# chunk.RetrievedChunk


A chunk returned from a retrieval operation with associated metrics.


Usage

``` python
chunk.RetrievedChunk(
    text,
    start_index,
    end_index,
    char_count,
    context=None,
    origin=None,
    attributes=None,
    metrics=list(),
    chunk_ids=list()
)
```


`store.retrieve()` returns [RetrievedChunk](chunk.RetrievedChunk.md#raghilda.chunk.RetrievedChunk) objects rather than plain [Chunk](chunk.Chunk.md#raghilda.chunk.Chunk)s. Each one is an ordinary [Chunk](chunk.Chunk.md#raghilda.chunk.Chunk) (text, position, context, origin, attributes) extended with the scores that explain *why* it was returned, so you can rank, threshold, or display results and still trace each passage back to its source.

In addition to the inherited [Chunk](chunk.Chunk.md#raghilda.chunk.Chunk) fields, [RetrievedChunk](chunk.RetrievedChunk.md#raghilda.chunk.RetrievedChunk) adds:


## Parameters


`metrics: list[Metric] = list()`  
Retrieval scores for this chunk, as a list of [Metric](chunk.Metric.md#raghilda.chunk.Metric) objects. With the default hybrid retrieval a chunk may carry several (for example a vector similarity score and a BM25 score); higher values indicate a better match.

`chunk_ids: list[int] = list()`  
Backend chunk identifiers represented by this retrieved chunk. A normal result contains a single id; a deoverlapped result that merged several adjacent chunks lists all of their ids.


## Examples

Construct one directly to see its shape (in practice `store.retrieve()` builds these for you). Here we attach two scores and then read them back:


``` python
from raghilda.chunk import RetrievedChunk, Metric

chunk = RetrievedChunk(
    text="This is relevant content.",
    start_index=0,
    end_index=25,
    char_count=25,
    metrics=[
        Metric(name="similarity", value=0.92),
        Metric(name="bm25_score", value=15.3),
    ],
)

# Each Metric carries a name and a numeric score
for metric in chunk.metrics:
    print(f"{metric.name}: {metric.value}")
```


    similarity: 0.92
    bm25_score: 15.3