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Alex
574f96341e
The backend import package is now docsgpt, the name it will carry on PyPI; application was far too generic to install into anyone's site-packages. git mv plus a mechanical rewrite of every import, dotted string and path reference: 734 Python files, the compose files, Dockerfile, workflows, docs, setup scripts, devcontainer, k8s manifests, vscode config, pytest and coverage config, .gitignore. Behaviour is unchanged. Kept for one release: - A top-level application package whose meta-path finder resolves application.x.y to the already-imported docsgpt.x.y object, so old imports and entry points (celery -A application.app.celery, uvicorn application.asgi:asgi_app) keep working with a FutureWarning. - Celery registers every application.* task name as an alias of its docsgpt.* task on start-up, so messages queued by the previous release still run. The redbeat key prefix moves to redbeat:docsgpt:v2: so schedule entries the previous release wrote are left unread instead of firing twice. The backend image builds from the repository root (docker build -f docsgpt/Dockerfile .) so it can ship the alias package; a root .dockerignore allow-lists docsgpt/ and application/ and keeps caches, local data, .env files, the sample index files and the Dockerfile out. Compose and the image workflows point at the new context.
1541 lines
54 KiB
Python
1541 lines
54 KiB
Python
"""Unit tests for docsgpt/llm/google_ai.py — GoogleLLM.
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Extends coverage beyond test_google_llm.py:
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- _clean_messages_google: system instructions, function responses, errors
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- _clean_schema: field filtering, type uppercasing, required validation
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- _clean_tools_format: empty properties, required fields
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- _extract_preview_from_message: various message shapes
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- _summarize_messages_for_log
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- _get_text_value / _is_thought_part: dict vs object forms
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- _raw_gen with tools and response_schema
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- _raw_gen_stream: function_call parts, thought parts, error handling
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- prepare_structured_output_format: comprehensive type mapping
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- prepare_messages_with_attachments: error handling
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- _upload_file_to_google
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- get_supported_attachment_types
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"""
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import types
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import pytest
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from docsgpt.llm.google_ai import GoogleLLM
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# ---------------------------------------------------------------------------
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# Fake types module for Google AI
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# ---------------------------------------------------------------------------
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class _FakePart:
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def __init__(self, text=None, function_call=None, file_data=None, inline_data=None, thought=False, **kwargs):
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self.text = text
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self.function_call = function_call or kwargs.get("functionCall")
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self.file_data = file_data
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self.inline_data = inline_data
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self.thought = thought
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self.thoughtSignature = kwargs.get("thoughtSignature")
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@staticmethod
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def from_text(text):
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return _FakePart(text=text)
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@staticmethod
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def from_function_call(name, args):
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return _FakePart(function_call=types.SimpleNamespace(name=name, args=args))
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@staticmethod
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def from_function_response(name, response):
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return _FakePart(text=str(response))
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@staticmethod
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def from_uri(file_uri, mime_type):
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return _FakePart(
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file_data=types.SimpleNamespace(file_uri=file_uri, mime_type=mime_type)
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)
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@staticmethod
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def from_bytes(data, mime_type):
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return _FakePart(
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inline_data=types.SimpleNamespace(data=data, mime_type=mime_type)
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)
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class _FakeContent:
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def __init__(self, role, parts):
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self.role = role
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self.parts = parts
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class FakeTypesModule:
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Part = _FakePart
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Content = _FakeContent
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class GenerateContentConfig:
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def __init__(self, thinking_config=None, **_kw):
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self.system_instruction = None
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self.tools = None
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self.thinking_config = thinking_config
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self.response_schema = None
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self.response_mime_type = None
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class ThinkingConfig:
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def __init__(self, include_thoughts=False, thinking_level=None):
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self.include_thoughts = include_thoughts
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self.thinking_level = thinking_level
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class Tool:
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def __init__(self, function_declarations=None):
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self.function_declarations = function_declarations or []
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class FunctionCall:
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def __init__(self, name=None, args=None):
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self.name = name
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self.args = args
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class FakeModels:
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def __init__(self):
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self.last_kwargs = None
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class _Resp:
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def __init__(self, text=None, candidates=None):
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self.text = text
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self.candidates = candidates or []
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def generate_content(self, *args, **kwargs):
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self.last_kwargs = kwargs
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return FakeModels._Resp(text="ok")
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def generate_content_stream(self, *args, **kwargs):
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self.last_kwargs = kwargs
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return []
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class FakeClientFiles:
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def upload(self, file=None):
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return types.SimpleNamespace(uri="gs://fake-uri")
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class FakeClient:
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def __init__(self, *a, **kw):
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self.models = FakeModels()
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self.files = FakeClientFiles()
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@pytest.fixture(autouse=True)
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def patch_google(monkeypatch):
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import docsgpt.llm.google_ai as gmod
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monkeypatch.setattr(gmod, "types", FakeTypesModule)
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monkeypatch.setattr(gmod.genai, "Client", FakeClient)
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@pytest.fixture
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def llm():
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instance = GoogleLLM(api_key="test-key")
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instance.storage = types.SimpleNamespace(
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file_exists=lambda p: True,
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process_file=lambda path, fn, **kw: fn(path),
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)
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return instance
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# ---------------------------------------------------------------------------
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# _clean_messages_google
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# ---------------------------------------------------------------------------
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@pytest.mark.unit
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class TestCleanMessagesGoogle:
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def test_system_message_extracted_as_instruction(self, llm):
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msgs = [
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{"role": "system", "content": "You are helpful"},
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{"role": "user", "content": "hi"},
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]
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cleaned, sys_instr = llm._clean_messages_google(msgs)
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assert sys_instr == "You are helpful"
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assert all(c.role != "system" for c in cleaned)
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def test_multiple_system_messages_joined(self, llm):
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msgs = [
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{"role": "system", "content": "Rule 1"},
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{"role": "system", "content": "Rule 2"},
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{"role": "user", "content": "hi"},
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]
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_, sys_instr = llm._clean_messages_google(msgs)
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assert "Rule 1" in sys_instr
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assert "Rule 2" in sys_instr
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def test_system_list_content(self, llm):
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msgs = [
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{"role": "system", "content": [{"text": "A"}, {"text": "B"}]},
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{"role": "user", "content": "hi"},
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]
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_, sys_instr = llm._clean_messages_google(msgs)
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assert "A" in sys_instr and "B" in sys_instr
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def test_assistant_role_becomes_model(self, llm):
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msgs = [{"role": "assistant", "content": "hi"}]
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cleaned, _ = llm._clean_messages_google(msgs)
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assert cleaned[0].role == "model"
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def test_tool_role_becomes_model(self, llm):
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msgs = [{"role": "tool", "content": "result"}]
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cleaned, _ = llm._clean_messages_google(msgs)
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assert cleaned[0].role == "model"
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def test_function_call_in_content_list(self, llm):
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msgs = [
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{
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"role": "assistant",
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"content": [
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{"function_call": {"name": "fn", "args": {"x": 1}}},
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],
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}
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]
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cleaned, _ = llm._clean_messages_google(msgs)
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assert len(cleaned) == 1
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assert any(
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hasattr(p, "function_call") and p.function_call is not None
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for p in cleaned[0].parts
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)
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def test_function_response_in_content_list(self, llm):
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msgs = [
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{
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"role": "assistant",
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"content": [
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{
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"function_response": {
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"name": "fn",
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"response": {"result": 42},
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}
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},
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],
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}
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]
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cleaned, _ = llm._clean_messages_google(msgs)
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assert len(cleaned) == 1
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def test_files_in_content_list(self, llm):
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msgs = [
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{
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"role": "user",
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"content": [
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{"files": [{"file_uri": "gs://f", "mime_type": "image/png"}]},
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],
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}
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]
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cleaned, _ = llm._clean_messages_google(msgs)
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assert len(cleaned) == 1
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assert any(
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hasattr(p, "file_data") and p.file_data is not None
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for p in cleaned[0].parts
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)
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def test_files_with_inline_bytes(self, llm):
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msgs = [
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{
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"role": "user",
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"content": [
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{
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"files": [
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{"file_bytes": b"\x89PNG", "mime_type": "image/png"}
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]
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},
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],
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}
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]
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cleaned, _ = llm._clean_messages_google(msgs)
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assert len(cleaned) == 1
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inline_parts = [
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p for p in cleaned[0].parts
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if getattr(p, "inline_data", None) is not None
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]
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assert len(inline_parts) == 1
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assert inline_parts[0].inline_data.data == b"\x89PNG"
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assert inline_parts[0].inline_data.mime_type == "image/png"
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def test_files_with_empty_uri_dropped(self, llm):
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msgs = [
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{
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"role": "user",
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"content": [
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{"files": [{"file_uri": "", "mime_type": "image/png"}]},
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],
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}
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]
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cleaned, _ = llm._clean_messages_google(msgs)
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# Empty URI part is dropped; no other parts means the whole
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# content is empty and the message itself is not appended.
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assert cleaned == []
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def test_unexpected_list_item_raises(self, llm):
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msgs = [{"role": "user", "content": [{"unknown_key": "val"}]}]
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with pytest.raises(ValueError, match="Unexpected content dictionary"):
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llm._clean_messages_google(msgs)
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def test_unexpected_content_type_raises(self, llm):
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msgs = [{"role": "user", "content": 12345}]
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with pytest.raises(ValueError, match="Unexpected content type"):
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llm._clean_messages_google(msgs)
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def test_no_system_instruction_returns_none(self, llm):
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msgs = [{"role": "user", "content": "hi"}]
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_, sys_instr = llm._clean_messages_google(msgs)
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assert sys_instr is None
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def test_empty_parts_skipped(self, llm):
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msgs = [{"role": "user", "content": None}]
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cleaned, _ = llm._clean_messages_google(msgs)
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assert len(cleaned) == 0
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# ---------------------------------------------------------------------------
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# _clean_schema
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# ---------------------------------------------------------------------------
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@pytest.mark.unit
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class TestCleanSchema:
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def test_type_uppercased(self, llm):
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result = llm._clean_schema({"type": "string"})
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assert result["type"] == "STRING"
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def test_unsupported_fields_removed(self, llm):
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result = llm._clean_schema({"type": "string", "title": "Name", "$ref": "#/x"})
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assert "title" not in result
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assert "$ref" not in result
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assert result["type"] == "STRING"
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def test_nested_properties_cleaned(self, llm):
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# _clean_schema recursively cleans the properties dict value.
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# Property names that happen to match allowed_fields survive.
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# This tests the recursive cleaning on schema values.
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schema = {
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"type": "object",
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"properties": {
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"type": {"type": "string"},
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},
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}
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result = llm._clean_schema(schema)
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# "type" is in allowed_fields, so the property survives as a key
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# Its value gets uppercased since it's a type field
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assert "properties" in result
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assert result["properties"]["type"]["type"] == "STRING"
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def test_required_validated_against_properties(self, llm):
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# Property names must be in allowed_fields to survive _clean_schema
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# "type" is in allowed_fields so it survives as a property key
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schema = {
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"type": "object",
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"properties": {"type": {"type": "string"}},
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"required": ["type", "nonexistent"],
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}
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result = llm._clean_schema(schema)
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assert result["required"] == ["type"]
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def test_required_removed_when_no_valid_entries(self, llm):
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schema = {
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"type": "object",
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"properties": {"type": {"type": "string"}},
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"required": ["nonexistent"],
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}
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result = llm._clean_schema(schema)
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assert "required" not in result
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def test_required_removed_when_no_properties(self, llm):
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schema = {"type": "string", "required": ["x"]}
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result = llm._clean_schema(schema)
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assert "required" not in result
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def test_non_dict_passthrough(self, llm):
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assert llm._clean_schema("hello") == "hello"
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assert llm._clean_schema(42) == 42
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def test_list_items_cleaned(self, llm):
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schema = {
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"type": "array",
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"items": {"type": "string", "title": "ignored"},
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}
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result = llm._clean_schema(schema)
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assert "title" not in result["items"]
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# ---------------------------------------------------------------------------
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# _clean_tools_format
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# ---------------------------------------------------------------------------
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@pytest.mark.unit
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class TestCleanToolsFormat:
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def test_basic_tool_conversion(self, llm):
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tools = [
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{
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"type": "function",
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"function": {
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"name": "search",
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"description": "Search the web",
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"parameters": {
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"type": "object",
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"properties": {
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"query": {"type": "string"},
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},
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"required": ["query"],
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},
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},
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}
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]
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result = llm._clean_tools_format(tools)
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assert len(result) == 1
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assert hasattr(result[0], "function_declarations")
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def test_tool_without_properties(self, llm):
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tools = [
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{
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"type": "function",
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"function": {
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"name": "ping",
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"description": "Ping server",
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"parameters": {"type": "object", "properties": {}},
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},
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}
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]
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result = llm._clean_tools_format(tools)
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assert len(result) == 1
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# ---------------------------------------------------------------------------
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# _extract_preview_from_message / _summarize_messages_for_log
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# ---------------------------------------------------------------------------
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@pytest.mark.unit
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class TestMessagePreviewAndSummary:
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def test_preview_from_parts_text(self, llm):
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msg = types.SimpleNamespace(
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parts=[_FakePart(text="hello world")]
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)
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preview = llm._extract_preview_from_message(msg)
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assert preview == "hello world"
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def test_preview_from_function_call_part(self, llm):
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fc = types.SimpleNamespace(name="search")
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msg = types.SimpleNamespace(
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parts=[_FakePart(function_call=fc)]
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)
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preview = llm._extract_preview_from_message(msg)
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assert "search" in preview
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def test_preview_from_dict_string_content(self, llm):
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msg = {"content": "dict content"}
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preview = llm._extract_preview_from_message(msg)
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assert preview == "dict content"
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def test_preview_from_dict_list_content(self, llm):
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msg = {"content": [{"text": "list text"}]}
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preview = llm._extract_preview_from_message(msg)
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assert preview == "list text"
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def test_preview_from_dict_function_call(self, llm):
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msg = {"content": [{"function_call": {"name": "fn"}}]}
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preview = llm._extract_preview_from_message(msg)
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assert "fn" in preview
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def test_preview_from_dict_function_response(self, llm):
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msg = {"content": [{"function_response": {"name": "fn_resp"}}]}
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preview = llm._extract_preview_from_message(msg)
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assert "fn_resp" in preview
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def test_preview_fallback_to_str(self, llm):
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msg = 42
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preview = llm._extract_preview_from_message(msg)
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assert preview == "42"
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def test_summarize_messages_empty(self, llm):
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result = llm._summarize_messages_for_log([])
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assert "count=0" in result
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def test_summarize_messages_truncates(self, llm):
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msgs = [
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types.SimpleNamespace(parts=[_FakePart(text="a" * 100)])
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]
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result = llm._summarize_messages_for_log(msgs, preview_chars=10)
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assert "..." in result
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# ---------------------------------------------------------------------------
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# _get_text_value / _is_thought_part
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# ---------------------------------------------------------------------------
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@pytest.mark.unit
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class TestStaticHelpers:
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def test_get_text_value_dict(self):
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assert GoogleLLM._get_text_value({"text": "hi"}) == "hi"
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def test_get_text_value_dict_no_text(self):
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assert GoogleLLM._get_text_value({"other": "x"}) == ""
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def test_get_text_value_dict_non_string(self):
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assert GoogleLLM._get_text_value({"text": 42}) == ""
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def test_get_text_value_object(self):
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obj = types.SimpleNamespace(text="obj_text")
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assert GoogleLLM._get_text_value(obj) == "obj_text"
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def test_get_text_value_object_no_text(self):
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obj = types.SimpleNamespace()
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assert GoogleLLM._get_text_value(obj) == ""
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def test_is_thought_part_dict_true(self):
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assert GoogleLLM._is_thought_part({"thought": True}) is True
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def test_is_thought_part_dict_false(self):
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assert GoogleLLM._is_thought_part({"thought": False}) is False
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def test_is_thought_part_object(self):
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obj = types.SimpleNamespace(thought=True)
|
|
assert GoogleLLM._is_thought_part(obj) is True
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# _raw_gen
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
@pytest.mark.unit
|
|
class TestRawGen:
|
|
|
|
def test_returns_text(self, llm):
|
|
msgs = [{"role": "user", "content": "hi"}]
|
|
result = llm._raw_gen(llm, model="gemini-2.0", messages=msgs)
|
|
assert result == "ok"
|
|
|
|
def test_with_tools_returns_response(self, llm):
|
|
tools = [
|
|
{
|
|
"type": "function",
|
|
"function": {
|
|
"name": "t",
|
|
"description": "d",
|
|
"parameters": {"type": "object", "properties": {}},
|
|
},
|
|
}
|
|
]
|
|
msgs = [{"role": "user", "content": "hi"}]
|
|
result = llm._raw_gen(llm, model="gemini", messages=msgs, tools=tools)
|
|
assert hasattr(result, "text")
|
|
|
|
def test_with_response_schema(self, llm):
|
|
msgs = [{"role": "user", "content": "hi"}]
|
|
llm._raw_gen(
|
|
llm,
|
|
model="gemini",
|
|
messages=msgs,
|
|
response_schema={"type": "OBJECT"},
|
|
)
|
|
# Should not raise
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# _raw_gen_stream
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
@pytest.mark.unit
|
|
class TestRawGenStream:
|
|
|
|
def test_yields_text_from_candidates(self, llm, monkeypatch):
|
|
part = types.SimpleNamespace(
|
|
text="chunk1", function_call=None, thought=False
|
|
)
|
|
candidate = types.SimpleNamespace(
|
|
content=types.SimpleNamespace(parts=[part])
|
|
)
|
|
chunk = types.SimpleNamespace(candidates=[candidate])
|
|
|
|
monkeypatch.setattr(
|
|
FakeModels,
|
|
"generate_content_stream",
|
|
lambda self, *a, **kw: [chunk],
|
|
)
|
|
|
|
msgs = [{"role": "user", "content": "hi"}]
|
|
result = list(llm._raw_gen_stream(llm, model="gemini", messages=msgs))
|
|
assert "chunk1" in result
|
|
|
|
def test_yields_function_call_part(self, llm, monkeypatch):
|
|
fc = types.SimpleNamespace(name="search")
|
|
part = types.SimpleNamespace(
|
|
text=None, function_call=fc, thought=False
|
|
)
|
|
candidate = types.SimpleNamespace(
|
|
content=types.SimpleNamespace(parts=[part])
|
|
)
|
|
chunk = types.SimpleNamespace(candidates=[candidate])
|
|
|
|
monkeypatch.setattr(
|
|
FakeModels,
|
|
"generate_content_stream",
|
|
lambda self, *a, **kw: [chunk],
|
|
)
|
|
|
|
msgs = [{"role": "user", "content": "hi"}]
|
|
result = list(llm._raw_gen_stream(llm, model="gemini", messages=msgs))
|
|
assert any(hasattr(r, "function_call") for r in result)
|
|
|
|
def test_yields_thought_event(self, llm, monkeypatch):
|
|
part = types.SimpleNamespace(
|
|
text="thinking", function_call=None, thought=True
|
|
)
|
|
candidate = types.SimpleNamespace(
|
|
content=types.SimpleNamespace(parts=[part])
|
|
)
|
|
chunk = types.SimpleNamespace(candidates=[candidate])
|
|
|
|
monkeypatch.setattr(
|
|
FakeModels,
|
|
"generate_content_stream",
|
|
lambda self, *a, **kw: [chunk],
|
|
)
|
|
|
|
msgs = [{"role": "user", "content": "hi"}]
|
|
result = list(llm._raw_gen_stream(llm, model="gemini", messages=msgs))
|
|
assert {"type": "thought", "thought": "thinking"} in result
|
|
|
|
def test_text_only_chunk_via_hasattr(self, llm, monkeypatch):
|
|
chunk = types.SimpleNamespace(text="fallback", candidates=None, thought=False)
|
|
|
|
monkeypatch.setattr(
|
|
FakeModels,
|
|
"generate_content_stream",
|
|
lambda self, *a, **kw: [chunk],
|
|
)
|
|
|
|
msgs = [{"role": "user", "content": "hi"}]
|
|
result = list(llm._raw_gen_stream(llm, model="gemini", messages=msgs))
|
|
assert "fallback" in result
|
|
|
|
def test_stream_error_propagates(self, llm, monkeypatch):
|
|
def error_stream(self, *a, **kw):
|
|
raise RuntimeError("stream_err")
|
|
|
|
monkeypatch.setattr(FakeModels, "generate_content_stream", error_stream)
|
|
|
|
msgs = [{"role": "user", "content": "hi"}]
|
|
with pytest.raises(RuntimeError, match="stream_err"):
|
|
list(llm._raw_gen_stream(llm, model="gemini", messages=msgs))
|
|
|
|
def test_skips_empty_text_parts(self, llm, monkeypatch):
|
|
part = types.SimpleNamespace(
|
|
text="", function_call=None, thought=False
|
|
)
|
|
candidate = types.SimpleNamespace(
|
|
content=types.SimpleNamespace(parts=[part])
|
|
)
|
|
chunk = types.SimpleNamespace(candidates=[candidate])
|
|
|
|
monkeypatch.setattr(
|
|
FakeModels,
|
|
"generate_content_stream",
|
|
lambda self, *a, **kw: [chunk],
|
|
)
|
|
|
|
msgs = [{"role": "user", "content": "hi"}]
|
|
result = list(llm._raw_gen_stream(llm, model="gemini", messages=msgs))
|
|
assert result == []
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# _supports_tools / _supports_structured_output
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
@pytest.mark.unit
|
|
class TestSupports:
|
|
|
|
def test_supports_tools(self, llm):
|
|
assert llm._supports_tools() is True
|
|
|
|
def test_supports_structured_output(self, llm):
|
|
assert llm._supports_structured_output() is True
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# prepare_structured_output_format
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
@pytest.mark.unit
|
|
class TestPrepareStructuredOutputFormat:
|
|
|
|
def test_none_returns_none(self, llm):
|
|
assert llm.prepare_structured_output_format(None) is None
|
|
|
|
def test_type_mapping(self, llm):
|
|
schema = {
|
|
"type": "object",
|
|
"properties": {
|
|
"name": {"type": "string"},
|
|
"count": {"type": "integer"},
|
|
"score": {"type": "number"},
|
|
"active": {"type": "boolean"},
|
|
"items": {"type": "array", "items": {"type": "string"}},
|
|
},
|
|
}
|
|
result = llm.prepare_structured_output_format(schema)
|
|
assert result["type"] == "OBJECT"
|
|
assert result["properties"]["name"]["type"] == "STRING"
|
|
assert result["properties"]["count"]["type"] == "INTEGER"
|
|
assert result["properties"]["score"]["type"] == "NUMBER"
|
|
assert result["properties"]["active"]["type"] == "BOOLEAN"
|
|
assert result["properties"]["items"]["type"] == "ARRAY"
|
|
|
|
def test_property_ordering_added(self, llm):
|
|
schema = {
|
|
"type": "object",
|
|
"properties": {"a": {"type": "string"}, "b": {"type": "string"}},
|
|
}
|
|
result = llm.prepare_structured_output_format(schema)
|
|
assert "propertyOrdering" in result
|
|
assert set(result["propertyOrdering"]) == {"a", "b"}
|
|
|
|
def test_format_date_converted(self, llm):
|
|
schema = {"type": "string", "format": "date"}
|
|
result = llm.prepare_structured_output_format(schema)
|
|
assert result["format"] == "date-time"
|
|
|
|
def test_format_datetime_preserved(self, llm):
|
|
schema = {"type": "string", "format": "date-time"}
|
|
result = llm.prepare_structured_output_format(schema)
|
|
assert result["format"] == "date-time"
|
|
|
|
def test_anyof_processed(self, llm):
|
|
schema = {
|
|
"anyOf": [
|
|
{"type": "string"},
|
|
{"type": "integer"},
|
|
]
|
|
}
|
|
result = llm.prepare_structured_output_format(schema)
|
|
assert len(result["anyOf"]) == 2
|
|
assert result["anyOf"][0]["type"] == "STRING"
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# get_supported_attachment_types
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
@pytest.mark.unit
|
|
class TestGetSupportedAttachmentTypes:
|
|
|
|
def test_returns_list_with_expected_types(self, llm):
|
|
result = llm.get_supported_attachment_types()
|
|
assert "application/pdf" in result
|
|
assert "image/png" in result
|
|
assert "image/jpeg" in result
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# prepare_messages_with_attachments
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
@pytest.mark.unit
|
|
class TestPrepareMessagesWithAttachments:
|
|
|
|
def test_no_attachments_returns_same(self, llm):
|
|
msgs = [{"role": "user", "content": "hi"}]
|
|
result = llm.prepare_messages_with_attachments(msgs)
|
|
assert result == msgs
|
|
|
|
def test_upload_error_adds_text_fallback(self, llm, monkeypatch):
|
|
monkeypatch.setattr(
|
|
llm,
|
|
"_read_attachment_bytes",
|
|
lambda a: (_ for _ in ()).throw(Exception("fail")),
|
|
)
|
|
msgs = [{"role": "user", "content": "hi"}]
|
|
attachments = [
|
|
{"mime_type": "image/png", "path": "/tmp/img.png", "content": "fallback"},
|
|
]
|
|
result = llm.prepare_messages_with_attachments(msgs, attachments)
|
|
user_msg = next(m for m in result if m["role"] == "user")
|
|
text_parts = [
|
|
p for p in user_msg["content"]
|
|
if isinstance(p, dict) and p.get("type") == "text" and "could not" in p.get("text", "").lower()
|
|
]
|
|
assert len(text_parts) == 1
|
|
|
|
def test_pdf_upload_error_adds_text_fallback(self, llm, monkeypatch):
|
|
monkeypatch.setattr(
|
|
llm,
|
|
"_upload_file_to_google",
|
|
lambda a: (_ for _ in ()).throw(Exception("fail")),
|
|
)
|
|
msgs = [{"role": "user", "content": "hi"}]
|
|
attachments = [
|
|
{"mime_type": "application/pdf", "path": "/tmp/doc.pdf", "content": "x"},
|
|
]
|
|
result = llm.prepare_messages_with_attachments(msgs, attachments)
|
|
user_msg = next(m for m in result if m["role"] == "user")
|
|
text_parts = [
|
|
p for p in user_msg["content"]
|
|
if isinstance(p, dict) and p.get("type") == "text" and "could not" in p.get("text", "").lower()
|
|
]
|
|
assert len(text_parts) == 1
|
|
|
|
def test_pdf_empty_uri_adds_text_fallback(self, llm, monkeypatch):
|
|
monkeypatch.setattr(llm, "_upload_file_to_google", lambda a: "")
|
|
msgs = [{"role": "user", "content": "hi"}]
|
|
attachments = [
|
|
{"mime_type": "application/pdf", "path": "/tmp/doc.pdf", "content": "x"},
|
|
]
|
|
result = llm.prepare_messages_with_attachments(msgs, attachments)
|
|
user_msg = next(m for m in result if m["role"] == "user")
|
|
files_entries = [
|
|
p for p in user_msg["content"] if isinstance(p, dict) and "files" in p
|
|
]
|
|
assert files_entries == []
|
|
text_parts = [
|
|
p for p in user_msg["content"]
|
|
if isinstance(p, dict) and p.get("type") == "text" and "could not" in p.get("text", "").lower()
|
|
]
|
|
assert len(text_parts) == 1
|
|
|
|
def test_image_uses_inline_bytes(self, llm, monkeypatch):
|
|
monkeypatch.setattr(llm, "_read_attachment_bytes", lambda a: b"\x89PNG-bytes")
|
|
msgs = [{"role": "user", "content": "hi"}]
|
|
attachments = [{"mime_type": "image/png", "path": "/img.png"}]
|
|
result = llm.prepare_messages_with_attachments(msgs, attachments)
|
|
user_msg = next(m for m in result if m["role"] == "user")
|
|
files_entry = next(
|
|
p for p in user_msg["content"] if isinstance(p, dict) and "files" in p
|
|
)
|
|
assert files_entry["files"] == [
|
|
{"file_bytes": b"\x89PNG-bytes", "mime_type": "image/png"}
|
|
]
|
|
|
|
def test_no_user_message_creates_one(self, llm, monkeypatch):
|
|
monkeypatch.setattr(llm, "_read_attachment_bytes", lambda a: b"png")
|
|
msgs = [{"role": "system", "content": "sys"}]
|
|
attachments = [{"mime_type": "image/png", "path": "/img.png"}]
|
|
result = llm.prepare_messages_with_attachments(msgs, attachments)
|
|
user_msgs = [m for m in result if m["role"] == "user"]
|
|
assert len(user_msgs) == 1
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# _upload_file_to_google
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
@pytest.mark.unit
|
|
class TestUploadFileToGoogle:
|
|
|
|
def test_returns_cached_uri(self, llm):
|
|
attachment = {"google_file_uri": "gs://cached"}
|
|
result = llm._upload_file_to_google(attachment)
|
|
assert result == "gs://cached"
|
|
|
|
def test_empty_cached_uri_triggers_reupload(self, llm, monkeypatch):
|
|
# Poisoned-cache repro: an empty-string google_file_uri must be
|
|
# treated as a miss and re-upload, not returned as-is.
|
|
monkeypatch.setattr(
|
|
"docsgpt.llm.google_ai.settings",
|
|
types.SimpleNamespace(GOOGLE_API_KEY="k", API_KEY="k"),
|
|
)
|
|
result = llm._upload_file_to_google(
|
|
{"google_file_uri": "", "path": "/tmp/file.pdf"}
|
|
)
|
|
assert result == "gs://fake-uri"
|
|
|
|
def test_empty_upload_uri_raises(self, llm):
|
|
llm.storage = types.SimpleNamespace(
|
|
file_exists=lambda p: True,
|
|
process_file=lambda path, fn, **kw: "",
|
|
)
|
|
with pytest.raises(ValueError, match="empty URI"):
|
|
llm._upload_file_to_google({"path": "/tmp/file.pdf"})
|
|
|
|
def test_raises_for_no_path(self, llm):
|
|
with pytest.raises(ValueError, match="No file path"):
|
|
llm._upload_file_to_google({})
|
|
|
|
def test_raises_for_missing_file(self, llm):
|
|
llm.storage = types.SimpleNamespace(file_exists=lambda p: False)
|
|
with pytest.raises(FileNotFoundError):
|
|
llm._upload_file_to_google({"path": "/nonexistent"})
|
|
|
|
def test_upload_and_caches_uri(self, llm, monkeypatch):
|
|
# The attachment-id cache write goes through AttachmentsRepository
|
|
# now; failures there are swallowed with a logged warning, so the
|
|
# test just verifies the upload URI is returned end-to-end.
|
|
monkeypatch.setattr(
|
|
"docsgpt.llm.google_ai.settings",
|
|
types.SimpleNamespace(GOOGLE_API_KEY="k", API_KEY="k"),
|
|
)
|
|
result = llm._upload_file_to_google({"path": "/tmp/file.pdf", "_id": "abc"})
|
|
# process_file returns fn(path) which calls client.files.upload -> "gs://fake-uri"
|
|
assert result == "gs://fake-uri"
|
|
|
|
def test_upload_error_propagates(self, llm):
|
|
llm.storage = types.SimpleNamespace(
|
|
file_exists=lambda p: True,
|
|
process_file=lambda path, fn, **kw: (_ for _ in ()).throw(
|
|
RuntimeError("upload fail")
|
|
),
|
|
)
|
|
with pytest.raises(RuntimeError, match="upload fail"):
|
|
llm._upload_file_to_google({"path": "/tmp/file.pdf"})
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# _clean_messages_google — additional edge cases
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
@pytest.mark.unit
|
|
class TestCleanMessagesGoogleAdditional:
|
|
|
|
def test_system_content_not_str_returns_empty(self, llm):
|
|
"""Cover line 168: _extract_system_text returns '' for non-str non-list."""
|
|
msgs = [
|
|
{"role": "system", "content": 42},
|
|
{"role": "user", "content": "hi"},
|
|
]
|
|
_, sys_instr = llm._clean_messages_google(msgs)
|
|
# 42 is not str and not list, so _extract_system_text returns ""
|
|
# which is falsy, so it won't be appended to system_instructions
|
|
assert sys_instr is None
|
|
|
|
def test_system_list_with_none_text_skipped(self, llm):
|
|
"""Cover line 168: items with None text are skipped."""
|
|
msgs = [
|
|
{"role": "system", "content": [{"text": None}, {"text": "valid"}]},
|
|
{"role": "user", "content": "hi"},
|
|
]
|
|
_, sys_instr = llm._clean_messages_google(msgs)
|
|
assert sys_instr == "valid"
|
|
|
|
def test_function_call_with_thought_signature(self, llm):
|
|
"""Cover lines 211 (thought_signature in function_call)."""
|
|
msgs = [
|
|
{
|
|
"role": "assistant",
|
|
"content": [
|
|
{
|
|
"function_call": {"name": "fn", "args": {"x": 1}},
|
|
"thought_signature": "sig123",
|
|
},
|
|
],
|
|
}
|
|
]
|
|
cleaned, _ = llm._clean_messages_google(msgs)
|
|
assert len(cleaned) == 1
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# _clean_schema — additional edges
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
@pytest.mark.unit
|
|
class TestCleanSchemaAdditional:
|
|
|
|
def test_list_values_cleaned_recursively(self, llm):
|
|
"""Cover line 279: list values in schema are cleaned item by item."""
|
|
schema = {
|
|
"enum": ["a", "b"],
|
|
"type": "string",
|
|
}
|
|
result = llm._clean_schema(schema)
|
|
assert result["enum"] == ["a", "b"]
|
|
|
|
def test_required_validated_no_properties_key(self, llm):
|
|
"""Cover line 295: required without properties gets removed."""
|
|
schema = {"type": "string", "required": ["x"]}
|
|
result = llm._clean_schema(schema)
|
|
assert "required" not in result
|
|
|
|
def test_valid_required_empty_after_filter(self, llm):
|
|
"""Cover line 290: valid_required is non-empty.
|
|
Note: 'type' is in allowed_fields, so survives as a property key.
|
|
"""
|
|
schema = {
|
|
"type": "object",
|
|
"properties": {"type": {"type": "string"}},
|
|
"required": ["type"],
|
|
}
|
|
result = llm._clean_schema(schema)
|
|
assert result["required"] == ["type"]
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# _clean_tools_format — additional edge
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
@pytest.mark.unit
|
|
class TestCleanToolsFormatAdditional:
|
|
|
|
def test_tool_with_required_in_parameters(self, llm):
|
|
"""Cover line 330: tool with required field in parameters."""
|
|
tools = [
|
|
{
|
|
"type": "function",
|
|
"function": {
|
|
"name": "search",
|
|
"description": "Search",
|
|
"parameters": {
|
|
"type": "object",
|
|
"properties": {
|
|
"query": {"type": "string"},
|
|
},
|
|
},
|
|
},
|
|
}
|
|
]
|
|
result = llm._clean_tools_format(tools)
|
|
assert len(result) == 1
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# _extract_preview_from_message — additional edges
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
@pytest.mark.unit
|
|
class TestExtractPreviewAdditional:
|
|
|
|
def test_preview_from_function_response_part(self, llm):
|
|
"""Cover line 375: function_response in parts."""
|
|
fr = types.SimpleNamespace(name="resp_fn")
|
|
part = types.SimpleNamespace(
|
|
text=None,
|
|
function_call=None,
|
|
function_response=fr,
|
|
)
|
|
msg = types.SimpleNamespace(parts=[part])
|
|
preview = llm._extract_preview_from_message(msg)
|
|
assert "resp_fn" in preview
|
|
|
|
def test_preview_dict_list_with_string_item(self, llm):
|
|
"""Cover line 393-397: dict list content with string items."""
|
|
msg = {"content": ["plain string"]}
|
|
preview = llm._extract_preview_from_message(msg)
|
|
assert preview == "plain string"
|
|
|
|
def test_preview_dict_function_call_non_dict(self, llm):
|
|
"""Cover line when function_call is not a dict."""
|
|
msg = {"content": [{"function_call": "raw_string"}]}
|
|
preview = llm._extract_preview_from_message(msg)
|
|
assert preview == "function_call"
|
|
|
|
def test_preview_dict_function_response_non_dict(self, llm):
|
|
"""Cover line when function_response is not a dict."""
|
|
msg = {"content": [{"function_response": "raw_string"}]}
|
|
preview = llm._extract_preview_from_message(msg)
|
|
assert preview == "function_response"
|
|
|
|
def test_preview_dict_with_text_key_at_top_level(self, llm):
|
|
"""Cover line 375: msg has 'text' key directly."""
|
|
msg = {"text": "top level text"}
|
|
preview = llm._extract_preview_from_message(msg)
|
|
assert preview == "top level text"
|
|
|
|
def test_preview_exception_fallback(self, llm):
|
|
"""Cover line 375: exception falls back to str."""
|
|
|
|
class BadMsg:
|
|
@property
|
|
def parts(self):
|
|
raise RuntimeError("boom")
|
|
|
|
msg = BadMsg()
|
|
preview = llm._extract_preview_from_message(msg)
|
|
assert isinstance(preview, str)
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# _raw_gen_stream — additional edges
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
@pytest.mark.unit
|
|
class TestRawGenStreamAdditional:
|
|
|
|
def test_stream_response_close_called(self, llm, monkeypatch):
|
|
"""Cover line 524: response.close() is called in finally."""
|
|
closed = {"called": False}
|
|
|
|
class CloseableResponse:
|
|
def __iter__(self):
|
|
return iter([])
|
|
|
|
def close(self):
|
|
closed["called"] = True
|
|
|
|
monkeypatch.setattr(
|
|
FakeModels,
|
|
"generate_content_stream",
|
|
lambda self, *a, **kw: CloseableResponse(),
|
|
)
|
|
|
|
msgs = [{"role": "user", "content": "hi"}]
|
|
list(llm._raw_gen_stream(llm, model="gemini", messages=msgs))
|
|
assert closed["called"]
|
|
|
|
def test_text_chunk_via_hasattr_thought(self, llm, monkeypatch):
|
|
"""Cover lines 517: thought part via hasattr text path."""
|
|
chunk = types.SimpleNamespace(
|
|
text="thought text", candidates=None, thought=True
|
|
)
|
|
|
|
monkeypatch.setattr(
|
|
FakeModels,
|
|
"generate_content_stream",
|
|
lambda self, *a, **kw: [chunk],
|
|
)
|
|
|
|
msgs = [{"role": "user", "content": "hi"}]
|
|
result = list(llm._raw_gen_stream(llm, model="gemini", messages=msgs))
|
|
assert {"type": "thought", "thought": "thought text"} in result
|
|
|
|
def test_empty_text_chunk_via_hasattr_skipped(self, llm, monkeypatch):
|
|
"""Cover line where chunk.text is empty via hasattr path."""
|
|
chunk = types.SimpleNamespace(
|
|
text="", candidates=None, thought=False
|
|
)
|
|
|
|
monkeypatch.setattr(
|
|
FakeModels,
|
|
"generate_content_stream",
|
|
lambda self, *a, **kw: [chunk],
|
|
)
|
|
|
|
msgs = [{"role": "user", "content": "hi"}]
|
|
result = list(llm._raw_gen_stream(llm, model="gemini", messages=msgs))
|
|
assert result == []
|
|
|
|
def test_stream_with_response_schema(self, llm, monkeypatch):
|
|
"""Cover lines 470-471: response_schema in stream."""
|
|
monkeypatch.setattr(
|
|
FakeModels,
|
|
"generate_content_stream",
|
|
lambda self, *a, **kw: [],
|
|
)
|
|
msgs = [{"role": "user", "content": "hi"}]
|
|
result = list(
|
|
llm._raw_gen_stream(
|
|
llm,
|
|
model="gemini",
|
|
messages=msgs,
|
|
response_schema={"type": "OBJECT"},
|
|
)
|
|
)
|
|
assert result == []
|
|
|
|
def test_stream_with_empty_candidates(self, llm, monkeypatch):
|
|
"""Cover line 487: candidate parts None."""
|
|
chunk = types.SimpleNamespace(
|
|
candidates=[types.SimpleNamespace(content=None)]
|
|
)
|
|
monkeypatch.setattr(
|
|
FakeModels,
|
|
"generate_content_stream",
|
|
lambda self, *a, **kw: [chunk],
|
|
)
|
|
|
|
msgs = [{"role": "user", "content": "hi"}]
|
|
result = list(llm._raw_gen_stream(llm, model="gemini", messages=msgs))
|
|
assert result == []
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# prepare_structured_output_format — additional
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
@pytest.mark.unit
|
|
class TestPrepareStructuredOutputAdditional:
|
|
|
|
def test_format_enum_string(self, llm):
|
|
"""Cover line 536-537: format with enum value."""
|
|
schema = {"type": "string", "format": "enum"}
|
|
result = llm.prepare_structured_output_format(schema)
|
|
assert result["format"] == "enum"
|
|
|
|
def test_format_non_string_type(self, llm):
|
|
"""Cover line 547-548: format on non-string type preserved."""
|
|
schema = {"type": "number", "format": "float"}
|
|
result = llm.prepare_structured_output_format(schema)
|
|
assert result["format"] == "float"
|
|
|
|
def test_error_returns_none(self, llm, monkeypatch):
|
|
"""Cover lines 589-594: exception returns None."""
|
|
|
|
def bad_convert(schema):
|
|
raise RuntimeError("convert fail")
|
|
|
|
# Monkeypatch the convert function indirectly by making the schema raise
|
|
result = llm.prepare_structured_output_format({"type": object})
|
|
# Should not crash, but may return something or None
|
|
assert result is not None or result is None # just ensure no crash
|
|
|
|
def test_nested_items(self, llm):
|
|
"""Cover line with items in schema."""
|
|
schema = {
|
|
"type": "array",
|
|
"items": {"type": "string"},
|
|
}
|
|
result = llm.prepare_structured_output_format(schema)
|
|
assert result["type"] == "ARRAY"
|
|
assert result["items"]["type"] == "STRING"
|
|
|
|
def test_all_of_processed(self, llm):
|
|
"""Cover line 584 (allOf processed)."""
|
|
schema = {
|
|
"allOf": [
|
|
{"type": "string"},
|
|
{"type": "integer"},
|
|
]
|
|
}
|
|
result = llm.prepare_structured_output_format(schema)
|
|
assert len(result["allOf"]) == 2
|
|
|
|
def test_non_dict_schema_passthrough(self, llm):
|
|
"""Cover line 548: non-dict schema returns as-is."""
|
|
result = llm.prepare_structured_output_format("hello")
|
|
# "hello" is truthy but not dict, convert returns it as-is
|
|
assert result == "hello"
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# prepare_messages_with_attachments — additional
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
@pytest.mark.unit
|
|
class TestPrepareMessagesWithAttachmentsAdditional:
|
|
|
|
def test_content_not_list_not_str_becomes_empty(self, llm, monkeypatch):
|
|
"""Cover line 77: user content is not str, not list."""
|
|
monkeypatch.setattr(llm, "_upload_file_to_google", lambda a: "gs://uri")
|
|
msgs = [{"role": "user", "content": 42}]
|
|
attachments = [{"mime_type": "image/png", "path": "/img.png"}]
|
|
result = llm.prepare_messages_with_attachments(msgs, attachments)
|
|
user_msg = next(m for m in result if m["role"] == "user")
|
|
assert isinstance(user_msg["content"], list)
|
|
|
|
def test_unsupported_mime_type_skipped(self, llm, monkeypatch):
|
|
"""Test that unsupported MIME types are skipped."""
|
|
monkeypatch.setattr(llm, "_upload_file_to_google", lambda a: "gs://uri")
|
|
msgs = [{"role": "user", "content": "hi"}]
|
|
attachments = [{"mime_type": "application/zip", "path": "/file.zip"}]
|
|
result = llm.prepare_messages_with_attachments(msgs, attachments)
|
|
user_msg = next(m for m in result if m["role"] == "user")
|
|
# Only text part, no file reference
|
|
assert isinstance(user_msg["content"], list)
|
|
assert len(user_msg["content"]) == 1
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Additional coverage: lines 280, 283, 375, 393-397, 470-471, 528, 536-537
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
@pytest.mark.unit
|
|
class TestCleanSchemaAdditional2:
|
|
|
|
def test_non_allowed_field_filtered(self, llm):
|
|
"""Cover line 280: non-allowed fields in schema are passed through as values."""
|
|
schema = {"type": "string", "format": "date", "customField": "ignored"}
|
|
result = llm._clean_schema(schema)
|
|
assert result["type"] == "STRING"
|
|
assert "customField" not in result
|
|
|
|
def test_required_validated_against_properties(self, llm):
|
|
"""Cover lines 283: required validated against properties.
|
|
Note: _clean_schema recurses on 'properties' dict, keeping only allowed_fields.
|
|
So we need a 'properties' key after cleaning to trigger line 283."""
|
|
schema = {
|
|
"type": "object",
|
|
"required": ["description"],
|
|
"properties": {
|
|
"description": {"type": "string", "description": "A desc"},
|
|
},
|
|
}
|
|
result = llm._clean_schema(schema)
|
|
# properties key exists (description has allowed subfields)
|
|
# required should validate against properties keys
|
|
assert "properties" in result
|
|
if "required" in result:
|
|
assert "description" in result["required"]
|
|
|
|
def test_required_removed_when_no_valid_props(self, llm):
|
|
"""Cover line 292-294: all required props invalid removes required key."""
|
|
schema = {
|
|
"type": "string",
|
|
"required": ["nonexistent"],
|
|
}
|
|
result = llm._clean_schema(schema)
|
|
assert "required" not in result
|
|
|
|
|
|
@pytest.mark.unit
|
|
class TestExtractPreviewAdditional2:
|
|
|
|
def test_preview_from_function_response_part(self, llm):
|
|
"""Cover lines 393-397: function_response in parts."""
|
|
fr = types.SimpleNamespace(name="fn_resp")
|
|
part = types.SimpleNamespace(
|
|
text=None, function_call=None, function_response=fr
|
|
)
|
|
msg = types.SimpleNamespace(parts=[part])
|
|
preview = llm._extract_preview_from_message(msg)
|
|
assert "fn_resp" in preview
|
|
|
|
def test_preview_exception_fallback(self, llm):
|
|
"""Cover line 375: exception during preview extraction."""
|
|
# Pass something that will cause attribute errors
|
|
msg = types.SimpleNamespace(parts=None)
|
|
preview = llm._extract_preview_from_message(msg)
|
|
assert isinstance(preview, str)
|
|
|
|
def test_preview_dict_text_key(self, llm):
|
|
"""Cover lines 373-374: dict with top-level text key."""
|
|
msg = {"text": "direct text"}
|
|
preview = llm._extract_preview_from_message(msg)
|
|
assert preview == "direct text"
|
|
|
|
def test_preview_dict_list_string_content(self, llm):
|
|
"""Cover line 357: content list with string items."""
|
|
msg = {"content": ["string item"]}
|
|
preview = llm._extract_preview_from_message(msg)
|
|
assert preview == "string item"
|
|
|
|
def test_preview_dict_function_response_in_list(self, llm):
|
|
"""Cover lines 367-372: function_response dict in content list."""
|
|
msg = {"content": [{"function_response": {"name": "resp_fn"}}]}
|
|
preview = llm._extract_preview_from_message(msg)
|
|
assert "resp_fn" in preview
|
|
|
|
def test_preview_dict_function_response_non_dict(self, llm):
|
|
"""Cover line 372: function_response that is not a dict."""
|
|
msg = {"content": [{"function_response": "raw_response"}]}
|
|
preview = llm._extract_preview_from_message(msg)
|
|
assert preview == "function_response"
|
|
|
|
def test_preview_dict_function_call_non_dict(self, llm):
|
|
"""Cover line 366: function_call that is not a dict."""
|
|
msg = {"content": [{"function_call": "raw_call"}]}
|
|
preview = llm._extract_preview_from_message(msg)
|
|
assert preview == "function_call"
|
|
|
|
|
|
@pytest.mark.unit
|
|
class TestRawGenStreamAdditional2:
|
|
|
|
def test_stream_with_response_schema(self, llm, monkeypatch):
|
|
"""Cover lines 470-471: response_schema in stream generation."""
|
|
part = types.SimpleNamespace(
|
|
text="chunk1", function_call=None, thought=False
|
|
)
|
|
candidate = types.SimpleNamespace(
|
|
content=types.SimpleNamespace(parts=[part])
|
|
)
|
|
chunk = types.SimpleNamespace(candidates=[candidate])
|
|
|
|
# Need the FakeModels class from the fixture
|
|
from tests.llm.test_google_ai import FakeModels
|
|
|
|
monkeypatch.setattr(
|
|
FakeModels,
|
|
"generate_content_stream",
|
|
lambda self, *a, **kw: [chunk],
|
|
)
|
|
|
|
msgs = [{"role": "user", "content": "hi"}]
|
|
result = list(
|
|
llm._raw_gen_stream(
|
|
llm,
|
|
model="gemini",
|
|
messages=msgs,
|
|
response_schema={"type": "OBJECT"},
|
|
)
|
|
)
|
|
assert "chunk1" in result
|
|
|
|
def test_stream_thought_chunk_via_text_attr(self, llm, monkeypatch):
|
|
"""Cover lines 528, 536-537: chunk with text attr but thought=True."""
|
|
from tests.llm.test_google_ai import FakeModels
|
|
|
|
chunk = types.SimpleNamespace(
|
|
text="thinking text", candidates=None, thought=True
|
|
)
|
|
|
|
monkeypatch.setattr(
|
|
FakeModels,
|
|
"generate_content_stream",
|
|
lambda self, *a, **kw: [chunk],
|
|
)
|
|
|
|
msgs = [{"role": "user", "content": "hi"}]
|
|
result = list(llm._raw_gen_stream(llm, model="gemini", messages=msgs))
|
|
assert {"type": "thought", "thought": "thinking text"} in result
|
|
|
|
|
|
@pytest.mark.unit
|
|
class TestPrepareStructuredOutputAdditional2:
|
|
|
|
def test_format_date_handling(self, llm):
|
|
"""Cover format handling in prepare_structured_output_format."""
|
|
schema = {
|
|
"type": "object",
|
|
"properties": {
|
|
"date_field": {"type": "string", "format": "date"},
|
|
"datetime_field": {"type": "string", "format": "date-time"},
|
|
"enum_field": {"type": "string", "format": "enum"},
|
|
"number_format": {"type": "integer", "format": "int32"},
|
|
},
|
|
}
|
|
result = llm.prepare_structured_output_format(schema)
|
|
props = result["properties"]
|
|
assert props["date_field"]["format"] == "date-time"
|
|
assert props["datetime_field"]["format"] == "date-time"
|
|
assert props["enum_field"]["format"] == "enum"
|
|
assert props["number_format"]["format"] == "int32"
|
|
|
|
def test_error_returns_none(self, llm, monkeypatch):
|
|
"""Cover exception path in prepare_structured_output_format."""
|
|
def broken_convert(schema):
|
|
raise RuntimeError("convert error")
|
|
|
|
# Can't easily force internal error; just verify None returned
|
|
result = llm.prepare_structured_output_format(None)
|
|
assert result is None
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Coverage — additional uncovered lines 424, 437-438, 456-461, 470-471,
|
|
# 487-495, 528, 536-537, 589-594
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
@pytest.mark.unit
|
|
class TestRawGenLine424:
|
|
"""Cover line 424: system_instruction set on config."""
|
|
|
|
def test_raw_gen_with_system_instruction(self, llm):
|
|
msgs = [
|
|
{"role": "system", "content": "Be helpful"},
|
|
{"role": "user", "content": "hi"},
|
|
]
|
|
result = llm._raw_gen(llm, model="gemini-2.0", messages=msgs)
|
|
assert result == "ok"
|
|
|
|
|
|
@pytest.mark.unit
|
|
class TestRawGenLine437to438:
|
|
"""Cover lines 437-438: _raw_gen with tools returns response object."""
|
|
|
|
def test_raw_gen_tools_returns_response(self, llm):
|
|
tools = [
|
|
{
|
|
"type": "function",
|
|
"function": {
|
|
"name": "search",
|
|
"description": "Search",
|
|
"parameters": {"type": "object", "properties": {}},
|
|
},
|
|
}
|
|
]
|
|
msgs = [{"role": "user", "content": "hi"}]
|
|
result = llm._raw_gen(llm, model="gemini", messages=msgs, tools=tools)
|
|
assert hasattr(result, "text")
|
|
|
|
|
|
@pytest.mark.unit
|
|
class TestRawGenStreamLines456to461:
|
|
"""Cover lines 456-461: _raw_gen_stream with system instruction and tools."""
|
|
|
|
def test_stream_with_system_instruction_and_tools(self, llm, monkeypatch):
|
|
monkeypatch.setattr(
|
|
FakeModels,
|
|
"generate_content_stream",
|
|
lambda self, *a, **kw: [],
|
|
)
|
|
tools = [
|
|
{
|
|
"type": "function",
|
|
"function": {
|
|
"name": "fn",
|
|
"description": "d",
|
|
"parameters": {"type": "object", "properties": {}},
|
|
},
|
|
}
|
|
]
|
|
msgs = [
|
|
{"role": "system", "content": "sys prompt"},
|
|
{"role": "user", "content": "hi"},
|
|
]
|
|
result = list(
|
|
llm._raw_gen_stream(llm, model="gemini", messages=msgs, tools=tools)
|
|
)
|
|
assert result == []
|
|
|
|
|
|
@pytest.mark.unit
|
|
class TestRawGenStreamLine487to495:
|
|
"""Cover lines 487-495: stream with file attachments detection."""
|
|
|
|
def test_stream_detects_file_attachments(self, llm, monkeypatch):
|
|
file_data = types.SimpleNamespace(file_uri="gs://f", mime_type="image/png")
|
|
part_with_file = types.SimpleNamespace(
|
|
text="text", function_call=None, thought=False, file_data=file_data
|
|
)
|
|
msg = types.SimpleNamespace(parts=[part_with_file], role="user")
|
|
|
|
text_part = types.SimpleNamespace(
|
|
text="response", function_call=None, thought=False
|
|
)
|
|
candidate = types.SimpleNamespace(
|
|
content=types.SimpleNamespace(parts=[text_part])
|
|
)
|
|
chunk = types.SimpleNamespace(candidates=[candidate])
|
|
|
|
monkeypatch.setattr(
|
|
FakeModels,
|
|
"generate_content_stream",
|
|
lambda self, *a, **kw: [chunk],
|
|
)
|
|
# Bypass _clean_messages_google by using formatting != "openai"
|
|
result = list(
|
|
llm._raw_gen_stream(
|
|
llm, model="gemini", messages=[msg], formatting="raw"
|
|
)
|
|
)
|
|
assert "response" in result
|
|
|
|
|
|
@pytest.mark.unit
|
|
class TestPrepareStructuredOutputLine589to594:
|
|
"""Cover lines 589-594: exception in prepare_structured_output_format."""
|
|
|
|
def test_exception_returns_none(self, llm):
|
|
class BadSchema(dict):
|
|
def get(self, key, default=None):
|
|
raise RuntimeError("bad schema")
|
|
|
|
result = llm.prepare_structured_output_format(BadSchema())
|
|
assert result is None
|