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Added coverage for the case where a vector DB was provided but no chunks were retrieved.

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ilya-kolchinsky 2025-04-09 19:53:59 +02:00
parent 127b62dee0
commit 1871cb9a71
1 changed files with 4 additions and 2 deletions
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6
llama_stack/providers/inline/tool_runtime/rag/memory.py
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@ -104,7 +104,9 @@ class MemoryToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, RAGToolRuntime):
query_config: Optional[RAGQueryConfig] = None,
) -> RAGQueryResult:
if not vector_db_ids:
raise ValueError("No vector DBs were provided to the RAG tool. Please provide at least one vector DB ID.")
raise ValueError(
"No vector DBs were provided to the knowledge search tool. Please provide at least one vector DB ID."
)
query_config = query_config or RAGQueryConfig()
query = await generate_rag_query(
@ -127,7 +129,7 @@ class MemoryToolRuntimeImpl(ToolsProtocolPrivate, ToolRuntime, RAGToolRuntime):
scores = [s for r in results for s in r.scores]
if not chunks:
return RAGQueryResult(content=None)
raise ValueError("The knowledge search tool did not find any information relevant to the query.")
# sort by score
chunks, scores = zip(*sorted(zip(chunks, scores, strict=False), key=lambda x: x[1], reverse=True), strict=False) # type: ignore