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from types import SimpleNamespace
import pytest
from src.database.embeddings import EmbeddingError
from src.database import weavservice
from src.database.weavservice import WeaviateService
from src.rag.agent_chain import ExecutiveAgentChain
from src.rag.models import ModelConfigurator
from src.config import config
class FakeDbService:
def __init__(self):
self.calls = []
def query(self, **kwargs):
self.calls.append(kwargs)
doc = SimpleNamespace(
properties={
"body": "emba X context",
"programs": ["emba_x"],
"source": "fake-source",
}
)
return SimpleNamespace(objects=[doc]), 0.01
def test_retrieve_context_filters_embax_with_canonical_programme_id():
agent = object.__new__(ExecutiveAgentChain)
agent._initial_language = "en"
agent._dbservice = FakeDbService()
result = agent._retrieve_context("admissions requirements", "emba X", "en")
assert "emba X context" in result
assert agent._dbservice.calls[0]["property_filters"] == {"programs": ["emba_x"]}
class FakeQuery:
def __init__(self):
self.hybrid_calls = []
self.bm25_calls = []
def hybrid(self, **kwargs):
self.hybrid_calls.append(kwargs)
return SimpleNamespace(objects=[])
def bm25(self, **kwargs):
self.bm25_calls.append(kwargs)
return SimpleNamespace(objects=[])
class FakeCollections:
def __init__(self, collection):
self.collection = collection
def exists(self, name):
self.exists_name = name
return True
def get(self, name):
self.get_name = name
return self.collection
class FakeEmbeddingClient:
def __init__(self, vector=None, fail=False):
self.vector = vector or [0.1, 0.2, 0.3]
self.fail = fail
self.document_inputs = []
self.query_inputs = []
def embed_documents(self, texts):
self.document_inputs.append(list(texts))
if self.fail:
raise EmbeddingError("embedding service unavailable")
return [self.vector for _ in self.document_inputs[-1]]
def embed_query(self, text):
self.query_inputs.append(text)
if self.fail:
raise EmbeddingError("embedding service unavailable")
return self.vector
def test_weaviate_keep_warm_once_runs_hybrid_warmup():
collection = SimpleNamespace(query=FakeQuery())
client = SimpleNamespace(collections=FakeCollections(collection))
service = object.__new__(WeaviateService)
service._client = client
service._client_lock = RLock()
service._last_query_time = 0
service._keep_warm_interval = 1
service._embedding_client = FakeEmbeddingClient(vector=[0.4, 0.5, 0.6])
assert service._keep_warm_once() is True
assert collection.query.hybrid_calls[0]["query"] == "HSG"
assert collection.query.hybrid_calls[0]["limit"] == 1
assert collection.query.hybrid_calls[0]["vector"] == [0.4, 0.5, 0.6]
assert collection.query.hybrid_calls[0]["target_vector"] == config.processing.EMBEDDING_VECTOR_NAME
def test_embedding_config_defaults_to_openrouter_small_model():
assert config.processing.EMBEDDING_MODEL == "openai/text-embedding-3-small"
assert config.processing.EMBEDDING_BASE_URL == "https://openrouter.ai/api/v1"
assert config.processing.EMBEDDING_DIMENSIONS == 1536
assert config.processing.EMBEDDING_BATCH_SIZE == 32
assert config.processing.MAX_TOKENS == 512
def test_processor_uses_embedding_model_tokenizer(monkeypatch):
processors = pytest.importorskip("src.pipeline.processors")
calls = []
class FakeEncoding:
def encode(self, text, **kwargs):
return [1, 2]
def decode(self, tokens):
return "decoded"
class FakeHybridChunker:
def __init__(self, **kwargs):
self.kwargs = kwargs
monkeypatch.setattr(
processors.tiktoken,
"encoding_for_model",
lambda model: calls.append(model) or FakeEncoding(),
)
monkeypatch.setattr(processors, "HybridChunker", FakeHybridChunker)
monkeypatch.setattr(processors, "EnhansedSerializerProvider", lambda: object())
processor = object.__new__(processors.ProcessorBase)
processor._chunker_instance = None
chunker = processors.ProcessorBase._chunker.fget(processor)
assert calls == ["text-embedding-3-small"]
assert chunker.kwargs["max_tokens"] == config.processing.MAX_TOKENS
assert chunker.kwargs["tokenizer"].count_tokens("test") == 2
def test_weaviate_vector_config_uses_self_provided_for_openrouter(monkeypatch):
monkeypatch.setattr(config.processing, "EMBEDDING_VECTOR_NAME", "test_vectors")
monkeypatch.setattr(
weavservice.Configure.Vectors,
"self_provided",
lambda name: ("self_provided", name),
)
service = object.__new__(WeaviateService)
assert service._vector_config() == ("self_provided", "test_vectors")
class FakeBatchContext:
def __init__(self):
self.added = []
self.number_errors = 0
def __enter__(self):
return self
def __exit__(self, exc_type, exc, tb):
return False
def add_object(self, properties, vector=None, uuid=None):
self.added.append({"properties": properties, "vector": vector, "uuid": uuid})
class FakeBatchFactory:
def __init__(self, context):
self.context = context
def fixed_size(self, **kwargs):
self.kwargs = kwargs
return self.context
def test_batch_import_embeds_rows_and_writes_named_vectors(monkeypatch):
monkeypatch.setattr(config.processing, "EMBEDDING_VECTOR_NAME", "test_vectors")
batch_context = FakeBatchContext()
collection = SimpleNamespace(batch=FakeBatchFactory(batch_context))
service = object.__new__(WeaviateService)
service._client_lock = RLock()
service._last_query_time = 0
service._embedding_client = FakeEmbeddingClient(vector=[0.7, 0.8, 0.9])
service._select_collection = lambda lang: (collection, "test_collection")
errors = service.batch_import(
data_rows=[{"chunk_id": "c1", "body": "First chunk"}],
lang="en",
)
assert errors == []
assert service._embedding_client.document_inputs == [["First chunk"]]
assert batch_context.added[0]["vector"] == {"test_vectors": [0.7, 0.8, 0.9]}
assert batch_context.added[0]["uuid"]
def test_query_embeds_once_and_passes_vector_to_hybrid(monkeypatch):
monkeypatch.setattr(config.processing, "EMBEDDING_VECTOR_NAME", "test_vectors")
collection = SimpleNamespace(query=FakeQuery())
service = object.__new__(WeaviateService)
service._client_lock = RLock()
service._last_query_time = 0
service._embedding_client = FakeEmbeddingClient(vector=[0.2, 0.3, 0.4])
service._select_collection = lambda lang: (collection, "test_collection")
service.query(query="admissions", lang="en", limit=3)
assert service._embedding_client.query_inputs == ["admissions"]
assert collection.query.hybrid_calls[0]["vector"] == [0.2, 0.3, 0.4]
assert collection.query.hybrid_calls[0]["target_vector"] == "test_vectors"
assert collection.query.hybrid_calls[0]["limit"] == 3
def test_query_falls_back_to_bm25_when_embedding_fails(monkeypatch):
collection = SimpleNamespace(query=FakeQuery())
service = object.__new__(WeaviateService)
service._client_lock = RLock()
service._last_query_time = 0
service._embedding_client = FakeEmbeddingClient(fail=True)
service._select_collection = lambda lang: (collection, "test_collection")
service.query(query="admissions", lang="en", limit=3)
assert collection.query.hybrid_calls == []
assert collection.query.bm25_calls[0]["query"] == "admissions"
assert collection.query.bm25_calls[0]["limit"] == 3
def test_model_config_uses_openrouter_defaults_and_budgets(monkeypatch):
calls = []
def fake_initialize_model(cls, provider, model, role="main"):
calls.append((provider, model, role))
return object()
monkeypatch.setattr(ModelConfigurator, "_initialize_model", classmethod(fake_initialize_model))
ModelConfigurator._main_model_instance = None
ModelConfigurator._language_detector_model_instance = None
ModelConfigurator._confidence_scoring_model_instance = None
ModelConfigurator._fallback_models_instances = None
ModelConfigurator.get_main_agent_model()
ModelConfigurator.get_language_detector_model()
ModelConfigurator.get_confidence_scoring_model()
ModelConfigurator.get_fallback_models()
# All roles run through OpenRouter so the app no longer needs a funded
# OpenAI account.
assert config.llm.MAIN_AGENT_MODEL == ("open_router:openai", "openai/gpt-4.1")
assert config.llm.FALLBACK_MODELS == [("open_router:openai", "openai/gpt-4o-mini")]
assert ModelConfigurator._openai_budget("main") == {
"max_tokens": 3072,
"timeout": 30,
"request_timeout": 30,
}
assert ModelConfigurator._openai_budget("language_detector") == {
"max_tokens": 64,
"timeout": 10,
"request_timeout": 10,
}
assert ("open_router:openai", "openai/gpt-4.1", "main") in calls
assert ("open_router:openai", "openai/gpt-4o-mini", "language_detector") in calls
assert ("open_router:openai", "openai/gpt-4o-mini", "confidence_scoring") in calls
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