AgenticEnsembleRetriever

Retriever

DashAI.back.models.RAG.retrievers.composite.AgenticEnsembleRetriever

---

Consults LLM-chosen children across rounds to build a sufficient pool.

A planning evaluation over an empty pool selects the first round's children. Each round consults the chosen children at the explicit per-child top_k supplied by the model, merges the results and — except for the final round — judges the pool. An insufficient verdict selects the next round's children.

Parameters

enable_retrieve_then_expand : boolean, default=False

Widen each retrieved chunk with its neighbouring chunks before the chunks are returned as the retriever's context.

expand_window : integer, default=3

Total number of chunks in the expanded window, the retrieved one included. Odd values are centred exactly on the retrieved chunk; even values keep the extra chunk after it. Windows never leave the document, and overlapping windows merge into one concatenated chunk.

children : array, default=[{'component': 'DenseEmbeddingRetriever', 'params': {'embedding_model': {'component': 'ParaphraseMultilingualMiniLML12V2Embedding', 'params': {'batch_size': 32, 'device': 'CPU', 'normalize': True, 'overflow_strategy': 'truncate'}}, 'similarity_metric': 'cosine', 'top_k': 10}}, {'component': 'BM25Retriever', 'params': {'b': 0.75, 'delta': 0.0, 'k1': 1.5, 'similarity_function': 'cosine', 'top_k': 10, 'vectorizer': {'component': 'BagOfWordsConverter', 'params': {'analyzer': 'word', 'custom_stop_words': None, 'drop_source_columns': False, 'lower_bound_ngrams': 1, 'lowercase': True, 'max_df': 1.0, 'max_features': None, 'min_df': 0.0, 'stop_words': None, 'strip_accents': None, 'upper_bound_ngrams': 1}}}}]

Child retrievers the LLM may consult. A catalog of their indices, names and top_k values is rendered into the prompt. Defaults to the default semantic and keyword retrievers; you can change them.

max_chunks : integer, default=10

Maximum number of chunks the accumulated pool may hold.

max_iters : integer, default=3

Maximum number of retrieval rounds. One planning call plus at most this many minus one judgement calls.

function_call_max_trials : integer, default=3

Maximum number of function-calling evaluation attempts, including the first. When the verdict is unusable or has no valid operation left, the evaluation is retried until this limit is reached.

prompt : object

The prompt used to choose children and judge sufficiency.

llm_model : object

The language model that chooses children and judges sufficiency. Defaults to the default RAG function-calling LLM; you can change it.

Methods

retrieve(self, query: 'str', **kwargs: 'Any') -> 'Tuple[List[Chunk], Dict[str, Any]]'

Defined on AgenticEnsembleRetriever

Retrieve by consulting LLM-chosen children across rounds.

Parameters

query : str

The search query string.

**kwargs

Unused; children choices and sizes come from the LLM verdicts.

Returns

Tuple[List[Chunk], Dict[str, Any]]

The final pool (at most max_chunks chunks) and this retriever's trace node, whose children holds every consulted child node and whose rounds holds one summary per retrieval round.

score_chunks(self, chunk_ids: 'List[int]', query: 'str') -> 'List[Tuple[int, float]]'

Defined on AgenticEnsembleRetriever

Aggregate chunk scores across all children by averaging.

Parameters

chunk_ids : List[int]

List of chunk IDs to score.

query : str

The search query string.

Returns

List[Tuple[int, float]]

A list of (chunk_id, average_distance) tuples sorted by distance.

add(self, child: DashAI.back.models.RAG.retrievers.base_retriever.BaseRetriever) -> None

Defined on CompositeRetriever

Add a child retriever.

close(self) -> 'None'

Defined on AgenticRetriever

Release the evaluation LLM and nested agentic children.

Returns

None

Nothing.

expand_retrieved_chunks(self, chunks: List[DashAI.back.models.RAG.documents.chunk.Chunk], window_size: int) -> List[DashAI.back.models.RAG.documents.chunk.Chunk]

Defined on BaseRetriever

Expand chunks into centred windows and merge overlapping windows.

get_children(self) -> List[DashAI.back.models.RAG.retrievers.base_retriever.BaseRetriever]

Defined on CompositeRetriever

Return a copy of the children list.

get_credential(self, name: str)

Defined on ConfigObject

Resolve a registered credential component by name.

Parameters

name : str

Credential component class name (e.g. "HuggingFaceCredential").

Returns

BaseCredential

An instance of the requested credential component.

get_id(self) -> int | None

Defined on BaseRetriever

Return the database ID of this retriever, or None.

get_metadata(cls) -> Dict[str, Any]

Defined on BaseRetriever

Return the UI metadata shared by every retriever.

Returns

Dict[str, Any]

Dictionary with the icon shown for the retriever in the frontend and, when declared, its representation template and the category the retriever pickers group it under.

get_representation(cls) -> DashAI.back.core.utils.MultilingualString | None

Defined on BaseRetriever

Return the retriever's representation template, if any.

Returns

MultilingualString | None

The template with {param} / {nested.param} placeholders, or None when the frontend should fall back to the component description.

get_schema(cls) -> dict

Defined on ConfigObject

Generates the component related Json Schema.

Returns

dict

Dictionary representing the Json Schema of the component.

get_schema_summary(self) -> DashAI.back.models.RAG.retrievers.retrieval_contracts.SchemaSummary

Defined on BaseRetriever

Return a recursive, JSON-serializable summary of this retriever.

Returns

SchemaSummary

{"class": ..., "params": ...} plus children when present.

get_schema_summary_parameters(cls) -> Dict[str, str]

Defined on BaseRetriever

Return a copy of the summary parameter mapping for the frontend.

init_model(self) -> None

Defined on BaseRetriever

Called by the factory after inject_infra().

inject_infra(self, env_RAG_path: str | os.PathLike, chunks: Dict[int, Dict[int, DashAI.back.models.RAG.documents.chunk.Chunk]], persistence: Any, documents: Optional[Dict[int, DashAI.back.models.RAG.documents.source_document.SourceDocument]] = None) -> None

Defined on BaseRetriever

Inject runtime infrastructure after schema validation.

load(self, filename: str = '') -> None

Defined on BaseRetriever

Restore the retriever's state from disk.

merge_expanded_chunks(self, chunks: List[DashAI.back.models.RAG.documents.chunk.Chunk]) -> List[DashAI.back.models.RAG.documents.chunk.Chunk]

Defined on BaseRetriever

Merge overlapping expanded units without widening plain chunks.

remove(self, child: DashAI.back.models.RAG.retrievers.base_retriever.BaseRetriever) -> None

Defined on CompositeRetriever

Remove a child retriever.

save(self, filename: str = '') -> None

Defined on BaseRetriever

Persist the retriever's state to disk.

set_id(self, id: int) -> None

Defined on BaseRetriever

Assign a database ID to this retriever.

set_trace_params(self, params: Dict[str, Any]) -> None

Defined on BaseRetriever

Store a JSON-serializable snapshot of this retriever's configuration.

Parameters

params : Dict[str, Any]

Raw construction parameters captured by the factory before any nested component reference is consumed. A deep copy is stored so later mutations of the original mapping cannot affect trace rendering.

validate_and_transform(self, raw_data: dict) -> dict

Defined on ConfigObject

It takes the data given by the user to initialize the model and returns it with all the objects that the model needs to work.

Parameters

raw_data : dict

A dictionary with the data provided by the user to initialize the model.

Returns

dict

A validated dictionary with the necessary objects.