> list[ContextItem]: ...

Parameter	Type	Default	Description
`ranked_lists`	`list[list[ContextItem]]`	--	Ranked item lists.
`weights`	`list[float] \| None`	`None`	Per-list weights. Defaults to `1.0`.
`k`	`int`	`60`	RRF smoothing constant.
`top_k`	`int \| None`	`None`	Max items to return. `None` returns all.

Returns fused items with normalized scores and retrieval_method: "rrf" in metadata.

Retrieval API Reference

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