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ChromadbRM

Note

Adapted from documentation provided by https://github.com/animtel

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.

Setting up the ChromadbRM Client

The constructor initializes 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.

  • 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.

Example of the ChromadbRM constructor: 

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

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

Sending Retrieval Requests via ChromadbRM Client

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")