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Weaviate Retrieval Model

Weaviate is an open-source vector database that can be used to retrieve relevant passages before passing it to the language model. Weaviate supports a variety of embedding models from OpenAI, Cohere, Google and more! Before building your DSPy program, you will need a Weaviate cluster running with data. You can follow this notebook as an example.

Configuring the Weaviate Client

Weaviate is available via a hosted service (WCD) or as a self managed instance. You can learn about the different installation methods here.

  • weaviate_collection_name (str): The name of the Weaviate collection
  • weaviate_client (WeaviateClient): An instance of the Weaviate client
  • k (int, optional): The number of top passages to retrieve. The default is set to 3

An example of the WeaviateRM constructor: 

WeaviateRM(
    collection_name: str
    weaviate_client: str,
    k: int = 5
)

Under the Hood

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

Parameters

  • query_or_queries (Union[str, List[str]]): The query or queries to search for
  • k (Optional[int]): The number of top passages to retrieve. It defaults toself.k`
  • **kwargs: Additional keyword arguments like rerank for example

Returns

  • dspy.Prediction: An object containing the retrieved passages

Sending Retrieval Requests via the WeaviateRM Client

Here is an example of the Weaviate constructor using embedded:

import weaviate
import dspy
from dspy.retrieve.weaviate_rm import WeaviateRM

weaviate_client = weaviate.connect_to_embedded() # you can also use local or WCD

retriever_model = WeaviateRM(
    collection_name="<WEAVIATE_COLLECTION>",
    weaviate_client=weaviate_client 
)

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

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

You can follow along with more DSPy and Weaviate examples here!