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284 lines
12 KiB
Python
284 lines
12 KiB
Python
from __future__ import annotations
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import pytest
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from stirling.rag.capability import RagCapability
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from stirling.rag.chunker import chunk_text
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from stirling.rag.service import RagService
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from stirling.rag.sqlite_vec_store import SqliteVecStore
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from stirling.rag.store import Document, SearchResult
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# ── chunk_text ──────────────────────────────────────────────────────────
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class TestChunkText:
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def test_empty_input_returns_empty(self) -> None:
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assert chunk_text("") == []
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assert chunk_text(" ") == []
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def test_short_text_returns_single_chunk(self) -> None:
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text = "Hello world."
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chunks = chunk_text(text, chunk_size=100)
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assert len(chunks) == 1
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assert chunks[0] == "Hello world."
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def test_splits_on_paragraph_boundaries(self) -> None:
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text = "First paragraph.\n\nSecond paragraph.\n\nThird paragraph."
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chunks = chunk_text(text, chunk_size=30, overlap=0)
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# Each paragraph fits in 30 chars, so they should be split
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assert len(chunks) >= 2
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assert "First paragraph." in chunks[0]
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def test_long_text_produces_multiple_chunks(self) -> None:
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text = " ".join(["word"] * 200)
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chunks = chunk_text(text, chunk_size=100, overlap=10)
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assert len(chunks) > 1
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for chunk in chunks:
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# Chunks may slightly exceed due to sentence boundary snapping
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assert len(chunk) <= 200 # generous upper bound
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def test_overlap_produces_shared_content(self) -> None:
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sentences = [f"Sentence number {i}." for i in range(20)]
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text = " ".join(sentences)
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chunks = chunk_text(text, chunk_size=100, overlap=30)
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if len(chunks) >= 2:
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# After word-boundary snapping, the second chunk should share
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# some content with the tail of the first chunk
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words_in_first_tail = chunks[0].split()[-3:] # last 3 words
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overlap_text = " ".join(words_in_first_tail)
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assert overlap_text in chunks[1], f"Expected overlap '{overlap_text}' in chunk[1]: '{chunks[1][:80]}...'"
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# ── SqliteVecStore ──────────────────────────────────────────────────────
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class TestSqliteVecStore:
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"""Each test gets its own ephemeral store to avoid cross-test dimension conflicts."""
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@pytest.mark.anyio
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async def test_add_and_search(self) -> None:
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store = SqliteVecStore.ephemeral()
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docs = [
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Document(id="1", text="Python is a programming language", metadata={"source": "test"}),
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Document(id="2", text="Java is another programming language", metadata={"source": "test"}),
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Document(id="3", text="The weather today is sunny", metadata={"source": "test"}),
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]
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# Simple 3-dimensional embeddings for testing
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embeddings = [
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[1.0, 0.0, 0.0],
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[0.9, 0.1, 0.0],
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[0.0, 0.0, 1.0],
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]
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await store.add_documents("test-col", docs, embeddings)
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# Search with a query close to the programming-related docs
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results = await store.search("test-col", [1.0, 0.05, 0.0], top_k=2)
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assert len(results) == 2
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assert isinstance(results[0], SearchResult)
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# The closest should be doc "1" (exact match on first dimension)
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assert results[0].document.id == "1"
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assert results[0].score > 0.5
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@pytest.mark.anyio
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async def test_list_and_has_collection(self) -> None:
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store = SqliteVecStore.ephemeral()
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docs = [Document(id="1", text="test", metadata={})]
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await store.add_documents("my-collection", docs, [[1.0, 0.0]])
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collections = await store.list_collections()
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assert "my-collection" in collections
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assert await store.has_collection("my-collection") is True
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assert await store.has_collection("nonexistent") is False
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@pytest.mark.anyio
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async def test_delete_collection(self) -> None:
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store = SqliteVecStore.ephemeral()
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docs = [Document(id="1", text="test", metadata={})]
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await store.add_documents("to-delete", docs, [[1.0]])
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assert await store.has_collection("to-delete") is True
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await store.delete_collection("to-delete")
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assert await store.has_collection("to-delete") is False
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@pytest.mark.anyio
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async def test_search_empty_collection(self) -> None:
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store = SqliteVecStore.ephemeral()
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docs = [Document(id="1", text="test", metadata={})]
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await store.add_documents("empty-test", docs, [[1.0, 0.0]])
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results = await store.search("empty-test", [1.0, 0.0], top_k=5)
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assert len(results) == 1
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@pytest.mark.anyio
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async def test_mismatched_docs_embeddings_raises(self) -> None:
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store = SqliteVecStore.ephemeral()
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docs = [Document(id="1", text="test", metadata={})]
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with pytest.raises(ValueError, match="documents.*embeddings"):
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await store.add_documents("bad", docs, [[1.0], [2.0]])
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# ── RagService (with stub embedder) ────────────────────────────────────
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class StubEmbeddingService:
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"""A minimal stub that returns fixed-dimension embeddings for testing."""
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def __init__(self, dim: int = 8) -> None:
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self._dim = dim
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async def embed_query(self, text: str) -> list[float]:
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# Deterministic embedding based on hash of text
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h = hash(text) % 1000
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return [(h + i) / 1000.0 for i in range(self._dim)]
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async def embed_documents(self, texts: list[str]) -> list[list[float]]:
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return [await self.embed_query(t) for t in texts]
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def chunk_and_prepare(
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self,
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text: str,
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source: str = "",
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base_metadata: dict[str, str] | None = None,
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) -> list[Document]:
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from stirling.rag.chunker import chunk_text
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chunks = chunk_text(text, 100, 10)
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docs = []
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for i, chunk in enumerate(chunks):
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meta = dict(base_metadata) if base_metadata else {}
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meta["source"] = source
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meta["chunk_index"] = str(i)
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doc_id = f"{source}:chunk:{i}" if source else f"chunk:{i}"
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docs.append(Document(id=doc_id, text=chunk, metadata=meta))
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return docs
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@pytest.fixture
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def rag_service() -> RagService:
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"""Each RagService test gets its own fresh ephemeral store to avoid dimension conflicts."""
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store = SqliteVecStore.ephemeral()
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return RagService(embedder=StubEmbeddingService(), store=store, default_top_k=3) # type: ignore[arg-type]
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class TestRagService:
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@pytest.mark.anyio
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async def test_index_and_search(self, rag_service: RagService) -> None:
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text = "Python is great for data science. It has many libraries like pandas and numpy."
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count = await rag_service.index_text("docs", text, source="guide.pdf")
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assert count > 0
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results = await rag_service.search("Python libraries", collection="docs")
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assert len(results) > 0
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assert results[0].document.text # non-empty text
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@pytest.mark.anyio
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async def test_index_empty_text_returns_zero(self, rag_service: RagService) -> None:
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count = await rag_service.index_text("docs", "", source="empty.pdf")
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assert count == 0
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@pytest.mark.anyio
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async def test_search_nonexistent_collection_returns_empty(self, rag_service: RagService) -> None:
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results = await rag_service.search("anything", collection="nonexistent")
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assert results == []
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@pytest.mark.anyio
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async def test_search_all_collections(self, rag_service: RagService) -> None:
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await rag_service.index_text("col-a", "Machine learning overview.", source="ml.pdf")
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await rag_service.index_text("col-b", "Deep learning with neural networks.", source="dl.pdf")
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results = await rag_service.search("neural networks")
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assert len(results) > 0
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@pytest.mark.anyio
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async def test_delete_collection(self, rag_service: RagService) -> None:
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await rag_service.index_text("temp", "Temporary data.", source="tmp.pdf")
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collections = await rag_service.list_collections()
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assert "temp" in collections
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await rag_service.delete_collection("temp")
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collections = await rag_service.list_collections()
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assert "temp" not in collections
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# ── RagCapability ──────────────────────────────────────────────────────
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async def _invoke_search_knowledge(capability: RagCapability, query: str, max_results: int = 5) -> str:
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"""Extract and call the search_knowledge tool function from a RagCapability's toolset."""
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from pydantic_ai import FunctionToolset
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toolset = capability.toolset
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assert isinstance(toolset, FunctionToolset)
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tool = toolset.tools["search_knowledge"]
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return await tool.function(query=query, max_results=max_results) # type: ignore[call-arg] — pyright can't infer the generic tool function's kwargs
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class TestRagCapability:
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def test_instructions_static_when_collections_pinned(self, rag_service: RagService) -> None:
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cap = RagCapability(rag_service, collections=["docs", "manuals"])
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instructions = cap.instructions
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assert isinstance(instructions, str)
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assert "docs, manuals" in instructions
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assert "search_knowledge" in instructions
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def test_instructions_dynamic_when_no_collections(self, rag_service: RagService) -> None:
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cap = RagCapability(rag_service)
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instructions = cap.instructions
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assert callable(instructions)
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@pytest.mark.anyio
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async def test_dynamic_instructions_list_available_collections(self, rag_service: RagService) -> None:
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await rag_service.index_text("col-a", "Alpha content.", source="a.pdf")
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await rag_service.index_text("col-b", "Beta content.", source="b.pdf")
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cap = RagCapability(rag_service)
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instructions_fn = cap.instructions
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assert callable(instructions_fn)
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text = await instructions_fn()
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assert "col-a" in text
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assert "col-b" in text
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@pytest.mark.anyio
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async def test_dynamic_instructions_when_store_empty(self, rag_service: RagService) -> None:
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cap = RagCapability(rag_service)
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instructions_fn = cap.instructions
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assert callable(instructions_fn)
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text = await instructions_fn()
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assert "empty" in text.lower()
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@pytest.mark.anyio
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async def test_search_knowledge_returns_no_results_message_when_empty(self, rag_service: RagService) -> None:
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cap = RagCapability(rag_service)
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output = await _invoke_search_knowledge(cap, "anything")
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assert output == "No relevant results found in the knowledge base."
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@pytest.mark.anyio
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async def test_search_knowledge_formats_results_with_source_and_score(self, rag_service: RagService) -> None:
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await rag_service.index_text("docs", "Python is a programming language.", source="guide.pdf")
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cap = RagCapability(rag_service)
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output = await _invoke_search_knowledge(cap, "Python")
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assert "[Result 1" in output
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assert "source: guide.pdf" in output
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assert "chunk:" in output
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assert "relevance:" in output
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@pytest.mark.anyio
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async def test_search_knowledge_restricts_to_pinned_collections(self, rag_service: RagService) -> None:
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await rag_service.index_text("pinned", "Pinned collection content.", source="pinned.pdf")
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await rag_service.index_text("other", "Content in another collection.", source="other.pdf")
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cap = RagCapability(rag_service, collections=["pinned"])
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output = await _invoke_search_knowledge(cap, "content")
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assert "pinned.pdf" in output
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assert "other.pdf" not in output
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@pytest.mark.anyio
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async def test_search_knowledge_respects_max_results(self, rag_service: RagService) -> None:
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paragraphs = "\n\n".join(f"Paragraph {i} about topic." for i in range(10))
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await rag_service.index_text("bulk", paragraphs, source="bulk.pdf")
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cap = RagCapability(rag_service)
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output = await _invoke_search_knowledge(cap, "topic", max_results=2)
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# Only two results requested, so only Result 1 and Result 2 should appear
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assert "[Result 1" in output
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assert "[Result 2" in output
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assert "[Result 3" not in output
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