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https://github.com/meta-llama/llama-stack.git
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8353ad4981
# What does this PR do?
<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->
I noticed a few issues with my implementation of the search mode
validation for RagQuery.
This PR replaces the check for search mode in RagQuery with a Literal.
There were issues before with
```
TypeError: Object of type RAGSearchMode is not JSON serializable
```
When using
```
query_config = RAGQueryConfig(max_chunks=6, mode="vector").model_dump()
```
It also fixes the fact that despite user input "vector" was always the
used search mode.
<!-- 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.* -->
Verify that a chosen search mode works when using Rag Query or use below
agent config:
```
agent = Agent(
client,
model=model_id,
instructions="You are a helpful assistant",
tools=[
{
"name": "builtin::rag/knowledge_search",
"args": {
"vector_db_ids": [vector_db_id],
"query_config": {
"mode": "keyword",
"max_chunks": 6
}
},
}
],
)
```
Running Unit Tests:
```
uv sync --extra dev
uv run pytest tests/unit/rag/test_rag_query.py -v
```
79 lines
3 KiB
Python
79 lines
3 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(config=MagicMock(), vector_io_api=MagicMock(), inference_api=MagicMock())
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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(config=MagicMock(), vector_io_api=MagicMock(), inference_api=MagicMock())
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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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