retrieve_chunks

Retrieve relevant chunks from DataBridge

retrieve_chunks

Retrieve relevant chunks from DataBridge.

def retrieve_chunks(
    query: str,
    filters: Optional[Dict[str, Any]] = None,
    k: int = 4,
    min_score: float = 0.0,
    use_colpali: bool = True,
) -> List[FinalChunkResult]

Parameters

• query (str): Search query text
• filters (Dict[str, Any], optional): Optional metadata filters
• k (int, optional): Number of results. Defaults to 4.
• min_score (float, optional): Minimum similarity threshold. Defaults to 0.0.
• use_colpali (bool, optional): Whether to use ColPali-style embedding model to retrieve the chunks (only works for documents ingested with use_colpali=True). Defaults to True.

Returns

• List[FinalChunkResult]: List of chunk results

Example

from databridge.sync import DataBridge

db = DataBridge()

chunks = db.retrieve_chunks(
    "What are the key findings?",
    filters={"department": "research"},
    k=5,
    min_score=0.5
)

for chunk in chunks:
    print(f"Score: {chunk.score}")
    print(f"Content: {chunk.content}")
    print(f"Document ID: {chunk.document_id}")
    print(f"Chunk Number: {chunk.chunk_number}")
    print(f"Metadata: {chunk.metadata}")
    print("---")

FinalChunkResult Properties

The FinalChunkResult objects returned by this method have the following properties:

• content (str | PILImage): Chunk content (text or image)
• score (float): Relevance score
• document_id (str): Parent document ID
• chunk_number (int): Chunk sequence number
• metadata (Dict[str, Any]): Document metadata
• content_type (str): Content type
• filename (Optional[str]): Original filename
• download_url (Optional[str]): URL to download full document