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feat: implement keyword and hybrid search for Weaviate provider

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This commit is contained in:
ChristianZaccaria 2025-08-27 12:24:38 +01:00
parent a1301911e4
commit 4541b517c8
8 changed files with 476 additions and 25 deletions
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3
llama_stack/providers/utils/memory/vector_store.py
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@ -50,6 +50,7 @@ class ChunkForDeletion(BaseModel):
# Constants for reranker types
RERANKER_TYPE_RRF = "rrf"
RERANKER_TYPE_WEIGHTED = "weighted"
RERANKER_TYPE_NORMALIZED = "normalized"
def parse_pdf(data: bytes) -> str:
@ -325,6 +326,8 @@ class VectorDBWithIndex:
weights = ranker.get("params", {}).get("weights", [0.5, 0.5])
reranker_type = RERANKER_TYPE_WEIGHTED
reranker_params = {"alpha": weights[0] if len(weights) > 0 else 0.5}
elif strategy == "normalized":
reranker_type = RERANKER_TYPE_NORMALIZED
else:
reranker_type = RERANKER_TYPE_RRF
k_value = ranker.get("params", {}).get("k", 60.0)