The Chroma Loader returns a set of texts corresponding to embeddings retrieved from a Chroma Index. The user initializes the loader with a Qdrant index. They then pass in a query vector.
Here's an example usage of the ChromaReader.
from llama_index.readers.chroma import ChromaReader
from llama_index.indices import GPTListIndex
# The chroma reader loads data from a persisted Chroma collection.
# This requires a collection name and a persist directory.
reader = ChromaReader(
collection_name="chroma_collection",
persist_directory="examples/data_connectors/chroma_collection"
)
query_vector=[n1, n2, n3, ...]
documents = reader.load_data(collection_name="demo", query_vector=query_vector, limit=5)
index = GPTListIndex(documents)
response = index.query("<query_text>")
display(Markdown(f"<b>{response}</b>"))This loader is designed to be used as a way to load data into LlamaIndex and/or subsequently used as a Tool in a LangChain Agent. See here for examples.