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Merge pull request #560 from ggozad/fix/canonical-config

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Make get_config the only configuration lookup
This commit is contained in:
Yiorgis Gozadinos 2026-08-19 14:54:14 +03:00 committed by GitHub
commit 6533c28377
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36 changed files with 260 additions and 183 deletions
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1
CHANGELOG.md
View file

@ -25,6 +25,7 @@
### Removed
- `haiku.rag.config.Config`, the eagerly loaded configuration instance. Use `get_config()` for the current global config, or pass an `AppConfig`. Every internal default (`get_embedder`, `get_converter`, `get_chunker`, `get_reranker`, `embed_chunks`, `HaikuRAG`, `Store`, `HaikuRAGApp`, `create_mcp_server`) now takes `config: AppConfig | None = None` and resolves it per call, so `set_config()` reaches them. `RerankerBase._model` no longer defaults to the configured reranker name; `CohereReranker` takes its model name as an argument.
- `wix` evaluation dataset and its reference config `evaluations/configs/wix.yaml`.
### Fixed

16
app/backend/main.py
View file

@ -44,16 +44,16 @@ logger = logging.getLogger(__name__)
config_path = Path("/app/haiku.rag.yaml")
if config_path.exists():
yaml_data = load_yaml_config(config_path)
Config = AppConfig.model_validate(yaml_data)
config = AppConfig.model_validate(yaml_data)
else:
Config = AppConfig()
config = AppConfig()
# Get DB path from environment
db_path_str = os.getenv("DB_PATH", "haiku_rag.lancedb")
db_path = Path(db_path_str)
logger.info(f"Database path: {db_path}")
logger.info(f"QA Provider: {Config.qa.model.provider}, Model: {Config.qa.model.name}")
logger.info(f"QA Provider: {config.qa.model.provider}, Model: {config.qa.model.name}")
# Only HaikuRAG client is a singleton (expensive to create)
_client: HaikuRAG | None = None
@ -71,7 +71,7 @@ async def get_client() -> HaikuRAG:
if _client is None:
async with _client_lock:
if _client is None:
client = HaikuRAG(db_path=db_path, config=Config, create=True)
client = HaikuRAG(db_path=db_path, config=config, create=True)
await client.__aenter__()
_client = client
return _client
@ -82,10 +82,10 @@ class AppDeps:
state: dict[str, Any] = field(default_factory=dict)
capability = create_capability(db_path=db_path, config=Config, defer_loading=False)
capability = create_capability(db_path=db_path, config=config, defer_loading=False)
agent = Agent(
get_model(Config.qa.model, Config),
get_model(config.qa.model, config),
instructions=AGENT_PREAMBLE,
# Conversations here are multi-turn, so earlier questions are reduced to the
# evidence they cited rather than carried whole, and every answer declares
@ -136,8 +136,8 @@ async def health_check(_: Request) -> JSONResponse:
return JSONResponse(
{
"status": "healthy",
"qa_provider": Config.qa.model.provider,
"qa_model": Config.qa.model.name,
"qa_provider": config.qa.model.provider,
"qa_model": config.qa.model.name,
"db_path": str(db_path),
"db_exists": db_path.exists(),
}

6
haiku_rag_slim/haiku/rag/app.py
View file

@ -16,7 +16,7 @@ from rich.progress import (
)
from haiku.rag.client import HaikuRAG, RebuildMode
from haiku.rag.config import AppConfig, Config
from haiku.rag.config import AppConfig, get_config
from haiku.rag.mcp import create_mcp_server
from haiku.rag.store.models.chunk import SearchType
from haiku.rag.store.models.document import Document
@ -33,11 +33,11 @@ class HaikuRAGApp: # pragma: no cover
def __init__(
self,
db_path: Path,
config: AppConfig = Config,
config: AppConfig | None = None,
read_only: bool = False,
):
self.db_path = db_path
self.config = config
self.config = config if config is not None else get_config()
self.read_only = read_only
self.console = Console()

7
haiku_rag_slim/haiku/rag/chunkers/__init__.py
View file

@ -1,16 +1,16 @@
"""Document chunker abstraction for haiku.rag."""
from haiku.rag.chunkers.base import DocumentChunker
from haiku.rag.config import AppConfig, Config
from haiku.rag.config import AppConfig, get_config
__all__ = ["DocumentChunker", "get_chunker"]
def get_chunker(config: AppConfig = Config) -> DocumentChunker:
def get_chunker(config: AppConfig | None = None) -> DocumentChunker:
"""Get a document chunker instance based on configuration.
Args:
config: Configuration to use. Defaults to global Config.
config: Configuration to use. Defaults to the current global config.
Returns:
DocumentChunker instance configured according to the config.
@ -18,6 +18,7 @@ def get_chunker(config: AppConfig = Config) -> DocumentChunker:
Raises:
ValueError: If the chunker provider is not recognized.
"""
config = config if config is not None else get_config()
if config.processing.chunker == "docling-local":
from haiku.rag.chunkers.docling_local import DoclingLocalChunker

18
haiku_rag_slim/haiku/rag/chunkers/docling_local.py
View file

@ -3,7 +3,7 @@ from functools import cache
from typing import TYPE_CHECKING, cast
from haiku.rag.chunkers.base import DocumentChunker
from haiku.rag.config import AppConfig, Config
from haiku.rag.config import AppConfig, get_config
from haiku.rag.store.models.chunk import Chunk, ChunkMetadata
if TYPE_CHECKING:
@ -68,7 +68,7 @@ class DoclingLocalChunker(DocumentChunker):
config: Application configuration.
"""
def __init__(self, config: AppConfig = Config):
def __init__(self, config: AppConfig | None = None):
from docling_core.transforms.chunker.hierarchical_chunker import (
HierarchicalChunker,
)
@ -77,10 +77,10 @@ class DoclingLocalChunker(DocumentChunker):
HuggingFaceTokenizer,
)
self.config = config
self.chunk_size = config.processing.chunk_size
self.chunker_type = config.processing.chunker_type
self.tokenizer_name = config.processing.chunking_tokenizer
self.config = config if config is not None else get_config()
self.chunk_size = self.config.processing.chunk_size
self.chunker_type = self.config.processing.chunker_type
self.tokenizer_name = self.config.processing.chunking_tokenizer
if self.chunker_type == "hybrid":
hf_tokenizer = _get_tokenizer(self.tokenizer_name)
@ -88,16 +88,16 @@ class DoclingLocalChunker(DocumentChunker):
tokenizer=hf_tokenizer, max_tokens=self.chunk_size
)
serializer_provider = _create_markdown_serializer_provider(
use_markdown_tables=config.processing.chunking_use_markdown_tables
use_markdown_tables=self.config.processing.chunking_use_markdown_tables
)
self.chunker = HybridChunker(
tokenizer=tokenizer,
merge_peers=config.processing.chunking_merge_peers,
merge_peers=self.config.processing.chunking_merge_peers,
serializer_provider=serializer_provider,
)
elif self.chunker_type == "hierarchical":
serializer_provider = _create_markdown_serializer_provider(
use_markdown_tables=config.processing.chunking_use_markdown_tables
use_markdown_tables=self.config.processing.chunking_use_markdown_tables
)
self.chunker = HierarchicalChunker(serializer_provider=serializer_provider)
else:

12
haiku_rag_slim/haiku/rag/chunkers/docling_serve.py
View file

@ -4,7 +4,7 @@ from io import BytesIO
from typing import TYPE_CHECKING
from haiku.rag.chunkers.base import DocumentChunker
from haiku.rag.config import AppConfig, Config
from haiku.rag.config import AppConfig, get_config
from haiku.rag.providers.docling_serve import DoclingServeClient
from haiku.rag.store.models.chunk import Chunk, ChunkMetadata
@ -54,10 +54,12 @@ class DoclingServeChunker(DocumentChunker):
config: Application configuration containing docling-serve settings.
"""
def __init__(self, config: AppConfig = Config):
self.config = config
self.client = DoclingServeClient.from_config(config.providers.docling_serve)
self.chunker_type = config.processing.chunker_type
def __init__(self, config: AppConfig | None = None):
self.config = config if config is not None else get_config()
self.client = DoclingServeClient.from_config(
self.config.providers.docling_serve
)
self.chunker_type = self.config.processing.chunker_type
def _build_chunking_data(self) -> dict[str, str | list[str]]:
"""Build form data for chunking request."""

8
haiku_rag_slim/haiku/rag/client/__init__.py
View file

@ -15,7 +15,7 @@ from urllib.parse import urlparse
import httpx
from haiku.rag.client.documents import DocumentImport
from haiku.rag.config import AppConfig, Config
from haiku.rag.config import AppConfig, get_config
from haiku.rag.converters import get_converter
from haiku.rag.reranking import get_reranker
from haiku.rag.store.engine import Store
@ -67,7 +67,7 @@ class HaikuRAG:
def __init__(
self,
db_path: Path | None = None,
config: AppConfig = Config,
config: AppConfig | None = None,
skip_validation: bool = False,
create: bool = False,
read_only: bool = False,
@ -76,12 +76,12 @@ class HaikuRAG:
Args:
db_path: Path to the database file. If None, uses config.storage.data_dir.
config: Configuration to use. Defaults to global Config.
config: Configuration to use. Defaults to the current global config.
skip_validation: Whether to skip configuration validation on database load.
create: Whether to create the database if it doesn't exist.
read_only: Whether to open the database in read-only mode.
"""
self._config = config
self._config = config if config is not None else get_config()
if db_path is None:
db_path = self._config.storage.data_dir / "haiku.rag.lancedb"

9
haiku_rag_slim/haiku/rag/config/__init__.py
View file

@ -36,7 +36,6 @@ from haiku.rag.config.models import (
)
__all__ = [
"Config",
"APIConfig",
"AppConfig",
"CircuitBreakerConfig",
@ -78,7 +77,7 @@ _config: AppConfig | None = None
def _load_default_config() -> AppConfig:
"""Load config from default locations (used at import time)."""
"""Load config from the default locations."""
config_path = find_config_file(None)
if config_path:
yaml_data = load_yaml_config(config_path)
@ -93,12 +92,8 @@ def set_config(config: AppConfig) -> None:
def get_config() -> AppConfig:
"""Get the current config instance."""
"""Get the current config instance, loading it on first use."""
global _config
if _config is None:
_config = _load_default_config()
return _config
# Legacy compatibility - Config is the default instance
Config = _load_default_config()

7
haiku_rag_slim/haiku/rag/converters/__init__.py
View file

@ -1,16 +1,16 @@
"""Document converter abstraction for haiku.rag."""
from haiku.rag.config import AppConfig, Config
from haiku.rag.config import AppConfig, get_config
from haiku.rag.converters.base import DocumentConverter
__all__ = ["DocumentConverter", "get_converter"]
def get_converter(config: AppConfig = Config) -> DocumentConverter:
def get_converter(config: AppConfig | None = None) -> DocumentConverter:
"""Get a document converter instance based on configuration.
Args:
config: Configuration to use. Defaults to global Config.
config: Configuration to use. Defaults to the current global config.
Returns:
DocumentConverter instance configured according to the config.
@ -18,6 +18,7 @@ def get_converter(config: AppConfig = Config) -> DocumentConverter:
Raises:
ValueError: If the converter provider is not recognized.
"""
config = config if config is not None else get_config()
if config.processing.converter == "docling-local":
from haiku.rag.converters.docling_local import DoclingLocalConverter

12
haiku_rag_slim/haiku/rag/embeddings/__init__.py
View file

@ -7,7 +7,7 @@ from pydantic_ai.embeddings.openai import OpenAIEmbeddingModel
from pydantic_ai.providers.ollama import OllamaProvider
from pydantic_ai.providers.openai import OpenAIProvider
from haiku.rag.config import AppConfig, Config
from haiku.rag.config import AppConfig, get_config
if TYPE_CHECKING:
from PIL import Image as PILImage
@ -118,7 +118,9 @@ def contextualize(chunks: list["Chunk"]) -> list[str]:
async def embed_chunks(
chunks: list["Chunk"], embedder: "EmbedderWrapper", config: AppConfig = Config
chunks: list["Chunk"],
embedder: "EmbedderWrapper",
config: AppConfig | None = None,
) -> list["Chunk"]:
"""Generate embeddings for chunks, dispatching text vs picture variants.
@ -127,6 +129,7 @@ async def embed_chunks(
are routed through ``embed_images`` and require a multimodal embedder.
Vectors land in the original chunk order.
"""
config = config if config is not None else get_config()
if not chunks:
return []
@ -181,15 +184,16 @@ async def embed_chunks(
]
def get_embedder(config: AppConfig = Config) -> EmbedderWrapper:
def get_embedder(config: AppConfig | None = None) -> EmbedderWrapper:
"""Factory function to get the appropriate embedder based on the configuration.
Args:
config: Configuration to use. Defaults to global Config.
config: Configuration to use. Defaults to the current global config.
Returns:
An embedder instance configured according to the config.
"""
config = config if config is not None else get_config()
embedding_model = config.embeddings.model
provider = embedding_model.provider
model_name = embedding_model.name

5
haiku_rag_slim/haiku/rag/mcp.py
View file

@ -7,7 +7,7 @@ from typing import Any
from fastmcp import FastMCP
from haiku.rag.client import HaikuRAG
from haiku.rag.config import AppConfig, Config
from haiku.rag.config import AppConfig, get_config
from haiku.rag.store.models import Document, SearchResult
from haiku.rag.tools.document import DocumentInfo
from haiku.rag.utils import format_citations
@ -22,7 +22,7 @@ def _decode_images(images_base64: list[str] | None) -> list[bytes] | None:
def create_mcp_server(
db_path: Path, config: AppConfig = Config, read_only: bool = False
db_path: Path, config: AppConfig | None = None, read_only: bool = False
) -> FastMCP:
"""Create an MCP server with the specified database path.
@ -31,6 +31,7 @@ def create_mcp_server(
config: Configuration to use.
read_only: If True, write tools (add_document_*, delete_document) are not registered.
"""
config = config if config is not None else get_config()
client: HaikuRAG | None = None
stack = AsyncExitStack()
client_lock = asyncio.Lock()

7
haiku_rag_slim/haiku/rag/reranking/__init__.py
View file

@ -1,10 +1,11 @@
from haiku.rag.config import AppConfig, Config
from haiku.rag.config import AppConfig, get_config
from haiku.rag.reranking.base import RerankerBase
def get_reranker(config: AppConfig = Config) -> RerankerBase | None:
def get_reranker(config: AppConfig | None = None) -> RerankerBase | None:
"""Build the configured reranker, or None if reranking is disabled or its
optional dependency is not installed."""
config = config if config is not None else get_config()
model = config.reranking.model
if model is None:
return None
@ -16,7 +17,7 @@ def get_reranker(config: AppConfig = Config) -> RerankerBase | None:
if model.provider == "cohere":
from haiku.rag.reranking.cohere import CohereReranker
return CohereReranker()
return CohereReranker(model.name)
if model.provider == "vllm":
if not model.base_url:

3
haiku_rag_slim/haiku/rag/reranking/base.py
View file

@ -1,9 +1,8 @@
from haiku.rag.config import Config
from haiku.rag.store.models.chunk import Chunk
class RerankerBase:
_model: str | None = Config.reranking.model.name if Config.reranking.model else None
_model: str | None = None
async def rerank(
self, query: str, chunks: list[Chunk], top_n: int = 10

3
haiku_rag_slim/haiku/rag/reranking/cohere.py
View file

@ -10,7 +10,8 @@ except ImportError as e: # pragma: no cover
class CohereReranker(RerankerBase): # pragma: no cover
def __init__(self):
def __init__(self, model: str | None = None):
self._model = model
# Cohere SDK reads CO_API_KEY from environment by default
self._client = cohere.AsyncClientV2()

6
haiku_rag_slim/haiku/rag/store/engine.py
View file

@ -18,7 +18,7 @@ from lancedb.pydantic import LanceModel, Vector
from packaging.version import parse
from pydantic import BaseModel, Field
from haiku.rag.config import AppConfig, Config
from haiku.rag.config import AppConfig, get_config
from haiku.rag.embeddings import get_embedder
from haiku.rag.store.exceptions import MigrationRequiredError, ReadOnlyError
@ -497,14 +497,14 @@ class Store:
def __init__(
self,
db_path: Path,
config: AppConfig = Config,
config: AppConfig | None = None,
skip_validation: bool = False,
create: bool = False,
read_only: bool = False,
skip_migration_check: bool = False,
):
self.db_path: Path = db_path
self._config = config
self._config = config if config is not None else get_config()
self._read_only = read_only
self._create = create
self._skip_validation = skip_validation

6
haiku_rag_slim/haiku/rag/utils.py
View file

@ -119,7 +119,7 @@ def get_model(
Args:
model_config: ModelConfig with provider, model, and settings
app_config: AppConfig for provider base URLs (defaults to global Config)
app_config: AppConfig for provider base URLs (defaults to the current global config)
Returns:
A configured model instance
@ -129,9 +129,9 @@ def get_model(
from pydantic_ai.providers.openai import OpenAIProvider
if app_config is None:
from haiku.rag.config import Config
from haiku.rag.config import get_config
app_config = Config
app_config = get_config()
provider = model_config.provider
model = model_config.name

40
tests/test_chunk.py
View file

@ -1,7 +1,7 @@
import pytest
from haiku.rag.client import HaikuRAG
from haiku.rag.config import Config
from haiku.rag.config import get_config
from haiku.rag.store.models.chunk import Chunk, ChunkMetadata, SearchResult
from tests.conftest import capture_logs
@ -11,7 +11,9 @@ async def test_chunk_repository_operations(
qa_corpus: list[dict[str, str]], temp_db_path
):
"""Test ChunkRepository operations."""
async with HaikuRAG(db_path=temp_db_path, config=Config, create=True) as client:
async with HaikuRAG(
db_path=temp_db_path, config=get_config(), create=True
) as client:
# Get the first document from the corpus
first_doc = qa_corpus[0]
document_text = first_doc["document_extracted"]
@ -52,7 +54,9 @@ async def test_chunk_repository_pagination(
qa_corpus: list[dict[str, str]], temp_db_path
):
"""Test ChunkRepository pagination with get_by_document_id and count_by_document_id."""
async with HaikuRAG(db_path=temp_db_path, config=Config, create=True) as client:
async with HaikuRAG(
db_path=temp_db_path, config=get_config(), create=True
) as client:
# Get the first document from the corpus (should produce multiple chunks)
first_doc = qa_corpus[0]
document_text = first_doc["document_extracted"]
@ -443,7 +447,9 @@ async def test_chunk_content_fts(temp_db_path, metadata, content, expected_conte
"""content_fts holds the contextualized content while content stays raw."""
from haiku.rag.embeddings import get_embedder
async with HaikuRAG(db_path=temp_db_path, config=Config, create=True) as client:
async with HaikuRAG(
db_path=temp_db_path, config=get_config(), create=True
) as client:
chunk = Chunk(
document_id="test-doc",
content=content,
@ -451,7 +457,7 @@ async def test_chunk_content_fts(temp_db_path, metadata, content, expected_conte
order=0,
)
embedder = get_embedder(Config)
embedder = get_embedder(get_config())
embedding = (await embedder.embed_documents([chunk.content]))[0]
chunk.embedding = embedding
@ -477,7 +483,9 @@ async def test_ensure_fts_index_warns_on_failure(temp_db_path):
from haiku.rag.store.repositories import chunk as chunk_module
async with HaikuRAG(db_path=temp_db_path, config=Config, create=True) as client:
async with HaikuRAG(
db_path=temp_db_path, config=get_config(), create=True
) as client:
repo = client.chunk_repository
async def _boom(*_args, **_kwargs):
@ -497,7 +505,9 @@ async def test_chunk_repository_get_by_id_and_list_all_pagination(
qa_corpus: list[dict[str, str]], temp_db_path
):
"""get_by_id resolves a stored chunk; list_all honours limit and offset."""
async with HaikuRAG(db_path=temp_db_path, config=Config, create=True) as client:
async with HaikuRAG(
db_path=temp_db_path, config=get_config(), create=True
) as client:
# A corpus document is long enough to chunk more than once, which is
# what makes the offset assertion below meaningful.
doc = await client.create_document(content=qa_corpus[0]["document_extracted"])
@ -531,7 +541,9 @@ async def test_chunk_repository_get_by_id_and_list_all_pagination(
@pytest.mark.vcr()
async def test_chunk_search_returns_empty_for_blank_query(temp_db_path):
"""A blank query with no precomputed vector short-circuits before searching."""
async with HaikuRAG(db_path=temp_db_path, config=Config, create=True) as client:
async with HaikuRAG(
db_path=temp_db_path, config=get_config(), create=True
) as client:
await client.create_document(content="Searchable body about elections.")
# Positive control: the corpus is non-empty, so [] is a real decision
@ -543,7 +555,9 @@ async def test_chunk_search_returns_empty_for_blank_query(temp_db_path):
@pytest.mark.vcr()
async def test_chunk_search_with_precomputed_vector_skips_text_query(temp_db_path):
"""The image-as-query path searches vector-only using a stored embedding."""
async with HaikuRAG(db_path=temp_db_path, config=Config, create=True) as client:
async with HaikuRAG(
db_path=temp_db_path, config=get_config(), create=True
) as client:
doc = await client.create_document(content="Vector-only search target.")
assert doc.id is not None
@ -557,7 +571,9 @@ async def test_chunk_search_with_precomputed_vector_skips_text_query(temp_db_pat
async def test_get_chunk_ids_by_self_ref_grouped_without_documents(temp_db_path):
async with HaikuRAG(db_path=temp_db_path, config=Config, create=True) as client:
async with HaikuRAG(
db_path=temp_db_path, config=get_config(), create=True
) as client:
assert await client.chunk_repository.get_chunk_ids_by_self_ref_grouped([]) == {}
@ -565,7 +581,9 @@ async def test_process_search_results_rejects_unknown_score_column(temp_db_path)
"""A result frame with no recognised score column is a programming error."""
import pandas as pd
async with HaikuRAG(db_path=temp_db_path, config=Config, create=True) as client:
async with HaikuRAG(
db_path=temp_db_path, config=get_config(), create=True
) as client:
class _Frame:
async def to_pandas(self):

38
tests/test_chunker.py
View file

@ -7,7 +7,7 @@ from transformers import AutoTokenizer
from haiku.rag.chunkers import get_chunker
from haiku.rag.chunkers.docling_local import DoclingLocalChunker
from haiku.rag.chunkers.docling_serve import DoclingServeChunker
from haiku.rag.config import AppConfig, Config
from haiku.rag.config import AppConfig, get_config
from haiku.rag.converters import get_converter
@ -23,7 +23,7 @@ async def test_local_chunker(qa_corpus: list[dict[str, str]]):
doc_text = qa_corpus[0]["document_extracted"]
# Convert text to DoclingDocument
converter = get_converter(Config)
converter = get_converter(get_config())
doc = await converter.convert_text(doc_text, name="test.md")
chunks = await chunker.chunk(doc)
@ -79,7 +79,7 @@ async def test_local_chunker_runs_off_event_loop_thread():
called_from.append(threading.current_thread())
return original(document)
converter = get_converter(Config)
converter = get_converter(get_config())
doc = await converter.convert_text("# Hello\n\nWorld", name="test.md")
with patch.object(DoclingLocalChunker, "_chunk_sync", recording_chunk_sync):
@ -149,7 +149,7 @@ async def test_local_chunker_hierarchical(qa_corpus: list[dict[str, str]]):
chunker = DoclingLocalChunker(config)
doc_text = qa_corpus[0]["document_extracted"]
converter = get_converter(Config)
converter = get_converter(get_config())
doc = await converter.convert_text(doc_text, name="test.md")
chunks = await chunker.chunk(doc)
@ -180,7 +180,7 @@ async def test_local_chunker_markdown_tables():
| Value D | Value E |
"""
converter = get_converter(Config)
converter = get_converter(get_config())
doc = await converter.convert_text(markdown_with_table, name="test.md")
# Test with markdown tables enabled
@ -222,7 +222,7 @@ Second paragraph.
Third paragraph.
"""
converter = get_converter(Config)
converter = get_converter(get_config())
doc = await converter.convert_text(sample_md, name="test.md")
chunker = DoclingLocalChunker()
@ -249,7 +249,7 @@ Here is some background information.
|----------|----------|
| Value 1 | Value 2 |
"""
converter = get_converter(Config)
converter = get_converter(get_config())
doc = await converter.convert_text(sample_md, name="test.md")
chunker = DoclingLocalChunker()
@ -347,7 +347,7 @@ class TestDoclingServeChunker:
mock_client_class.return_value.__aenter__.return_value = mock_client
# Create a simple document
converter = get_converter(Config)
converter = get_converter(get_config())
doc = await converter.convert_text("# Test\n\nContent", name="test.md")
chunks = await chunker.chunk(doc)
@ -371,7 +371,7 @@ class TestDoclingServeChunker:
mock_client.get = AsyncMock(side_effect=[poll_resp, result_resp])
mock_client_class.return_value.__aenter__.return_value = mock_client
converter = get_converter(Config)
converter = get_converter(get_config())
doc = await converter.convert_text("# Test", name="test.md")
await chunker.chunk(doc)
@ -394,7 +394,7 @@ class TestDoclingServeChunker:
mock_client.get = AsyncMock(side_effect=[poll_resp, result_resp])
mock_client_class.return_value.__aenter__.return_value = mock_client
converter = get_converter(Config)
converter = get_converter(get_config())
doc = await converter.convert_text("# Test", name="test.md")
await chunker.chunk(doc)
@ -422,7 +422,7 @@ class TestDoclingServeChunker:
mock_client.get = AsyncMock(side_effect=[poll_resp, result_resp])
mock_client_class.return_value.__aenter__.return_value = mock_client
converter = get_converter(Config)
converter = get_converter(get_config())
doc = await converter.convert_text("# Test", name="test.md")
await chunker.chunk(doc)
@ -453,7 +453,7 @@ class TestDoclingServeChunker:
mock_client.get = AsyncMock(side_effect=[poll_resp, result_resp])
mock_client_class.return_value.__aenter__.return_value = mock_client
converter = get_converter(Config)
converter = get_converter(get_config())
doc = await converter.convert_text("# Test", name="test.md")
await chunker.chunk(doc)
@ -476,7 +476,7 @@ class TestDoclingServeChunker:
mock_client.post.side_effect = httpx.ConnectError("Connection failed")
mock_client_class.return_value.__aenter__.return_value = mock_client
converter = get_converter(Config)
converter = get_converter(get_config())
doc = await converter.convert_text("# Test", name="test.md")
with pytest.raises(httpx.ConnectError):
@ -492,7 +492,7 @@ class TestDoclingServeChunker:
mock_client.post.side_effect = httpx.TimeoutException("Timeout")
mock_client_class.return_value.__aenter__.return_value = mock_client
converter = get_converter(Config)
converter = get_converter(get_config())
doc = await converter.convert_text("# Test", name="test.md")
with pytest.raises(httpx.TimeoutException):
@ -517,7 +517,7 @@ class TestDoclingServeChunker:
mock_client.post.return_value = mock_response
mock_client_class.return_value.__aenter__.return_value = mock_client
converter = get_converter(Config)
converter = get_converter(get_config())
doc = await converter.convert_text("# Test", name="test.md")
with pytest.raises(httpx.HTTPStatusError) as exc_info:
@ -545,7 +545,7 @@ class TestDoclingServeChunker:
mock_client.get = AsyncMock(side_effect=[poll_resp, result_resp])
mock_client_class.return_value.__aenter__.return_value = mock_client
converter = get_converter(Config)
converter = get_converter(get_config())
doc = await converter.convert_text("# Test", name="test.md")
with pytest.raises(ValueError, match="Chunking failed"):
@ -574,7 +574,7 @@ class TestDoclingServeChunker:
mock_client.get = AsyncMock(side_effect=[poll_resp, result_resp])
mock_client_class.return_value.__aenter__.return_value = mock_client
converter = get_converter(Config)
converter = get_converter(get_config())
doc = await converter.convert_text("# Test", name="test.md")
chunks = await chunker.chunk(doc)
@ -612,7 +612,7 @@ class TestDoclingServeChunker:
mock_client_class.return_value.__aenter__.return_value = mock_client
# Create a document with texts and tables that match the mocked refs
converter = get_converter(Config)
converter = get_converter(get_config())
doc = await converter.convert_text(
"""# Chapter 1
@ -700,7 +700,7 @@ async def test_local_and_serve_chunkers_produce_same_output(doclaynet_first_page
from haiku.rag.converters.docling_serve import DoclingServeConverter
# Use docling-serve to convert the PDF (ensures same conversion for both chunkers)
converter = DoclingServeConverter(Config)
converter = DoclingServeConverter(get_config())
pdf_path = doclaynet_first_page_pdf
doc = await converter.convert_file(pdf_path)

34
tests/test_client.py
View file

@ -17,7 +17,7 @@ from haiku.rag.client.documents import (
_write_fetch_body,
check_source_accessible,
)
from haiku.rag.config import Config
from haiku.rag.config import get_config
from haiku.rag.embeddings import EmbedderWrapper
from haiku.rag.ingester.sources.base import FetchResult
from haiku.rag.store.compression import decompress_json
@ -813,7 +813,7 @@ async def test_client_import_document_with_custom_chunks(temp_db_path):
Chunk(
content="This is the second chunk",
metadata={"custom": "metadata2"},
embedding=[0.1] * Config.embeddings.model.vector_dim,
embedding=[0.1] * get_config().embeddings.model.vector_dim,
order=1,
), # With embedding
Chunk(
@ -856,7 +856,7 @@ def _docling_doc(name: str, text: str):
def _import(name: str, text: str, **overrides) -> "DocumentImport":
"""Build a DocumentImport with one pre-embedded chunk (no embedder call)."""
dim = Config.embeddings.model.vector_dim
dim = get_config().embeddings.model.vector_dim
chunk = Chunk(content=text, embedding=[0.1] * dim, order=0)
return DocumentImport(
docling_document=_docling_doc(name, text),
@ -868,7 +868,7 @@ def _import(name: str, text: str, **overrides) -> "DocumentImport":
async def test_client_import_documents_single_version_per_table(temp_db_path):
"""import_documents writes documents/chunks/document_items once for the
whole batch (issue #287)."""
config = Config.model_copy(deep=True)
config = get_config().model_copy(deep=True)
config.storage.auto_vacuum = False
async with HaikuRAG(temp_db_path, config=config, create=True) as client:
@ -902,7 +902,7 @@ async def test_client_import_documents_single_version_per_table(temp_db_path):
async def test_client_import_documents_rolls_back_on_failure(temp_db_path):
"""A failure mid-batch restores all tables: nothing is persisted."""
dim = Config.embeddings.model.vector_dim
dim = get_config().embeddings.model.vector_dim
async with HaikuRAG(temp_db_path, create=True) as client:
good = _import("good", "Good document body", uri="mem://good")
@ -943,7 +943,7 @@ async def test_client_import_documents_batches_embeddings(temp_db_path):
"""Chunks missing embeddings are embedded in one pass across the whole
batch, not one embedder call per document. Duplicate chunk texts across
documents keep their per-document embeddings."""
dim = Config.embeddings.model.vector_dim
dim = get_config().embeddings.model.vector_dim
embedder = _CountingEmbedder(dim)
async with HaikuRAG(temp_db_path, create=True) as client:
@ -977,7 +977,7 @@ async def test_client_import_documents_batches_embeddings(temp_db_path):
async def test_client_import_documents_mixed_embeddings(temp_db_path):
"""Pre-embedded chunks keep their vectors; only the unembedded ones go
through the embedder, in one batch."""
dim = Config.embeddings.model.vector_dim
dim = get_config().embeddings.model.vector_dim
embedder = _CountingEmbedder(dim)
async with HaikuRAG(temp_db_path, create=True) as client:
@ -1024,8 +1024,8 @@ async def test_client_update_document_replaces_rows_with_bounded_versions(
auto_vacuum is off: its writes would land inside the measured window.
"""
dim = Config.embeddings.model.vector_dim
config = Config.model_copy(deep=True)
dim = get_config().embeddings.model.vector_dim
config = get_config().model_copy(deep=True)
config.storage.auto_vacuum = False
async with HaikuRAG(temp_db_path, config=config, create=True) as client:
@ -1076,8 +1076,8 @@ async def test_metadata_only_update_does_not_advance_documents_table(temp_db_pat
live in document_meta, so the documents table version must stay frozen while
only metadata/title change — and reads must still hydrate the full Document.
"""
dim = Config.embeddings.model.vector_dim
config = Config.model_copy(deep=True)
dim = get_config().embeddings.model.vector_dim
config = get_config().model_copy(deep=True)
config.storage.auto_vacuum = False
async with HaikuRAG(temp_db_path, config=config, create=True) as client:
@ -1121,7 +1121,7 @@ async def test_metadata_only_update_does_not_advance_documents_table(temp_db_pat
async def test_delete_marks_vacuum_dirty(temp_db_path):
"""A delete adds tombstone/table versions, so it must enter the auto-vacuum
lifecycle — otherwise a delete-only run closes without a final vacuum."""
dim = Config.embeddings.model.vector_dim
dim = get_config().embeddings.model.vector_dim
async with HaikuRAG(temp_db_path, create=True) as client:
doc = await client.import_document(
_docling_doc("d", "body"),
@ -1138,8 +1138,8 @@ async def test_delete_marks_vacuum_dirty(temp_db_path):
async def test_delete_rolls_back_on_partial_failure(temp_db_path, monkeypatch):
"""A multi-table delete is atomic: if a later table delete fails, the write
lock + version restore bring every table back, leaving no orphaned rows."""
dim = Config.embeddings.model.vector_dim
config = Config.model_copy(deep=True)
dim = get_config().embeddings.model.vector_dim
config = get_config().model_copy(deep=True)
config.storage.auto_vacuum = False
async with HaikuRAG(temp_db_path, config=config, create=True) as client:
@ -1175,8 +1175,8 @@ async def test_cascade_delete_is_atomic(temp_db_path, monkeypatch):
"""Deleting a parent cascades to children under one lock + snapshot. If any
delete in the subtree fails, the whole subtree is restored — a child isn't
left deleted while its parent survives."""
dim = Config.embeddings.model.vector_dim
config = Config.model_copy(deep=True)
dim = get_config().embeddings.model.vector_dim
config = get_config().model_copy(deep=True)
config.storage.auto_vacuum = False
async with HaikuRAG(temp_db_path, config=config, create=True) as client:
@ -2390,7 +2390,7 @@ async def test_metadata_only_update_waits_for_write_lock(temp_db_path):
create_tag's version snapshot and its per-table tag creation)."""
import asyncio
dim = Config.embeddings.model.vector_dim
dim = get_config().embeddings.model.vector_dim
docling_doc = DoclingDocument(name="d")
docling_doc.add_text(label=DocItemLabel.TEXT, text="body")

47
tests/test_config.py
View file

@ -3,7 +3,7 @@ from pathlib import Path
import pytest
import yaml
from haiku.rag.config import AppConfig
from haiku.rag.config import AppConfig, set_config
from haiku.rag.config.loader import (
find_config_file,
generate_default_config,
@ -577,3 +577,48 @@ def test_example_configs_are_present():
)
def test_example_config_validates(path: Path):
AppConfig.model_validate(yaml.safe_load(path.read_text()) or {})
def _use_config(monkeypatch, cfg):
"""Install cfg as the global config, restored by monkeypatch teardown."""
import haiku.rag.config as config_module
monkeypatch.setattr(config_module, "_config", None)
set_config(cfg)
def test_set_config_reaches_the_factories(monkeypatch, tmp_path):
"""A config installed after import must reach every factory and
constructor that resolves the global config itself."""
from haiku.rag.chunkers import get_chunker
from haiku.rag.chunkers.docling_serve import DoclingServeChunker
from haiku.rag.client import HaikuRAG
from haiku.rag.config.models import ModelConfig, RerankingConfig
from haiku.rag.converters import get_converter
from haiku.rag.converters.docling_serve import DoclingServeConverter
from haiku.rag.embeddings import get_embedder
from haiku.rag.reranking import get_reranker
from haiku.rag.reranking.vllm import VLLMReranker
from haiku.rag.store.engine import Store
cfg = AppConfig()
cfg.processing.converter = "docling-serve"
cfg.processing.chunker = "docling-serve"
cfg.embeddings.model.vector_dim = 7
cfg.reranking.model = ModelConfig(
provider="vllm", name="reranker-x", base_url="http://localhost:9/v1"
)
assert isinstance(cfg.reranking, RerankingConfig)
_use_config(monkeypatch, cfg)
assert isinstance(get_converter(), DoclingServeConverter)
assert isinstance(get_chunker(), DoclingServeChunker)
assert get_embedder().vector_dim == 7
reranker = get_reranker()
assert isinstance(reranker, VLLMReranker)
assert reranker._model == "reranker-x"
assert HaikuRAG(tmp_path / "db")._config is cfg
assert Store(tmp_path / "db", create=True)._config is cfg

4
tests/test_context_enhancement.py
View file

@ -69,7 +69,7 @@ def create_code_document() -> DoclingDocument:
# Multiple adjacent code blocks - type-aware expansion should group them
doc.add_text(label=DocItemLabel.CODE, text="# Part 1: Setup\nimport os\nimport sys")
doc.add_text(
label=DocItemLabel.CODE, text='# Part 2: Config\nCONFIG = {"debug": True}'
label=DocItemLabel.CODE, text='# Part 2: get_config()\nCONFIG = {"debug": True}'
)
doc.add_text(
label=DocItemLabel.CODE, text="# Part 3: Main\ndef main():\n print(CONFIG)"
@ -81,7 +81,7 @@ def create_code_document() -> DoclingDocument:
@pytest.fixture
def small_chunk_config() -> AppConfig:
"""Config with small chunk size to force splitting."""
"""get_config() with small chunk size to force splitting."""
config = AppConfig()
config.processing.chunk_size = 32
config.search.max_context_chars = 10000

6
tests/test_download_models.py
View file

@ -5,7 +5,7 @@ import httpx
import pytest
from haiku.rag.client.downloads import download_models
from haiku.rag.config import Config
from haiku.rag.config import get_config
from haiku.rag.config.models import ModelConfig
@ -39,7 +39,7 @@ async def test_download_models_ollama_connect_error(mock_to_thread):
with pytest.raises(
ConnectionError, match="Cannot connect to Ollama"
) as exc_info:
async for _ in download_models(Config):
async for _ in download_models(get_config()):
pass
assert "ollama serve" in str(exc_info.value)
@ -74,7 +74,7 @@ async def test_download_models_ollama_pulls_models(mock_to_thread):
return_value=_mock_httpx_client(mock_stream),
):
events = []
async for progress in download_models(Config):
async for progress in download_models(get_config()):
events.append(progress)
# Default config has embeddings=qwen3-embedding:4b, qa=gpt-oss

8
tests/test_embedder.py
View file

@ -5,9 +5,9 @@ import pytest
from haiku.rag.config import (
AppConfig,
Config,
EmbeddingModelConfig,
EmbeddingsConfig,
get_config,
)
from haiku.rag.embeddings import (
EmbedderWrapper,
@ -197,7 +197,7 @@ async def test_embed_chunks_basic(allow_model_requests):
),
]
embedded_chunks = await embed_chunks(chunks, get_embedder(Config))
embedded_chunks = await embed_chunks(chunks, get_embedder(get_config()))
assert len(embedded_chunks) == 2
# Check that all original fields are preserved
@ -216,7 +216,7 @@ async def test_embed_chunks_basic(allow_model_requests):
async def test_embed_chunks_returns_new_objects(allow_model_requests):
"""Test that embed_chunks returns new Chunk objects (immutable pattern)."""
original = Chunk(id="orig", content="Test content.")
embedded = await embed_chunks([original], get_embedder(Config))
embedded = await embed_chunks([original], get_embedder(get_config()))
# Original should be unchanged
assert original.embedding is None
@ -302,7 +302,7 @@ async def test_embed_chunks_preserves_all_fields(allow_model_requests):
document_meta={"author": "Test"},
)
embedded = await embed_chunks([chunk], get_embedder(Config))
embedded = await embed_chunks([chunk], get_embedder(get_config()))
assert embedded[0].id == "test-id"
assert embedded[0].document_id == "doc-id"

28
tests/test_lancedb_connection.py
View file

@ -4,7 +4,7 @@ from unittest.mock import AsyncMock, patch
import pytest
from pydantic import ValidationError
from haiku.rag.config import Config
from haiku.rag.config import get_config
from haiku.rag.config.models import AppConfig, LanceDBConfig
from haiku.rag.store.engine import ConnectionMode, Store, connect_lancedb
@ -139,16 +139,16 @@ class TestStoreConnectionMode:
async def test_store_connection_mode_cloud(self, temp_db_path):
async with Store(temp_db_path, create=True) as store:
with (
patch.object(Config.lancedb, "uri", "db://test-database"),
patch.object(Config.lancedb, "api_key", "test-api-key"),
patch.object(Config.lancedb, "region", "us-east-1"),
patch.object(get_config().lancedb, "uri", "db://test-database"),
patch.object(get_config().lancedb, "api_key", "test-api-key"),
patch.object(get_config().lancedb, "region", "us-east-1"),
):
assert store._connection_mode == ConnectionMode.CLOUD
@pytest.mark.asyncio
async def test_store_connection_mode_object_storage(self, temp_db_path):
async with Store(temp_db_path, create=True) as store:
with patch.object(Config.lancedb, "uri", "s3://bucket/path"):
with patch.object(get_config().lancedb, "uri", "s3://bucket/path"):
assert store._connection_mode == ConnectionMode.OBJECT_STORAGE
@ -157,9 +157,9 @@ class TestVacuumByConnectionMode:
async def test_cloud_skips_vacuum(self, temp_db_path):
async with Store(temp_db_path, create=True) as store:
with (
patch.object(Config.lancedb, "uri", "db://test-database"),
patch.object(Config.lancedb, "api_key", "test-api-key"),
patch.object(Config.lancedb, "region", "us-east-1"),
patch.object(get_config().lancedb, "uri", "db://test-database"),
patch.object(get_config().lancedb, "api_key", "test-api-key"),
patch.object(get_config().lancedb, "region", "us-east-1"),
):
with patch.object(
store.chunks_table, "optimize", new_callable=AsyncMock
@ -170,7 +170,7 @@ class TestVacuumByConnectionMode:
@pytest.mark.asyncio
async def test_object_storage_runs_vacuum(self, temp_db_path):
async with Store(temp_db_path, create=True) as store:
with patch.object(Config.lancedb, "uri", "s3://bucket/path"):
with patch.object(get_config().lancedb, "uri", "s3://bucket/path"):
with patch.object(
store.chunks_table, "optimize", new_callable=AsyncMock
) as mock_optimize:
@ -180,7 +180,7 @@ class TestVacuumByConnectionMode:
@pytest.mark.asyncio
async def test_local_runs_vacuum(self, temp_db_path):
async with Store(temp_db_path, create=True) as store:
with patch.object(Config.lancedb, "uri", ""):
with patch.object(get_config().lancedb, "uri", ""):
with patch.object(
store.chunks_table, "optimize", new_callable=AsyncMock
) as mock_optimize:
@ -193,9 +193,9 @@ class TestVectorIndexByConnectionMode:
async def test_cloud_skips_index_creation(self, temp_db_path):
async with Store(temp_db_path, create=True) as store:
with (
patch.object(Config.lancedb, "uri", "db://test-database"),
patch.object(Config.lancedb, "api_key", "test-api-key"),
patch.object(Config.lancedb, "region", "us-east-1"),
patch.object(get_config().lancedb, "uri", "db://test-database"),
patch.object(get_config().lancedb, "api_key", "test-api-key"),
patch.object(get_config().lancedb, "region", "us-east-1"),
):
with patch.object(
store.chunks_table, "count_rows", new_callable=AsyncMock
@ -206,7 +206,7 @@ class TestVectorIndexByConnectionMode:
@pytest.mark.asyncio
async def test_object_storage_runs_index_creation(self, temp_db_path):
async with Store(temp_db_path, create=True) as store:
with patch.object(Config.lancedb, "uri", "s3://bucket/path"):
with patch.object(get_config().lancedb, "uri", "s3://bucket/path"):
with patch.object(
store.chunks_table,
"count_rows",

4
tests/test_mcp.py
View file

@ -644,10 +644,10 @@ class TestMCPClientLifetime:
"""Validation is unchanged: same-dimension identity drift warns in
read-only mode and raises in writable mode. The MCP server no longer
opts out of it for deletion."""
from haiku.rag.config import Config
from haiku.rag.config import get_config
from haiku.rag.store.repositories.settings import ConfigMismatchError
drifted = Config.model_copy(deep=True)
drifted = get_config().model_copy(deep=True)
drifted.embeddings.model.name = "a-different-model"
async with create_mcp_server(

4
tests/test_picture_in_context.py
View file

@ -15,7 +15,7 @@ from pydantic_ai.usage import RunUsage
from haiku.rag.capabilities.rag import RAGState, create_capability
from haiku.rag.client import HaikuRAG
from haiku.rag.client.search import _populate_image_data
from haiku.rag.config import AppConfig, Config
from haiku.rag.config import AppConfig, get_config
from haiku.rag.store.models.chunk import Chunk, SearchResult
from haiku.rag.store.models.document_item import DocumentItem
from haiku.rag.tools.search import create_search_toolset
@ -980,7 +980,7 @@ async def test_search_tool_returns_plain_string_when_no_pictures():
fake_client.search = AsyncMock(return_value=[text_result])
fake_client.expand_context = AsyncMock(return_value=[text_result])
toolset = create_search_toolset(Config, expand_context=False)
toolset = create_search_toolset(get_config(), expand_context=False)
func = toolset.tools["search"].function
ctx = RunContext(

4
tests/test_reader.py
View file

@ -3,7 +3,7 @@ from pathlib import Path
import pytest
from haiku.rag.config import Config
from haiku.rag.config import get_config
from haiku.rag.converters import get_converter
@ -19,7 +19,7 @@ async def test_code_file_wrapped_in_code_block():
f.flush()
temp_path = Path(f.name)
converter = get_converter(Config)
converter = get_converter(get_config())
document = await converter.convert_file(temp_path)
result = document.export_to_markdown()

6
tests/test_rebuild.py
View file

@ -6,7 +6,7 @@ from typing import TypedDict
import pytest
from haiku.rag.client import HaikuRAG, RebuildMode
from haiku.rag.config import Config
from haiku.rag.config import get_config
from tests.conftest import capture_logs
@ -194,7 +194,7 @@ async def test_rebuild_resumes_phase2_from_staging_after_crash(
# crash, where no background vacuum would be in flight. Leaving it on lets
# create_document's scheduled optimize race the raw drop_table ("Directory
# not empty").
config = Config.model_copy(deep=True)
config = get_config().model_copy(deep=True)
config.storage.auto_vacuum = False
async with HaikuRAG(temp_db_path, config=config, create=True) as client:
@ -1093,7 +1093,7 @@ async def test_rebuild_blocks_tag_operations(temp_db_path, monkeypatch):
monkeypatch.setattr(EmbedderWrapper, "embed_documents", fake_embed_documents)
dim = Config.embeddings.model.vector_dim
dim = get_config().embeddings.model.vector_dim
docling_doc = DoclingDocument(name="d")
docling_doc.add_text(label=DocItemLabel.TEXT, text="body")

12
tests/test_reranker.py
View file

@ -38,11 +38,9 @@ chunks = [
@pytest.mark.asyncio
async def test_reranker_base():
from haiku.rag.config import Config
reranker = RerankerBase()
expected_model = Config.reranking.model.name if Config.reranking.model else None
assert reranker._model == expected_model
# The base carries no model: each reranker takes its own from the factory.
assert reranker._model is None
# Empty input short-circuits in the base class without dispatching to _rerank.
assert await reranker.rerank("query", []) == []
@ -156,12 +154,12 @@ class TestGetReranker:
[
(
"cohere",
"rerank-v3.5",
"rerank-english-v3.0",
"haiku.rag.reranking.cohere",
"CohereReranker",
{},
{},
{},
{"_model": "rerank-english-v3.0"},
{"CO_API_KEY": "test-api-key"},
),
(
"vllm",

22
tests/test_search.py
View file

@ -1,14 +1,16 @@
import pytest
from haiku.rag.client import HaikuRAG
from haiku.rag.config import Config
from haiku.rag.config import get_config
from haiku.rag.store.models import SearchResult
@pytest.mark.vcr()
async def test_search_qa_corpus(qa_corpus: list[dict[str, str]], temp_db_path):
"""Test that documents can be found by searching with their associated questions."""
async with HaikuRAG(db_path=temp_db_path, config=Config, create=True) as client:
async with HaikuRAG(
db_path=temp_db_path, config=get_config(), create=True
) as client:
# Load unique documents (limited to 10)
seen_documents = set()
documents = []
@ -57,7 +59,9 @@ async def test_search_qa_corpus(qa_corpus: list[dict[str, str]], temp_db_path):
@pytest.mark.vcr()
async def test_search_chunk_includes_document_provenance(temp_db_path):
"""Test that raw chunk search results include document URI, metadata, and ID."""
async with HaikuRAG(db_path=temp_db_path, config=Config, create=True) as client:
async with HaikuRAG(
db_path=temp_db_path, config=get_config(), create=True
) as client:
# Create a document with URI and metadata but no title
created_document = await client.create_document(
content="This is a test document with some content for searching.",
@ -92,7 +96,9 @@ async def test_search_chunk_includes_document_provenance(temp_db_path):
@pytest.mark.vcr()
async def test_search_score_types(temp_db_path):
"""Test that different search types return appropriate score ranges."""
async with HaikuRAG(db_path=temp_db_path, config=Config, create=True) as client:
async with HaikuRAG(
db_path=temp_db_path, config=get_config(), create=True
) as client:
# Create multiple documents with different content
documents_content = [
"Machine learning algorithms are powerful tools for data analysis and pattern recognition.",
@ -159,7 +165,9 @@ async def test_search_score_types(temp_db_path):
@pytest.mark.vcr()
async def test_search_returns_search_result(temp_db_path):
"""Test that client.search() returns SearchResult with provenance info."""
async with HaikuRAG(db_path=temp_db_path, config=Config, create=True) as client:
async with HaikuRAG(
db_path=temp_db_path, config=get_config(), create=True
) as client:
await client.create_document(
content="Machine learning models can classify images with high accuracy.",
uri="https://example.com/ml.html",
@ -188,7 +196,9 @@ async def test_search_graceful_degradation(temp_db_path):
"""Test search works when docling data is unavailable."""
from haiku.rag.store.models import Chunk
async with HaikuRAG(db_path=temp_db_path, config=Config, create=True) as client:
async with HaikuRAG(
db_path=temp_db_path, config=get_config(), create=True
) as client:
# Import document with custom chunks (no docling document)
custom_chunks = [
Chunk(content="Custom chunk without docling metadata", metadata={}),

12
tests/test_settings.py
View file

@ -2,7 +2,7 @@ import logging
import pytest
from haiku.rag.config import AppConfig, Config
from haiku.rag.config import AppConfig, get_config
from haiku.rag.store.repositories.settings import ConfigMismatchError
@ -16,7 +16,7 @@ async def test_settings_table_populated_on_store_init(temp_db_path):
settings_repo = SettingsRepository(store)
db_settings = await settings_repo.get_current_settings()
config_dict = Config.model_dump(mode="json")
config_dict = get_config().model_dump(mode="json")
# Remove version from db_settings since it's added automatically
db_settings_without_version = {
@ -34,14 +34,14 @@ async def test_settings_save_and_retrieve(temp_db_path):
async with Store(temp_db_path, create=True) as store:
settings_repo = SettingsRepository(store)
original_chunk_size = Config.processing.chunk_size
Config.processing.chunk_size = 2 * original_chunk_size
original_chunk_size = get_config().processing.chunk_size
get_config().processing.chunk_size = 2 * original_chunk_size
await settings_repo.save_current_settings()
retrieved_settings = await settings_repo.get_current_settings()
assert retrieved_settings["processing"]["chunk_size"] == 2 * original_chunk_size
Config.processing.chunk_size = original_chunk_size
get_config().processing.chunk_size = original_chunk_size
@pytest.mark.asyncio
@ -52,7 +52,7 @@ async def test_set_haiku_version_recreates_row_from_store_config(temp_db_path):
from haiku.rag.store.repositories.settings import SettingsRepository
config = AppConfig()
config.processing.chunk_size = Config.processing.chunk_size + 512
config.processing.chunk_size = get_config().processing.chunk_size + 512
async with Store(temp_db_path, config=config, create=True) as store:
settings_repo = SettingsRepository(store)

12
tests/test_utils.py
View file

@ -3,7 +3,7 @@ import importlib.util
import pytest
from pydantic_ai.models.openai import OpenAIChatModel
from haiku.rag.config import Config
from haiku.rag.config import get_config
from haiku.rag.config.models import ModelConfig
from haiku.rag.converters import get_converter
from haiku.rag.utils import get_model
@ -20,7 +20,7 @@ async def test_text_to_docling_document():
"""Test text to DoclingDocument conversion."""
# Test basic text conversion
simple_text = "This is a simple text document."
converter = get_converter(Config)
converter = get_converter(get_config())
doc = await converter.convert_text(simple_text)
# Verify it returns a DoclingDocument
@ -44,7 +44,7 @@ def hello():
return True
```"""
converter = get_converter(Config)
converter = get_converter(get_config())
doc = await converter.convert_text(code_text, name="hello.md")
# Verify it's a valid DoclingDocument
@ -77,7 +77,7 @@ def test():
**Bold text** and *italic text*."""
converter = get_converter(Config)
converter = get_converter(get_config())
doc = await converter.convert_text(markdown_text, name="test.md")
# Verify it's a DoclingDocument
@ -95,7 +95,7 @@ def test():
@pytest.mark.asyncio
async def test_text_to_docling_document_empty_content():
"""Test text to DoclingDocument conversion with empty content."""
converter = get_converter(Config)
converter = get_converter(get_config())
doc = await converter.convert_text("")
# Should still create a valid DoclingDocument
@ -124,7 +124,7 @@ function saludar() {
Emoji test: 🚀 ✅ 📝"""
converter = get_converter(Config)
converter = get_converter(get_config())
doc = await converter.convert_text(unicode_text, name="unicode.md")
# Verify it's a DoclingDocument

6
tests/test_vacuum_debounce.py
View file

@ -5,7 +5,7 @@ import pytest
import haiku.rag.client as client_mod
from haiku.rag.client import HaikuRAG
from haiku.rag.client.documents import _refresh_doc_metadata
from haiku.rag.config import Config
from haiku.rag.config import get_config
from haiku.rag.store.models.chunk import Chunk
@ -73,7 +73,7 @@ async def test_metadata_refresh_sweep_schedules_vacuum(temp_db_path):
"""A source re-sweep that only rolls source_revision (MD5/revision
short-circuit) writes document_meta and must still schedule the (debounced)
vacuum, so that tiny churn gets reclaimed instead of accumulating."""
dim = Config.embeddings.model.vector_dim
dim = get_config().embeddings.model.vector_dim
async with HaikuRAG(temp_db_path, create=True) as client:
doc = await client.import_document(
_docling_doc("d", "body"),
@ -99,7 +99,7 @@ async def test_metadata_refresh_waits_for_write_lock(temp_db_path):
"""The revision/MD5 short-circuit write serializes with other writers so
it cannot land inside another writer's critical section (e.g. between
create_tag's version snapshot and its per-table tag creation)."""
dim = Config.embeddings.model.vector_dim
dim = get_config().embeddings.model.vector_dim
async with HaikuRAG(temp_db_path, create=True) as client:
doc = await client.import_document(
_docling_doc("d", "body"),

22
tests/test_versioning.py
View file

@ -271,10 +271,10 @@ async def test_vacuum_with_retention_threshold(temp_db_path):
@pytest.mark.vcr()
async def test_vacuum_completes_before_context_exit(temp_db_path, monkeypatch):
"""Test that background vacuum completes when context manager exits."""
from haiku.rag.config import Config
from haiku.rag.config import get_config
# Set aggressive vacuum retention for this test
monkeypatch.setattr(Config.storage, "vacuum_retention_seconds", 0)
monkeypatch.setattr(get_config().storage, "vacuum_retention_seconds", 0)
async with HaikuRAG(db_path=temp_db_path, create=True) as client:
# Create multiple documents - each creation triggers automatic vacuum with retention=0
@ -302,9 +302,9 @@ async def test_aexit_awaits_background_vacuum(temp_db_path, monkeypatch):
it yet when __aexit__ runs. Simply acquiring the vacuum lock (which is free until
the task actually starts) would let close() proceed before vacuum runs.
"""
from haiku.rag.config import Config
from haiku.rag.config import get_config
monkeypatch.setattr(Config.storage, "auto_vacuum", True)
monkeypatch.setattr(get_config().storage, "auto_vacuum", True)
vacuum_started = asyncio.Event()
vacuum_completed = asyncio.Event()
@ -339,9 +339,9 @@ async def test_aexit_awaits_all_background_vacuums(temp_db_path, monkeypatch):
__aexit__ awaits the fast no-op B and closes the connection while Task A
is still running.
"""
from haiku.rag.config import Config
from haiku.rag.config import get_config
monkeypatch.setattr(Config.storage, "auto_vacuum", True)
monkeypatch.setattr(get_config().storage, "auto_vacuum", True)
first_vacuum_completed = asyncio.Event()
@ -378,10 +378,10 @@ async def test_aexit_awaits_all_background_vacuums(temp_db_path, monkeypatch):
@pytest.mark.vcr()
async def test_auto_vacuum_disabled_skips_vacuum(temp_db_path, monkeypatch):
"""Test that auto_vacuum=False prevents automatic vacuum after operations."""
from haiku.rag.config import Config
from haiku.rag.config import get_config
# Disable auto-vacuum
monkeypatch.setattr(Config.storage, "auto_vacuum", False)
monkeypatch.setattr(get_config().storage, "auto_vacuum", False)
async with HaikuRAG(db_path=temp_db_path, create=True) as client:
# Create multiple documents
@ -404,11 +404,11 @@ async def test_auto_vacuum_disabled_skips_vacuum(temp_db_path, monkeypatch):
@pytest.mark.vcr()
async def test_auto_vacuum_enabled_triggers_vacuum(temp_db_path, monkeypatch):
"""Test that auto_vacuum=True (default) triggers vacuum after operations."""
from haiku.rag.config import Config
from haiku.rag.config import get_config
# Enable auto-vacuum with aggressive retention
monkeypatch.setattr(Config.storage, "auto_vacuum", True)
monkeypatch.setattr(Config.storage, "vacuum_retention_seconds", 0)
monkeypatch.setattr(get_config().storage, "auto_vacuum", True)
monkeypatch.setattr(get_config().storage, "vacuum_retention_seconds", 0)
async with HaikuRAG(db_path=temp_db_path, create=True) as client:
# Create multiple documents

4
tests/tools/test_document.py
View file

@ -238,6 +238,6 @@ async def doc_client(temp_db_path):
@pytest.fixture
def doc_config():
"""Default AppConfig for document tests."""
from haiku.rag.config import Config
from haiku.rag.config import get_config
return Config
return get_config()

4
tests/tools/test_search.py
View file

@ -205,9 +205,9 @@ async def search_client(temp_db_path):
@pytest.fixture
def search_config():
"""Default AppConfig for search tests."""
from haiku.rag.config import Config
from haiku.rag.config import get_config
return Config
return get_config()
def _png_b64():