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removed skip_if_provider_doesnt_support_openai_vector_store_files_api, updated chroma _get_and_cache_vector_db_index, and updated test_query_unregistered_raises
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
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2defebc835
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c7ffe98588
3 changed files with 16 additions and 36 deletions
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@ -58,7 +58,6 @@ class ChromaIndex(EmbeddingIndex):
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self.kvstore = kvstore
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async def initialize(self):
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# Chroma does not require explicit initialization, this is just a helper for unit tests
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pass
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async def add_chunks(self, chunks: list[Chunk], embeddings: NDArray):
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@ -215,18 +214,12 @@ class ChromaVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolP
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if vector_db_id in self.cache:
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return self.cache[vector_db_id]
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try:
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collection = await maybe_await(self.client.get_collection(vector_db_id))
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if not collection:
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raise ValueError(f"Vector DB {vector_db_id} not found in Chroma")
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vector_db = await self.vector_db_store.get_vector_db(vector_db_id)
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if not vector_db:
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raise ValueError(f"Vector DB {vector_db_id} not found in Llama Stack")
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index = VectorDBWithIndex(vector_db, ChromaIndex(self.client, collection), self.inference_api)
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self.cache[vector_db_id] = index
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return index
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except Exception as exc:
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raise ValueError(f"Vector DB {vector_db_id} not found in Chroma") from exc
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vector_db = await self.vector_db_store.get_vector_db(vector_db_id)
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if not vector_db:
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raise ValueError(f"Vector DB {vector_db_id} not found in Llama Stack")
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collection = await maybe_await(self.client.get_collection(vector_db_id))
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if not collection:
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raise ValueError(f"Vector DB {vector_db_id} not found in Chroma")
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index = VectorDBWithIndex(vector_db, ChromaIndex(self.client, collection), self.inference_api)
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self.cache[vector_db_id] = index
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return index
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