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c836fa29e3
# What does this PR do? <!-- Provide a short summary of what this PR does and why. Link to relevant issues if applicable. --> Fix pre-commit issues: non executable shebang file, @pytest.mark.asyncio decorator <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> ## Test Plan <!-- Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.* -->
138 lines
5 KiB
Python
138 lines
5 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from unittest.mock import AsyncMock, MagicMock
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import pytest
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from llama_stack.apis.tools.rag_tool import RAGQueryConfig
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from llama_stack.apis.vector_io import (
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Chunk,
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ChunkMetadata,
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QueryChunksResponse,
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)
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from llama_stack.providers.inline.tool_runtime.rag.memory import MemoryToolRuntimeImpl
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class TestRagQuery:
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async def test_query_raises_on_empty_vector_db_ids(self):
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rag_tool = MemoryToolRuntimeImpl(
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config=MagicMock(), vector_io_api=MagicMock(), inference_api=MagicMock(), files_api=MagicMock()
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)
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with pytest.raises(ValueError):
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await rag_tool.query(content=MagicMock(), vector_db_ids=[])
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async def test_query_chunk_metadata_handling(self):
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rag_tool = MemoryToolRuntimeImpl(
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config=MagicMock(), vector_io_api=MagicMock(), inference_api=MagicMock(), files_api=MagicMock()
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)
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content = "test query content"
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vector_db_ids = ["db1"]
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chunk_metadata = ChunkMetadata(
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document_id="doc1",
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chunk_id="chunk1",
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source="test_source",
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metadata_token_count=5,
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)
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interleaved_content = MagicMock()
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chunk = Chunk(
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content=interleaved_content,
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metadata={
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"key1": "value1",
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"token_count": 10,
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"metadata_token_count": 5,
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# Note this is inserted into `metadata` during MemoryToolRuntimeImpl().insert()
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"document_id": "doc1",
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},
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stored_chunk_id="chunk1",
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chunk_metadata=chunk_metadata,
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)
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query_response = QueryChunksResponse(chunks=[chunk], scores=[1.0])
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rag_tool.vector_io_api.query_chunks = AsyncMock(return_value=query_response)
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result = await rag_tool.query(content=content, vector_db_ids=vector_db_ids)
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assert result is not None
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expected_metadata_string = (
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"Metadata: {'chunk_id': 'chunk1', 'document_id': 'doc1', 'source': 'test_source', 'key1': 'value1'}"
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)
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assert expected_metadata_string in result.content[1].text
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assert result.content is not None
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async def test_query_raises_incorrect_mode(self):
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with pytest.raises(ValueError):
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RAGQueryConfig(mode="invalid_mode")
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async def test_query_accepts_valid_modes(self):
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default_config = RAGQueryConfig() # Test default (vector)
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assert default_config.mode == "vector"
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vector_config = RAGQueryConfig(mode="vector") # Test vector
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assert vector_config.mode == "vector"
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keyword_config = RAGQueryConfig(mode="keyword") # Test keyword
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assert keyword_config.mode == "keyword"
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hybrid_config = RAGQueryConfig(mode="hybrid") # Test hybrid
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assert hybrid_config.mode == "hybrid"
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# Test that invalid mode raises an error
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with pytest.raises(ValueError):
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RAGQueryConfig(mode="wrong_mode")
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async def test_query_adds_vector_db_id_to_chunk_metadata(self):
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rag_tool = MemoryToolRuntimeImpl(
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config=MagicMock(),
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vector_io_api=MagicMock(),
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inference_api=MagicMock(),
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files_api=MagicMock(),
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)
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vector_db_ids = ["db1", "db2"]
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# Fake chunks from each DB
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chunk_metadata1 = ChunkMetadata(
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document_id="doc1",
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chunk_id="chunk1",
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source="test_source1",
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metadata_token_count=5,
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)
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chunk1 = Chunk(
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content="chunk from db1",
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metadata={"vector_db_id": "db1", "document_id": "doc1"},
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stored_chunk_id="c1",
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chunk_metadata=chunk_metadata1,
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)
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chunk_metadata2 = ChunkMetadata(
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document_id="doc2",
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chunk_id="chunk2",
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source="test_source2",
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metadata_token_count=5,
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)
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chunk2 = Chunk(
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content="chunk from db2",
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metadata={"vector_db_id": "db2", "document_id": "doc2"},
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stored_chunk_id="c2",
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chunk_metadata=chunk_metadata2,
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)
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rag_tool.vector_io_api.query_chunks = AsyncMock(
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side_effect=[
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QueryChunksResponse(chunks=[chunk1], scores=[0.9]),
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QueryChunksResponse(chunks=[chunk2], scores=[0.8]),
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]
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)
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result = await rag_tool.query(content="test", vector_db_ids=vector_db_ids)
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returned_chunks = result.metadata["chunks"]
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returned_scores = result.metadata["scores"]
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returned_doc_ids = result.metadata["document_ids"]
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returned_vector_db_ids = result.metadata["vector_db_ids"]
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assert returned_chunks == ["chunk from db1", "chunk from db2"]
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assert returned_scores == (0.9, 0.8)
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assert returned_doc_ids == ["doc1", "doc2"]
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assert returned_vector_db_ids == ["db1", "db2"]
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