class MilvusRetriever( client: pymilvus.MilvusClient, embedder: langchain_core.embeddings.Embeddings, content_field: str = 'text', use_iterator: bool = False, )#

Bases: aiq.retriever.interface.AIQRetriever

Client for retrieving document chunks from a Milvus vectorstore

Initialize the Milvus Retriever using a preconfigured MilvusClient

Args:: client (MilvusClient): Preinstantiate pymilvus.MilvusClient object.

_client#

_embedder#

_search_func#

_default_params = None#

_bound_params = []#

content_field = 'text'#

bind(**kwargs) → None#

Bind default values to the search method. Cannot bind the ‘query’ parameter.

Args:: kwargs (dict): Key value pairs corresponding to the default values of search parameters.

get_unbound_params() → list[str]#: Returns a list of unbound parameters which will need to be passed to the search function.

_validate_collection(collection_name: str) → bool#

async search(query: str, **kwargs)#: Retireve max(top_k) items from the data store based on vector similarity search (implementation dependent).

async _search_with_iterator(

query: str,

*,

collection_name: str,

top_k: int,

filters: str | None = None,

output_fields: list[str] | None = None,

search_params: dict | None = None,

timeout: float | None = None,

vector_field_name: str | None = 'vector',

distance_cutoff: float | None = None,

**kwargs,

)#: Retrieve document chunks from a Milvus vectorstore using a search iterator, allowing for the retrieval of more results.

async _search(

query: str,

*,

collection_name: str,

top_k: int,

filters: str | None = None,

output_fields: list[str] | None = None,

search_params: dict | None = None,

timeout: float | None = None,

vector_field_name: str | None = 'vector',

**kwargs,

)#: Retrieve document chunks from a Milvus vectorstore

_wrap_milvus_results( res: list[pymilvus.client.abstract.Hit], content_field: str, )#

_wrap_milvus_single_results( res: pymilvus.client.abstract.Hit | dict, content_field: str, ) → aiq.retriever.models.AIQDocument#