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retrieve.ChromadbRM

Constructor​

Initialize an instance of the ChromadbRM class, with the option to use OpenAI's embeddings or any alternative supported by chromadb, as detailed in the official chromadb embeddings documentation.

ChromadbRM(
    collection_name: str,
    persist_directory: str,
    embedding_function: Optional[EmbeddingFunction[Embeddable]] = OpenAIEmbeddingFunction(),
    k: int = 7,
)

Parameters:

  • collection_name (str): The name of the chromadb collection.
  • persist_directory (str): Path to the directory where chromadb data is persisted.
  • embedding_function (Optional[EmbeddingFunction[Embeddable]], optional): The function used for embedding documents and queries. Defaults to DefaultEmbeddingFunction() if not specified.
  • k (int, optional): The number of top passages to retrieve. Defaults to 7.

Methods​

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

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

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 schema [{"id": str, "score": float, "long_text": str, "metadatas": dict }]

Quickstart with OpenAI Embeddings​

ChromadbRM have the flexibility from a variety of embedding functions as outlined in the chromadb embeddings documentation. While different options are available, this example demonstrates how to utilize OpenAI embeddings specifically.

from dspy.retrieve.chromadb_rm import ChromadbRM
import os
import openai
from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction

embedding_function = OpenAIEmbeddingFunction(
    api_key=os.environ.get('OPENAI_API_KEY'),
    model_name="text-embedding-ada-002"
)

retriever_model = ChromadbRM(
    'your_collection_name',
    '/path/to/your/db',
    embedding_function=embedding_function,
    k=5
)

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

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