Skip to content

MilvusRM

MilvusRM uses OpenAI's text-embedding-3-small embedding by default or any customized embedding function. To support passage retrieval, it assumes that a Milvus collection has been created and populated with the following field:

  • text: The text of the passage

Set up the MilvusRM Client

The constructor initializes an instance of the MilvusRM class, with the option to use OpenAI's text-embedding-3-small embeddings or any customized embedding function .

  • collection_name (str): The name of the Milvus collection to query against.
  • uri (str, optional): The Milvus connection uri. Defaults to "http://localhost:19530".
  • token (str, optional): The Milvus connection token. Defaults to None.
  • db_name (str, optional): The Milvus database name. Defaults to "default".
  • embedding_function (callable, optional): The function to convert a list of text to embeddings. The embedding function should take a list of text strings as input and output a list of embeddings. Defaults to None. By default, it will get OpenAI client by the environment variable OPENAI_API_KEY and use OpenAI's embedding model "text-embedding-3-small" with the default dimension.
  • k (int, optional): The number of top passages to retrieve. Defaults to 3.

Example of the MilvusRM constructor: 

MilvusRM(
    collection_name: str,
    uri: Optional[str] = "http://localhost:19530",
    token: Optional[str] = None,
    db_name: Optional[str] = "default",
    embedding_function: Optional[Callable] = None,
    k: int = 3,
)

Under the Hood

forward(self, query_or_queries: Union[str, List[str]], k: Optional[int] = None) -> dspy.Prediction

Parameters: - query_or_queries (Union[str, List[str]]): The query or list of queries to search for. - k (Optional[int], optional): The number of results to retrieve. If not specified, defaults to the value set during initialization.

Returns: - dspy.Prediction: Contains the retrieved passages, each represented as a dotdict with a long_text attribute.

Search the Milvus collection for the top k passages matching the given query or queries, using embeddings generated via the default OpenAI embedding or the specified embedding_function.

Sending Retrieval Requests via MilvusRM Client

from dspy.retrieve.milvus_rm import MilvusRM
import os

os.environ["OPENAI_API_KEY"] = "<YOUR_OPENAI_API_KEY>"

retriever_model = MilvusRM(
    collection_name="<YOUR_COLLECTION_NAME>",
    uri="<YOUR_MILVUS_URI>",
    token="<YOUR_MILVUS_TOKEN>"  # ignore this if no token is required for Milvus connection
    )

results = retriever_model("Explore the significance of quantum computing", k=5)

for result in results:
    print("Document:", result.long_text, "\n")