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Feature: Configuring search modes for RAG - Address review

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Signed-off-by: Varsha Prasad Narsing <varshaprasad96@gmail.com>
This commit is contained in:
Varsha Prasad Narsing 2025-05-21 10:57:37 -07:00
parent 2060fdba7f
commit 6a2a0836c5
11 changed files with 11 additions and 13 deletions
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2
llama_stack/providers/inline/vector_io/faiss/faiss.py
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@ -118,7 +118,7 @@ class FaissIndex(EmbeddingIndex):
async def query_keyword(
self,
query_string: str | None,
query_string: str,
k: int,
score_threshold: float,
) -> QueryChunksResponse:

4
llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py
View file

@ -203,8 +203,6 @@ class SQLiteVecIndex(EmbeddingIndex):
"""
Performs vector-based search using a virtual table for vector similarity.
"""
if embedding is None:
raise ValueError("embedding is required for vector search.")
def _execute_query():
connection = _create_sqlite_connection(self.db_path)
@ -243,7 +241,7 @@ class SQLiteVecIndex(EmbeddingIndex):
async def query_keyword(
self,
query_string: str | None,
query_string: str,
k: int,
score_threshold: float,
) -> QueryChunksResponse:

2
llama_stack/providers/remote/vector_io/chroma/chroma.py
View file

@ -86,7 +86,7 @@ class ChromaIndex(EmbeddingIndex):
async def query_keyword(
self,
query_string: str | None,
query_string: str,
k: int,
score_threshold: float,
) -> QueryChunksResponse:

2
llama_stack/providers/remote/vector_io/milvus/milvus.py
View file

@ -88,7 +88,7 @@ class MilvusIndex(EmbeddingIndex):
async def query_keyword(
self,
query_string: str | None,
query_string: str,
k: int,
score_threshold: float,
) -> QueryChunksResponse:

2
llama_stack/providers/remote/vector_io/pgvector/pgvector.py
View file

@ -122,7 +122,7 @@ class PGVectorIndex(EmbeddingIndex):
async def query_keyword(
self,
query_string: str | None,
query_string: str,
k: int,
score_threshold: float,
) -> QueryChunksResponse:

2
llama_stack/providers/remote/vector_io/qdrant/qdrant.py
View file

@ -97,7 +97,7 @@ class QdrantIndex(EmbeddingIndex):
async def query_keyword(
self,
query_string: str | None,
query_string: str,
k: int,
score_threshold: float,
) -> QueryChunksResponse:

2
llama_stack/providers/remote/vector_io/weaviate/weaviate.py
View file

@ -86,7 +86,7 @@ class WeaviateIndex(EmbeddingIndex):
async def query_keyword(
self,
query_string: str | None,
query_string: str,
k: int,
score_threshold: float,
) -> QueryChunksResponse:

2
llama_stack/providers/utils/memory/vector_store.py
View file

@ -181,7 +181,7 @@ class EmbeddingIndex(ABC):
raise NotImplementedError()
@abstractmethod
async def query_keyword(self, query_string: str | None, k: int, score_threshold: float) -> QueryChunksResponse:
async def query_keyword(self, query_string: str, k: int, score_threshold: float) -> QueryChunksResponse:
raise NotImplementedError()
@abstractmethod