Navegador

feat: LLM backend abstraction — unified provider interface with auto-discovery LLMProvider ABC with Anthropic, OpenAI, and Ollama implementations. Auto-discovery of installed SDKs, priority-ordered provider selection. Closes #64

lmata 2026-03-23 05:08 trunk

Commit 13d6a44dde2023109309ab7a9ae66399c5b43d3f7811289ddf8caa17a592a1b0

Parent a24deda54b88d4e…

2 files changed +276 +489

+ navegador/llm.py + tests/test_llm.py

A navegador/llm.py

+276

		--- a/navegador/llm.py
		+++ b/navegador/llm.py
		@@ -0,0 +1,276 @@
	1	+"""
	2	+LLM backend abstraction — unified provider interface with auto-discovery.
	3	+
	4	+Provides a common interface for multiple LLM providers (Anthropic, OpenAI,
	5	+Ollama). SDK imports are lazy and guarded so that missing optional dependencies
	6	+produce a clear, actionable ImportError rather than a confusing traceback.
	7	+
	8	+Usage::
	9	+
	10	+ from navegador.llm import get_provider, auto_provider, discover_providers
	11	+
	12	+ # Explicit provider
	13	+ provider = get_provider("anthropic", model="claude-3-5-haiku-20241022")
	14	+ response = provider.complete("Explain this function: ...")
	15	+
	16	+ # Auto-detect the first available SDK
	17	+ provider = auto_provider()
	18	+
	19	+ # See what is installed
	20	+ available = discover_providers() # e.g. ["anthropic", "openai"]
	21	+"""
	22	+
	23	+from __future__ import annotations
	24	+
	25	+from abc iimport ABC, abstractmethod
	26	+
	27	+# ── Abstract base ─────────────────────────────────────────────────────────────
	28	+
	29	+
	30	+class LLMProvider(ABC):
	31	+ """Abstract interface that every concrete LLM provider must satisfy."""
	32	+
	33	+ @abstractmethod
	34	+ def complete(self, prompt: str, **kwargs) -> str:
	35	+ """
	36	+ Send prompt to the model and return the completion as a string.
	37	+
	38	+ Args:
	39	+ prompt: The user/system prompt text.
	40	+ **kwargs: Provider-specific options (temperature, max_tokens, …).
	41	+
	42	+ Returns:
	43	+ The model's text response.
	44	+ """
	45	+
	46	+ @abstractmethod
	47	+ def embed(self, text: str) -> list[float]:
	48	+ """
	49	+ Return an embedding vector for text.
	50	+
	51	+ Args:
	52	+ text: The input string to embed.
	53	+
	54	+ Returns:
	55	+ A list of floats representing the embedding.
	56	+ """
	57	+
	58	+ @property
	59	+ @abstractmethod
	60	+ def name(self) -> str:
	61	+ """Short identifier for the provider, e.g. ``"anthropic"``."""
	62	+
	63	+ @property
	64	+ @abstractmethod
	65	+ def model(self) -> str:
	66	+ """Model identifier used for API calls, e.g. ``"claude-3-5-haiku-20241022"``."""
	67	+
	68	+
	69	+# ── Concrete providers ────────────────────────────────────────────────────────
	70	+
	71	+
	72	+class AnthropicProvider(LLMProvider):
	73	+ """
	74	+ LLM provider backed by the ``anthropic`` Python SDK.
	75	+
	76	+ Install::
	77	+
	78	+ pip install anthropic
	79	+
	80	+ Args:
	81	+ model: Anthropic model ID (default ``"claude-3-5-haiku-20241022"``).
	82	+ """
	83	+
	84	+ _DEFAULT_MODEL = "claude-3-5-haiku-20241022"
	85	+
	86	+ def __init__(self, model: str = "") -> None:
	87	+ try:
	88	+ import anthropic # noqa: F401
	89	+ except ImportError as exc:
	90	+ raise ImportError(
	91	+ "The 'anthropic' package is required to use AnthropicProvider. "
	92	+ "Install it with: pip install anthropic"
	93	+ ) from exc
	94	+
	95	+ self._model = model or self._DEFAULT_MODEL
	96	+
	97	+ import anthropic
	98	+
	99	+ self._client = anthropic.Anthropic()
	100	+
	101	+ @property
	102	+ def name(self) -> str:
	103	+ return "anthropic"
	104	+
	105	+ @property
	106	+ def model(self) -> str:
	107	+ return self._model
	108	+
	109	+ def complete(self, prompt: str, **kwargs) -> str:
	110	+ """Call the Anthropic Messages API and return the first text block."""
	111	+ max_tokens = kwargs.pop("max_tokens", 1024)
	112	+ message = self._client.messages.create(
	113	+ model=self._model,
	114	+ max_tokens=max_tokens,
	115	+ messages=[{"role": "user", "content": prompt}],
	116	+ **kwargs,
	117	+ )
	118	+ return message.content[0].text
	119	+
	120	+ def embed(self, text: str) -> list[float]:
	121	+ """
	122	+ Anthropic does not currently expose a public embeddings API.
	123	+
	124	+ Raises:
	125	+ NotImplementedError: Always.
	126	+ """
	127	+ raise NotImplementedError(
	128	+ "Anthropic does not provide a public embeddings API. "
	129	+ "Use OpenAIProvider or OllamaProvider for embeddings."
	130	+ )
	131	+
	132	+
	133	+class OpenAIProvider(LLMProvider):
	134	+ """
	135	+ LLM provider backed by the ``openai`` Python SDK.
	136	+
	137	+ Install::
	138	+
	139	+ pip install openai
	140	+
	141	+ Args:
	142	+ model: OpenAI model ID (default ``"gpt-4o-mini"``).
	143	+ """
	144	+
	145	+ _DEFAULT_MODEL = "gpt-4o-mini"
	146	+
	147	+ def __init__(self, model: str = "") -> None:
	148	+ try:
	149	+ import openai # noqa: F401
	150	+ except ImportError as exc:
	151	+ raise ImportError(
	152	+ "The 'openai' package is required to use OpenAIProvider. "
	153	+ "Install it with: pip install openai"
	154	+ ) from exc
	155	+
	156	+ self._model = model or self._DEFAULT_MODEL
	157	+
	158	+ import openai
	159	+
	160	+ self._client = openai.OpenAI()
	161	+
	162	+ @property
	163	+ def name(self) -> str:
	164	+ return "openai"
	165	+
	166	+ @property
	167	+ def model(self) -> str:
	168	+ return self._model
	169	+
	170	+ def complete(self, prompt: str, **kwargs) -> str:
	171	+ """Call the OpenAI Chat Completions API and return the assistant message."""
	172	+ response = self._client.chat.completions.create(
	173	+ model=self._model,
	174	+ messages=[{"role": "user", "content": prompt}],
	175	+ **kwargs,
	176	+ )
	177	+ return response.choices[0].message.content
	178	+
	179	+ def embed(self, text: str) -> list[float]:
	180	+ """Call the OpenAI Embeddings API and return the embedding vector."""
	181	+ embed_model = "text-embedding-3-small"
	182	+ response = self._client.embeddings.create(input=text, model=embed_model)
	183	+ return response.data[0].embedding
	184	+
	185	+
	186	+class OllamaProvider(LLMProvider):
	187	+ """
	188	+ LLM provider backed by the ``ollama`` Python SDK (local models via Ollama).
	189	+
	190	+ Install::
	191	+
	192	+ pip install ollama
	193	+
	194	+ The Ollama server must be running locally (``ollama serve``).
	195	+
	196	+ Args:
	197	+ model: Ollama model tag (default ``"llama3.2"``).
	198	+ """
	199	+
	200	+ _DEFAULT_MODEL = "llama3.2"
	201	+
	202	+ def __init__(self, model: str = "") -> None:
	203	+ try:
	204	+ import ollama # noqa: F401
	205	+ except ImportError as exc:
	206	+ raise ImportError(
	207	+ "The 'ollama' package is required to use OllamaProvider. "
	208	+ "Install it with: pip install ollama"
	209	+ ) from exc
	210	+
	211	+ self._model = model or self._DEFAULT_MODEL
	212	+
	213	+ import ollama
	214	+
	215	+ self._client = ollama.Client()
	216	+
	217	+ @property
	218	+ def name(self) -> str:
	219	+ return "ollama"
	220	+
	221	+ @property
	222	+ def model(self) -> str:
	223	+ return self._model
	224	+
	225	+ def complete(self, prompt: str, **kwargs) -> str:
	226	+ """Call the Ollama chat API and return the assistant message content."""
	227	+ response = self._client.chat(
	228	+ model=self._model,
	229	+ messages=[{"role": "user", "content": prompt}],
	230	+ **kwargs,
	231	+ )
	232	+ return response["message"]["content"]
	233	+
	234	+ def embed(self, text: str) -> list[float]:
	235	+ """Call the Ollama embeddings API and return the embedding vector."""
	236	+ response = self._client.embeddings(model=self._model, prompt=text)
	237	+ return response["embedding"]
	238	+
	239	+
	240	+# ── Discovery & factory ───────"
	241	+ rovider_namf"Valid options are: {sorted(_PROVIDER_CLASS_MAP)}"
	242	+ )
	243	+ cls = _PROVIDER_CLASS_MAP[name]
	244	+ return cls(model=model)
	245	+
	246	+
	247	+def auto_provider(model: str = "") -> LLMProvider:
	248	+ """
	249	+ Return the first available LLM provider based on installed SDKs.
	250	+
	251	+ Priority order: anthropic → openai → ollama.
	252	+
	253	+ Args:
	254	+ model: Optional model ID forwarded to the provider constructor.
	255	+
	256	+ Returns:
	257	+ An :class:`LLMProvider` instance for the first available SDK.
	258	+
	259	+ Raises:
	260	+ RuntimeError: If no supported LLM SDK is installed.
	261	+ """
	262	+ for provider_name in _PROVIDER_NAMES:
	263	+ sdk_name = _PROVIDER_SDK_MAP[provider_name]
	264	+ try:
	265	+ __import__(sdk_name)
	266	+ except ImportError:
	267	+ continue
	268	+ return get_provider(provider_name, model=model)
	269	+
	270	+ raise RuntimeError(
	271	+ "No LLM SDK is installed. Install at least one of: "
	272	+ "anthropic, openai, ollama.\n"
	273	+ " pip install anthropic # Anthropic Claude\n"
	274	+ " pip install openai # OpenAI GPT\n"
	275	+ " pip install ollama # Ollama (local models)"
	276	+ )

	--- a/navegador/llm.py
	+++ b/navegador/llm.py
	@@ -0,0 +1,276 @@

	--- a/navegador/llm.py
	+++ b/navegador/llm.py
	@@ -0,0 +1,276 @@
1	"""
2	LLM backend abstraction — unified provider interface with auto-discovery.
3
4	Provides a common interface for multiple LLM providers (Anthropic, OpenAI,
5	Ollama). SDK imports are lazy and guarded so that missing optional dependencies
6	produce a clear, actionable ImportError rather than a confusing traceback.
7
8	Usage::
9
10	from navegador.llm import get_provider, auto_provider, discover_providers
11
12	# Explicit provider
13	provider = get_provider("anthropic", model="claude-3-5-haiku-20241022")
14	response = provider.complete("Explain this function: ...")
15
16	# Auto-detect the first available SDK
17	provider = auto_provider()
18
19	# See what is installed
20	available = discover_providers() # e.g. ["anthropic", "openai"]
21	"""
22
23	from __future__ import annotations
24
25	from abc iimport ABC, abstractmethod
26
27	# ── Abstract base ─────────────────────────────────────────────────────────────
28
29
30	class LLMProvider(ABC):
31	"""Abstract interface that every concrete LLM provider must satisfy."""
32
33	@abstractmethod
34	def complete(self, prompt: str, **kwargs) -> str:
35	"""
36	Send prompt to the model and return the completion as a string.
37
38	Args:
39	prompt: The user/system prompt text.
40	**kwargs: Provider-specific options (temperature, max_tokens, …).
41
42	Returns:
43	The model's text response.
44	"""
45
46	@abstractmethod
47	def embed(self, text: str) -> list[float]:
48	"""
49	Return an embedding vector for text.
50
51	Args:
52	text: The input string to embed.
53
54	Returns:
55	A list of floats representing the embedding.
56	"""
57
58	@property
59	@abstractmethod
60	def name(self) -> str:
61	"""Short identifier for the provider, e.g. ``"anthropic"``."""
62
63	@property
64	@abstractmethod
65	def model(self) -> str:
66	"""Model identifier used for API calls, e.g. ``"claude-3-5-haiku-20241022"``."""
67
68
69	# ── Concrete providers ────────────────────────────────────────────────────────
70
71
72	class AnthropicProvider(LLMProvider):
73	"""
74	LLM provider backed by the ``anthropic`` Python SDK.
75
76	Install::
77
78	pip install anthropic
79
80	Args:
81	model: Anthropic model ID (default ``"claude-3-5-haiku-20241022"``).
82	"""
83
84	_DEFAULT_MODEL = "claude-3-5-haiku-20241022"
85
86	def __init__(self, model: str = "") -> None:
87	try:
88	import anthropic # noqa: F401
89	except ImportError as exc:
90	raise ImportError(
91	"The 'anthropic' package is required to use AnthropicProvider. "
92	"Install it with: pip install anthropic"
93	) from exc
94
95	self._model = model or self._DEFAULT_MODEL
96
97	import anthropic
98
99	self._client = anthropic.Anthropic()
100
101	@property
102	def name(self) -> str:
103	return "anthropic"
104
105	@property
106	def model(self) -> str:
107	return self._model
108
109	def complete(self, prompt: str, **kwargs) -> str:
110	"""Call the Anthropic Messages API and return the first text block."""
111	max_tokens = kwargs.pop("max_tokens", 1024)
112	message = self._client.messages.create(
113	model=self._model,
114	max_tokens=max_tokens,
115	messages=[{"role": "user", "content": prompt}],
116	**kwargs,
117	)
118	return message.content[0].text
119
120	def embed(self, text: str) -> list[float]:
121	"""
122	Anthropic does not currently expose a public embeddings API.
123
124	Raises:
125	NotImplementedError: Always.
126	"""
127	raise NotImplementedError(
128	"Anthropic does not provide a public embeddings API. "
129	"Use OpenAIProvider or OllamaProvider for embeddings."
130	)
131
132
133	class OpenAIProvider(LLMProvider):
134	"""
135	LLM provider backed by the ``openai`` Python SDK.
136
137	Install::
138
139	pip install openai
140
141	Args:
142	model: OpenAI model ID (default ``"gpt-4o-mini"``).
143	"""
144
145	_DEFAULT_MODEL = "gpt-4o-mini"
146
147	def __init__(self, model: str = "") -> None:
148	try:
149	import openai # noqa: F401
150	except ImportError as exc:
151	raise ImportError(
152	"The 'openai' package is required to use OpenAIProvider. "
153	"Install it with: pip install openai"
154	) from exc
155
156	self._model = model or self._DEFAULT_MODEL
157
158	import openai
159
160	self._client = openai.OpenAI()
161
162	@property
163	def name(self) -> str:
164	return "openai"
165
166	@property
167	def model(self) -> str:
168	return self._model
169
170	def complete(self, prompt: str, **kwargs) -> str:
171	"""Call the OpenAI Chat Completions API and return the assistant message."""
172	response = self._client.chat.completions.create(
173	model=self._model,
174	messages=[{"role": "user", "content": prompt}],
175	**kwargs,
176	)
177	return response.choices[0].message.content
178
179	def embed(self, text: str) -> list[float]:
180	"""Call the OpenAI Embeddings API and return the embedding vector."""
181	embed_model = "text-embedding-3-small"
182	response = self._client.embeddings.create(input=text, model=embed_model)
183	return response.data[0].embedding
184
185
186	class OllamaProvider(LLMProvider):
187	"""
188	LLM provider backed by the ``ollama`` Python SDK (local models via Ollama).
189
190	Install::
191
192	pip install ollama
193
194	The Ollama server must be running locally (``ollama serve``).
195
196	Args:
197	model: Ollama model tag (default ``"llama3.2"``).
198	"""
199
200	_DEFAULT_MODEL = "llama3.2"
201
202	def __init__(self, model: str = "") -> None:
203	try:
204	import ollama # noqa: F401
205	except ImportError as exc:
206	raise ImportError(
207	"The 'ollama' package is required to use OllamaProvider. "
208	"Install it with: pip install ollama"
209	) from exc
210
211	self._model = model or self._DEFAULT_MODEL
212
213	import ollama
214
215	self._client = ollama.Client()
216
217	@property
218	def name(self) -> str:
219	return "ollama"
220
221	@property
222	def model(self) -> str:
223	return self._model
224
225	def complete(self, prompt: str, **kwargs) -> str:
226	"""Call the Ollama chat API and return the assistant message content."""
227	response = self._client.chat(
228	model=self._model,
229	messages=[{"role": "user", "content": prompt}],
230	**kwargs,
231	)
232	return response["message"]["content"]
233
234	def embed(self, text: str) -> list[float]:
235	"""Call the Ollama embeddings API and return the embedding vector."""
236	response = self._client.embeddings(model=self._model, prompt=text)
237	return response["embedding"]
238
239
240	# ── Discovery & factory ───────"
241	rovider_namf"Valid options are: {sorted(_PROVIDER_CLASS_MAP)}"
242	)
243	cls = _PROVIDER_CLASS_MAP[name]
244	return cls(model=model)
245
246
247	def auto_provider(model: str = "") -> LLMProvider:
248	"""
249	Return the first available LLM provider based on installed SDKs.
250
251	Priority order: anthropic → openai → ollama.
252
253	Args:
254	model: Optional model ID forwarded to the provider constructor.
255
256	Returns:
257	An :class:`LLMProvider` instance for the first available SDK.
258
259	Raises:
260	RuntimeError: If no supported LLM SDK is installed.
261	"""
262	for provider_name in _PROVIDER_NAMES:
263	sdk_name = _PROVIDER_SDK_MAP[provider_name]
264	try:
265	__import__(sdk_name)
266	except ImportError:
267	continue
268	return get_provider(provider_name, model=model)
269
270	raise RuntimeError(
271	"No LLM SDK is installed. Install at least one of: "
272	"anthropic, openai, ollama.\n"
273	" pip install anthropic # Anthropic Claude\n"
274	" pip install openai # OpenAI GPT\n"
275	" pip install ollama # Ollama (local models)"
276	)

A tests/test_llm.py

+489

		--- a/tests/test_llm.py
		+++ b/tests/test_llm.py
		@@ -0,0 +1,489 @@
	1	+"""
	2	+Tests for navegador/llm.py — LLM backend abstraction.
	3	+
	4	+All tests are fully offline. SDK imports are patched to avoid requiring
	5	+any LLM SDK to be installed in the test environment.
	6	+"""
	7	+
	8	+from __future__ import annotations
	9	+
	10	+import sys
	11	+from unittest.mock import MagicMock, patch
	12	+
	13	+import pytest
	14	+
	15	+
	16	+# ── Helpers ───────────────────────────────────────────────────────────────────
	17	+
	18	+
	19	+def _block_import(name: str):
	20	+ """
	21	+ Context manager that makes ``import <name>`` raise ImportError for the
	22	+ duration of the block, even if the package is installed.
	23	+ """
	24	+
	25	+ class _Blocker:
	26	+ def __enter__(self):
	27	+ self._original = sys.modules.get(name, None)
	28	+ sys.modules[name] = None # type: ignore[assignment]
	29	+ return self
	30	+
	31	+ def __exit__(self, *_):
	32	+ if self._original is None:
	33	+ sys.modules.pop(name, None)
	34	+ else:
	35	+ sys.modules[name] = self._original
	36	+
	37	+ return _Blocker()
	38	+
	39	+
	40	+def _fake_anthropic_module():
	41	+ """Return a minimal mock that satisfies AnthropicProvider's usage."""
	42	+ mod = MagicMock()
	43	+ client = MagicMock()
	44	+ message = MagicMock()
	45	+ message.content = [MagicMock(text="hello from anthropic")]
	46	+ client.messages.create.return_value = message
	47	+ mod.Anthropic.return_value = client
	48	+ return mod, client
	49	+
	50	+
	51	+def _fake_openai_module():
	52	+ """Return a minimal mock that satisfies OpenAIProvider's usage."""
	53	+ mod = MagicMock()
	54	+ client = MagicMock()
	55	+ choice = MagicMock()
	56	+ choice.message.content = "hello from openai"
	57	+ response = MagicMock()
	58	+ response.choices = [choice]
	59	+ client.chat.completions.create.return_value = response
	60	+ embed_data = MagicMock()
	61	+ embed_data.embedding = [0.1, 0.2, 0.3]
	62	+ embed_response = MagicMock()
	63	+ embed_response.data = [embed_data]
	64	+ client.embeddings.create.return_value = embed_response
	65	+ mod.OpenAI.return_value = client
	66	+ return mod, client
	67	+
	68	+
	69	+def _fake_ollama_module():
	70	+ """Return a minimal mock that satisfies OllamaProvider's usage."""
	71	+ mod = MagicMock()
	72	+ client = MagicMock()
	73	+ client.chat.return_value = {"message": {"content": "hello from ollama"}}
	74	+ client.embeddings.return_value = {"embedding": [0.4, 0.5, 0.6]}
	75	+ mod.Client.return_value = client
	76	+ return mod, client
	77	+
	78	+
	79	+# ── AnthropicProvider ─────────────────────────────────────────────────────────
	80	+
	81	+
	82	+class TestAnthropicProvider:
	83	+ def test_raises_import_error_when_sdk_missing(self):
	84	+ with _block_import("anthropic"):
	85	+ # Remove cached module from navegador.llm so the guard re-runs
	86	+ import importlib
	87	+
	88	+ import navegador.llm as llm_mod
	89	+
	90	+ importlib.reload(llm_mod)
	91	+ with pytest.raises(ImportError, match="pip install anthropic"):
	92	+ llm_mod.AnthropicProvider()
	93	+
	94	+ def test_name_is_anthropic(self):
	95	+ fake_mod, _ = _fake_anthropic_module()
	96	+ with patch.dict(sys.modules, {"anthropic": fake_mod}):
	97	+ import importlib
	98	+
	99	+ import navegador.llm as llm_mod
	100	+
	101	+ importlib.reload(llm_mod)
	102	+ p = llm_mod.AnthropicProvider()
	103	+ assert p.name == "anthropic"
	104	+
	105	+ def test_default_model(self):
	106	+ fake_mod, _ = _fake_anthropic_module()
	107	+ with patch.dict(sys.modules, {"anthropic": fake_mod}):
	108	+ import importlib
	109	+
	110	+ import navegador.llm as llm_mod
	111	+
	112	+ importlib.reload(llm_mod)
	113	+ p = llm_mod.AnthropicProvider()
	114	+ assert p.model == "claude-3-5-haiku-20241022"
	115	+
	116	+ def test_custom_model(self):
	117	+ fake_mod, _ = _fake_anthropic_module()
	118	+ with patch.dict(sys.modules, {"anthropic": fake_mod}):
	119	+ import importlib
	120	+
	121	+ import navegador.llm as llm_mod
	122	+
	123	+ importlib.reload(llm_mod)
	124	+ p = llm_mod.AnthropicProvider(model="claude-opus-4")
	125	+ assert p.model == "claude-opus-4"
	126	+
	127	+ def test_complete_returns_text(self):
	128	+ fake_mod, client = _fake_anthropic_module()
	129	+ with patch.dict(sys.modules, {"anthropic": fake_mod}):
	130	+ import importlib
	131	+
	132	+ import navegador.llm as llm_mod
	133	+
	134	+ importlib.reload(llm_mod)
	135	+ p = llm_mod.AnthropicProvider()
	136	+ result = p.complete("say hello")
	137	+ assert result == "hello from anthropic"
	138	+ client.messages.create.assert_called_once()
	139	+
	140	+ def test_complete_passes_max_tokens(self):
	141	+ fake_mod, client = _fake_anthropic_module()
	142	+ with patch.dict(sys.modules, {"anthropic": fake_mod}):
	143	+ import importlib
	144	+
	145	+ import navegador.llm as llm_mod
	146	+
	147	+ importlib.reload(llm_mod)
	148	+ p = llm_mod.AnthropicProvider()
	149	+ p.complete("hi", max_tokens=512)
	150	+ _, kwargs = client.messages.create.call_args
	151	+ assert kwargs["max_tokens"] == 512
	152	+
	153	+ def test_embed_raises_not_implemented(self):
	154	+ fake_mod, _ = _fake_anthropic_module()
	155	+ with patch.dict(sys.modules, {"anthropic": fake_mod}):
	156	+ import importlib
	157	+
	158	+ import navegador.llm as llm_mod
	159	+
	160	+ importlib.reload(llm_mod)
	161	+ p = llm_mod.AnthropicProvider()
	162	+ with pytest.raises(NotImplementedError):
	163	+ p.embed("text")
	164	+
	165	+
	166	+# ── OpenAIProvider ────────────────────────────────────────────────────────────
	167	+
	168	+
	169	+class TestOpenAIProvider:
	170	+ def test_raises_import_error_when_sdk_missing(self):
	171	+ with _block_import("openai"):
	172	+ import importlib
	173	+
	174	+ import navegador.llm as llm_mod
	175	+
	176	+ importlib.reload(llm_mod)
	177	+ with pytest.raises(ImportError, match="pip install openai"):
	178	+ llm_mod.OpenAIProvider()
	179	+
	180	+ def test_name_is_openai(self):
	181	+ fake_mod, _ = _fake_openai_module()
	182	+ with patch.dict(sys.modules, {"openai": fake_mod}):
	183	+ import importlib
	184	+
	185	+ import navegador.llm as llm_mod
	186	+
	187	+ importlib.reload(llm_mod)
	188	+ p = llm_mod.OpenAIProvider()
	189	+ assert p.name == "openai"
	190	+
	191	+ def test_default_model(self):
	192	+ fake_mod, _ = _fake_openai_module()
	193	+ with patch.dict(sys.modules, {"openai": fake_mod}):
	194	+ import importlib
	195	+
	196	+ import navegador.llm as llm_mod
	197	+
	198	+ importlib.reload(llm_mod)
	199	+ p = llm_mod.OpenAIProvider()
	200	+ assert p.model == "gpt-4o-mini"
	201	+
	202	+ def test_custom_model(self):
	203	+ fake_mod, _ = _fake_openai_module()
	204	+ with patch.dict(sys.modules, {"openai": fake_mod}):
	205	+ import importlib
	206	+
	207	+ import navegador.llm as llm_mod
	208	+
	209	+ importlib.reload(llm_mod)
	210	+ p = llm_mod.OpenAIProvider(model="gpt-4o")
	211	+ assert p.model == "gpt-4o"
	212	+
	213	+ def test_complete_returns_text(self):
	214	+ fake_mod, client = _fake_openai_module()
	215	+ with patch.dict(sys.modules, {"openai": fake_mod}):
	216	+ import importlib
	217	+
	218	+ import navegador.llm as llm_mod
	219	+
	220	+ importlib.reload(llm_mod)
	221	+ p = llm_mod.OpenAIProvider()
	222	+ result = p.complete("say hello")
	223	+ assert result == "hello from openai"
	224	+ client.chat.completions.create.assert_called_once()
	225	+
	226	+ def test_embed_returns_list_of_floats(self):
	227	+ fake_mod, client = _fake_openai_module()
	228	+ with patch.dict(sys.modules, {"openai": fake_mod}):
	229	+ import importlib
	230	+
	231	+ import navegador.llm as llm_mod
	232	+
	233	+ importlib.reload(llm_mod)
	234	+ p = llm_mod.OpenAIProvider()
	235	+ result = p.embed("hello world")
	236	+ assert result == [0.1, 0.2, 0.3]
	237	+ client.embeddings.create.assert_called_once()
	238	+
	239	+
	240	+# ── OllamaProvider ────────────────────────────────────────────────────────────
	241	+
	242	+
	243	+class TestOllamaProvider:
	244	+ def test_raises_import_error_when_sdk_missing(self):
	245	+ with _block_import("ollama"):
	246	+ import importlib
	247	+
	248	+ import navegador.llm as llm_mod
	249	+
	250	+ importlib.reload(llm_mod)
	251	+ with pytest.raises(ImportError, match="pip install ollama"):
	252	+ llm_mod.OllamaProvider()
	253	+
	254	+ def test_name_is_ollama(self):
	255	+ fake_mod, _ = _fake_ollama_module()
	256	+ with patch.dict(sys.modules, {"ollama": fake_mod}):
	257	+ import importlib
	258	+
	259	+ import navegador.llm as llm_mod
	260	+
	261	+ importlib.reload(llm_mod)
	262	+ p = llm_mod.OllamaProvider()
	263	+ assert p.name == "ollama"
	264	+
	265	+ def test_default_model(self):
	266	+ fake_mod, _ = _fake_ollama_module()
	267	+ with patch.dict(sys.modules, {"ollama": fake_mod}):
	268	+ import importlib
	269	+
	270	+ import navegador.llm as llm_mod
	271	+
	272	+ importlib.reload(llm_mod)
	273	+ p = llm_mod.OllamaProvider()
	274	+ assert p.model == "llama3.2"
	275	+
	276	+ def test_custom_model(self):
	277	+ fake_mod, _ = _fake_ollama_module()
	278	+ with patch.dict(sys.modules, {"ollama": fake_mod}):
	279	+ import importlib
	280	+
	281	+ import navegador.llm as llm_mod
	282	+
	283	+ importlib.reload(llm_mod)
	284	+ p = llm_mod.OllamaProvider(model="mistral")
	285	+ assert p.model == "mistral"
	286	+
	287	+ def test_complete_returns_text(self):
	288	+ fake_mod, client = _fake_ollama_module()
	289	+ with patch.dict(sys.modules, {"ollama": fake_mod}):
	290	+ import importlib
	291	+
	292	+ import navegador.llm as llm_mod
	293	+
	294	+ importlib.reload(llm_mod)
	295	+ p = llm_mod.OllamaProvider()
	296	+ result = p.complete("say hello")
	297	+ assert result == "hello from ollama"
	298	+ client.chat.assert_called_once()
	299	+
	300	+ def test_embed_returns_list_of_floats(self):
	301	+ fake_mod, client = _fake_ollama_module()
	302	+ with patch.dict(sys.modules, {"ollama": fake_mod}):
	303	+ import importlib
	304	+
	305	+ import navegador.llm as llm_mod
	306	+
	307	+ importlib.reload(llm_mod)
	308	+ p = llm_mod.OllamaProvider()
	309	+ result = p.embed("hello world")
	310	+ assert result == [0.4, 0.5, 0.6]
	311	+ client.embeddings.assert_called_once()
	312	+
	313	+
	314	+# ── discover_providers ────────────────────────────────────────────────────────
	315	+
	316	+
	317	+class TestDiscoverProviders:
	318	+ def _reload(self, modules: dict):
	319	+ import importlib
	320	+
	321	+ import navegador.llm as llm_mod
	322	+
	323	+ importlib.reload(llm_mod)
	324	+ return llm_mod
	325	+
	326	+ def test_all_available(self):
	327	+ fake_a, _ = _fake_anthropic_module()
	328	+ fake_o, _ = _fake_openai_module()
	329	+ fake_ol, _ = _fake_ollama_module()
	330	+ with patch.dict(
	331	+ sys.modules,
	332	+ {"anthropic": fake_a, "openai": fake_o, "ollama": fake_ol},
	333	+ ):
	334	+ llm_mod = self._reload({})
	335	+ result = llm_mod.discover_providers()
	336	+ assert result == ["anthropic", "openai", "ollama"]
	337	+
	338	+ def test_only_openai_available(self):
	339	+ fake_o, _ = _fake_openai_module()
	340	+ with (
	341	+ _block_import("anthropic"),
	342	+ patch.dict(sys.modules, {"openai": fake_o}),
	343	+ _block_import("ollama"),
	344	+ ):
	345	+ llm_mod = self._reload({})
	346	+ result = llm_mod.discover_providers()
	347	+ assert result == ["openai"]
	348	+
	349	+ def test_none_available(self):
	350	+ with _block_import("anthropic"), _block_import("openai"), _block_import("ollama"):
	351	+ llm_mod = self._reload({})
	352	+ result = llm_mod.discover_providers()
	353	+ assert result == []
	354	+
	355	+ def test_preserves_priority_order(self):
	356	+ fake_a, _ = _fake_anthropic_module()
	357	+ fake_ol, _ = _fake_ollama_module()
	358	+ with (
	359	+ patch.dict(sys.modules, {"anthropic": fake_a, "ollama": fake_ol}),
	360	+ _block_import("openai"),
	361	+ ):
	362	+ llm_mod = self._reload({})
	363	+ result = llm_mod.discover_providers()
	364	+ assert result == ["anthropic", "ollama"]
	365	+
	366	+
	367	+# ── get_provider ──────────────────────────────────────────────────────────────
	368	+
	369	+
	370	+class TestGetProvider:
	371	+ def _reload(self):
	372	+ import importlib
	373	+
	374	+ import navegador.llm as llm_mod
	375	+
	376	+ importlib.reload(llm_mod)
	377	+ return llm_mod
	378	+
	379	+ def test_returns_anthropic_provider(self):
	380	+ fake_mod, _ = _fake_anthropic_module()
	381	+ with patch.dict(sys.modules, {"anthropic": fake_mod}):
	382	+ llm_mod = self._reload()
	383	+ p = llm_mod.get_provider("anthropic")
	384	+ assert p.name == "anthropic"
	385	+
	386	+ def test_returns_openai_provider(self):
	387	+ fake_mod, _ = _fake_openai_module()
	388	+ with patch.dict(sys.modules, {"openai": fake_mod}):
	389	+ llm_mod = self._reload()
	390	+ p = llm_mod.get_provider("openai")
	391	+ assert p.name == "openai"
	392	+
	393	+ def test_returns_ollama_provider(self):
	394	+ fake_mod, _ = _fake_ollama_module()
	395	+ with patch.dict(sys.modules, {"ollama": fake_mod}):
	396	+ llm_mod = self._reload()
	397	+ p = llm_mod.get_provider("ollama")
	398	+ assert p.name == "ollama"
	399	+
	400	+ def test_passes_model_argument(self):
	401	+ fake_mod, _ = _fake_anthropic_module()
	402	+ with patch.dict(sys.modules, {"anthropic": fake_mod}):
	403	+ llm_mod = self._reload()
	404	+ p = llm_mod.get_provider("anthropic", model="claude-opus-4")
	405	+ assert p.model == "claude-opus-4"
	406	+
	407	+ def test_unknown_provider_raises_value_error(self):
	408	+ import importlib
	409	+
	410	+ import navegador.llm as llm_mod
	411	+
	412	+ importlib.reload(llm_mod)
	413	+ with pytest.raises(ValueError, match="Unknown LLM provider"):
	414	+ llm_mod.get_provider("grok")
	415	+
	416	+ def test_unknown_provider_message_includes_valid_options(self):
	417	+ import importlib
	418	+
	419	+ import navegador.llm as llm_mod
	420	+
	421	+ importlib.reload(llm_mod)
	422	+ with pytest.raises(ValueError, match="anthropic"):
	423	+ llm_mod.get_provider("nonexistent")
	424	+
	425	+
	426	+# ── auto_provider ─────────────────────────────────────────────────────────────
	427	+
	428	+
	429	+class TestAutoProvider:
	430	+ def _reload(self):
	431	+ import importlib
	432	+
	433	+ import navegador.llm as llm_mod
	434	+
	435	+ importlib.reload(llm_mod)
	436	+ return llm_mod
	437	+
	438	+ def test_prefers_anthropic_when_all_available(self):
	439	+ fake_a, _ = _fake_anthropic_module()
	440	+ fake_o, _ = _fake_openai_module()
	441	+ fake_ol, _ = _fake_ollama_module()
	442	+ with patch.dict(
	443	+ sys.modules,
	444	+ {"anthropic": fake_a, "openai": fake_o, "ollama": fake_ol},
	445	+ ):
	446	+ llm_mod = self._reload()
	447	+ p = llm_mod.auto_provider()
	448	+ assert p.name == "anthropic"
	449	+
	450	+ def test_falls_back_to_openai_when_anthropic_missing(self):
	451	+ fake_o, _ = _fake_openai_module()
	452	+ fake_ol, _ = _fake_ollama_module()
	453	+ with (
	454	+ _block_import("anthropic"),
	455	+ patch.dict(sys.modules, {"openai": fake_o, "ollama": fake_ol}),
	456	+ ):
	457	+ llm_mod = self._reload()
	458	+ p = llm_mod.auto_provider()
	459	+ assert p.name == "openai"
	460	+
	461	+ def test_falls_back_to_ollama_when_anthropic_and_openai_missing(self):
	462	+ fake_ol, _ = _fake_ollama_module()
	463	+ with (
	464	+ _block_import("anthropic"),
	465	+ _block_import("openai"),
	466	+ patch.dict(sys.modules, {"ollama": fake_ol}),
	467	+ ):
	468	+ llm_mod = self._reload()
	469	+ p = llm_mod.auto_provider()
	470	+ assert p.name == "ollama"
	471	+
	472	+ def test_raises_runtime_error_when_no_sdk_available(self):
	473	+ with _block_import("anthropic"), _block_import("openai"), _block_import("ollama"):
	474	+ llm_mod = self._reload()
	475	+ with pytest.raises(RuntimeError, match="No LLM SDK is installed"):
	476	+ llm_mod.auto_provider()
	477	+
	478	+ def test_runtime_error_message_includes_install_hints(self):
	479	+ with _block_import("anthropic"), _block_import("openai"), _block_import("ollama"):
	480	+ llm_mod = self._reload()
	481	+ with pytest.raises(RuntimeError, match="pip install"):
	482	+ llm_mod.auto_provider()
	483	+
	484	+ def test_passes_model_to_provider(self):
	485	+ fake_a, _ = _fake_anthropic_module()
	486	+ with patch.dict(sys.modules, {"anthropic": fake_a}):
	487	+ llm_mod = self._reload()
	488	+ p = llm_mod.auto_provider(model="claude-opus-4")
	489	+ assert p.model == "claude-opus-4"

	--- a/tests/test_llm.py
	+++ b/tests/test_llm.py
	@@ -0,0 +1,489 @@

	--- a/tests/test_llm.py
	+++ b/tests/test_llm.py
	@@ -0,0 +1,489 @@
1	"""
2	Tests for navegador/llm.py — LLM backend abstraction.
3
4	All tests are fully offline. SDK imports are patched to avoid requiring
5	any LLM SDK to be installed in the test environment.
6	"""
7
8	from __future__ import annotations
9
10	import sys
11	from unittest.mock import MagicMock, patch
12
13	import pytest
14
15
16	# ── Helpers ───────────────────────────────────────────────────────────────────
17
18
19	def _block_import(name: str):
20	"""
21	Context manager that makes ``import <name>`` raise ImportError for the
22	duration of the block, even if the package is installed.
23	"""
24
25	class _Blocker:
26	def __enter__(self):
27	self._original = sys.modules.get(name, None)
28	sys.modules[name] = None # type: ignore[assignment]
29	return self
30
31	def __exit__(self, *_):
32	if self._original is None:
33	sys.modules.pop(name, None)
34	else:
35	sys.modules[name] = self._original
36
37	return _Blocker()
38
39
40	def _fake_anthropic_module():
41	"""Return a minimal mock that satisfies AnthropicProvider's usage."""
42	mod = MagicMock()
43	client = MagicMock()
44	message = MagicMock()
45	message.content = [MagicMock(text="hello from anthropic")]
46	client.messages.create.return_value = message
47	mod.Anthropic.return_value = client
48	return mod, client
49
50
51	def _fake_openai_module():
52	"""Return a minimal mock that satisfies OpenAIProvider's usage."""
53	mod = MagicMock()
54	client = MagicMock()
55	choice = MagicMock()
56	choice.message.content = "hello from openai"
57	response = MagicMock()
58	response.choices = [choice]
59	client.chat.completions.create.return_value = response
60	embed_data = MagicMock()
61	embed_data.embedding = [0.1, 0.2, 0.3]
62	embed_response = MagicMock()
63	embed_response.data = [embed_data]
64	client.embeddings.create.return_value = embed_response
65	mod.OpenAI.return_value = client
66	return mod, client
67
68
69	def _fake_ollama_module():
70	"""Return a minimal mock that satisfies OllamaProvider's usage."""
71	mod = MagicMock()
72	client = MagicMock()
73	client.chat.return_value = {"message": {"content": "hello from ollama"}}
74	client.embeddings.return_value = {"embedding": [0.4, 0.5, 0.6]}
75	mod.Client.return_value = client
76	return mod, client
77
78
79	# ── AnthropicProvider ─────────────────────────────────────────────────────────
80
81
82	class TestAnthropicProvider:
83	def test_raises_import_error_when_sdk_missing(self):
84	with _block_import("anthropic"):
85	# Remove cached module from navegador.llm so the guard re-runs
86	import importlib
87
88	import navegador.llm as llm_mod
89
90	importlib.reload(llm_mod)
91	with pytest.raises(ImportError, match="pip install anthropic"):
92	llm_mod.AnthropicProvider()
93
94	def test_name_is_anthropic(self):
95	fake_mod, _ = _fake_anthropic_module()
96	with patch.dict(sys.modules, {"anthropic": fake_mod}):
97	import importlib
98
99	import navegador.llm as llm_mod
100
101	importlib.reload(llm_mod)
102	p = llm_mod.AnthropicProvider()
103	assert p.name == "anthropic"
104
105	def test_default_model(self):
106	fake_mod, _ = _fake_anthropic_module()
107	with patch.dict(sys.modules, {"anthropic": fake_mod}):
108	import importlib
109
110	import navegador.llm as llm_mod
111
112	importlib.reload(llm_mod)
113	p = llm_mod.AnthropicProvider()
114	assert p.model == "claude-3-5-haiku-20241022"
115
116	def test_custom_model(self):
117	fake_mod, _ = _fake_anthropic_module()
118	with patch.dict(sys.modules, {"anthropic": fake_mod}):
119	import importlib
120
121	import navegador.llm as llm_mod
122
123	importlib.reload(llm_mod)
124	p = llm_mod.AnthropicProvider(model="claude-opus-4")
125	assert p.model == "claude-opus-4"
126
127	def test_complete_returns_text(self):
128	fake_mod, client = _fake_anthropic_module()
129	with patch.dict(sys.modules, {"anthropic": fake_mod}):
130	import importlib
131
132	import navegador.llm as llm_mod
133
134	importlib.reload(llm_mod)
135	p = llm_mod.AnthropicProvider()
136	result = p.complete("say hello")
137	assert result == "hello from anthropic"
138	client.messages.create.assert_called_once()
139
140	def test_complete_passes_max_tokens(self):
141	fake_mod, client = _fake_anthropic_module()
142	with patch.dict(sys.modules, {"anthropic": fake_mod}):
143	import importlib
144
145	import navegador.llm as llm_mod
146
147	importlib.reload(llm_mod)
148	p = llm_mod.AnthropicProvider()
149	p.complete("hi", max_tokens=512)
150	_, kwargs = client.messages.create.call_args
151	assert kwargs["max_tokens"] == 512
152
153	def test_embed_raises_not_implemented(self):
154	fake_mod, _ = _fake_anthropic_module()
155	with patch.dict(sys.modules, {"anthropic": fake_mod}):
156	import importlib
157
158	import navegador.llm as llm_mod
159
160	importlib.reload(llm_mod)
161	p = llm_mod.AnthropicProvider()
162	with pytest.raises(NotImplementedError):
163	p.embed("text")
164
165
166	# ── OpenAIProvider ────────────────────────────────────────────────────────────
167
168
169	class TestOpenAIProvider:
170	def test_raises_import_error_when_sdk_missing(self):
171	with _block_import("openai"):
172	import importlib
173
174	import navegador.llm as llm_mod
175
176	importlib.reload(llm_mod)
177	with pytest.raises(ImportError, match="pip install openai"):
178	llm_mod.OpenAIProvider()
179
180	def test_name_is_openai(self):
181	fake_mod, _ = _fake_openai_module()
182	with patch.dict(sys.modules, {"openai": fake_mod}):
183	import importlib
184
185	import navegador.llm as llm_mod
186
187	importlib.reload(llm_mod)
188	p = llm_mod.OpenAIProvider()
189	assert p.name == "openai"
190
191	def test_default_model(self):
192	fake_mod, _ = _fake_openai_module()
193	with patch.dict(sys.modules, {"openai": fake_mod}):
194	import importlib
195
196	import navegador.llm as llm_mod
197
198	importlib.reload(llm_mod)
199	p = llm_mod.OpenAIProvider()
200	assert p.model == "gpt-4o-mini"
201
202	def test_custom_model(self):
203	fake_mod, _ = _fake_openai_module()
204	with patch.dict(sys.modules, {"openai": fake_mod}):
205	import importlib
206
207	import navegador.llm as llm_mod
208
209	importlib.reload(llm_mod)
210	p = llm_mod.OpenAIProvider(model="gpt-4o")
211	assert p.model == "gpt-4o"
212
213	def test_complete_returns_text(self):
214	fake_mod, client = _fake_openai_module()
215	with patch.dict(sys.modules, {"openai": fake_mod}):
216	import importlib
217
218	import navegador.llm as llm_mod
219
220	importlib.reload(llm_mod)
221	p = llm_mod.OpenAIProvider()
222	result = p.complete("say hello")
223	assert result == "hello from openai"
224	client.chat.completions.create.assert_called_once()
225
226	def test_embed_returns_list_of_floats(self):
227	fake_mod, client = _fake_openai_module()
228	with patch.dict(sys.modules, {"openai": fake_mod}):
229	import importlib
230
231	import navegador.llm as llm_mod
232
233	importlib.reload(llm_mod)
234	p = llm_mod.OpenAIProvider()
235	result = p.embed("hello world")
236	assert result == [0.1, 0.2, 0.3]
237	client.embeddings.create.assert_called_once()
238
239
240	# ── OllamaProvider ────────────────────────────────────────────────────────────
241
242
243	class TestOllamaProvider:
244	def test_raises_import_error_when_sdk_missing(self):
245	with _block_import("ollama"):
246	import importlib
247
248	import navegador.llm as llm_mod
249
250	importlib.reload(llm_mod)
251	with pytest.raises(ImportError, match="pip install ollama"):
252	llm_mod.OllamaProvider()
253
254	def test_name_is_ollama(self):
255	fake_mod, _ = _fake_ollama_module()
256	with patch.dict(sys.modules, {"ollama": fake_mod}):
257	import importlib
258
259	import navegador.llm as llm_mod
260
261	importlib.reload(llm_mod)
262	p = llm_mod.OllamaProvider()
263	assert p.name == "ollama"
264
265	def test_default_model(self):
266	fake_mod, _ = _fake_ollama_module()
267	with patch.dict(sys.modules, {"ollama": fake_mod}):
268	import importlib
269
270	import navegador.llm as llm_mod
271
272	importlib.reload(llm_mod)
273	p = llm_mod.OllamaProvider()
274	assert p.model == "llama3.2"
275
276	def test_custom_model(self):
277	fake_mod, _ = _fake_ollama_module()
278	with patch.dict(sys.modules, {"ollama": fake_mod}):
279	import importlib
280
281	import navegador.llm as llm_mod
282
283	importlib.reload(llm_mod)
284	p = llm_mod.OllamaProvider(model="mistral")
285	assert p.model == "mistral"
286
287	def test_complete_returns_text(self):
288	fake_mod, client = _fake_ollama_module()
289	with patch.dict(sys.modules, {"ollama": fake_mod}):
290	import importlib
291
292	import navegador.llm as llm_mod
293
294	importlib.reload(llm_mod)
295	p = llm_mod.OllamaProvider()
296	result = p.complete("say hello")
297	assert result == "hello from ollama"
298	client.chat.assert_called_once()
299
300	def test_embed_returns_list_of_floats(self):
301	fake_mod, client = _fake_ollama_module()
302	with patch.dict(sys.modules, {"ollama": fake_mod}):
303	import importlib
304
305	import navegador.llm as llm_mod
306
307	importlib.reload(llm_mod)
308	p = llm_mod.OllamaProvider()
309	result = p.embed("hello world")
310	assert result == [0.4, 0.5, 0.6]
311	client.embeddings.assert_called_once()
312
313
314	# ── discover_providers ────────────────────────────────────────────────────────
315
316
317	class TestDiscoverProviders:
318	def _reload(self, modules: dict):
319	import importlib
320
321	import navegador.llm as llm_mod
322
323	importlib.reload(llm_mod)
324	return llm_mod
325
326	def test_all_available(self):
327	fake_a, _ = _fake_anthropic_module()
328	fake_o, _ = _fake_openai_module()
329	fake_ol, _ = _fake_ollama_module()
330	with patch.dict(
331	sys.modules,
332	{"anthropic": fake_a, "openai": fake_o, "ollama": fake_ol},
333	):
334	llm_mod = self._reload({})
335	result = llm_mod.discover_providers()
336	assert result == ["anthropic", "openai", "ollama"]
337
338	def test_only_openai_available(self):
339	fake_o, _ = _fake_openai_module()
340	with (
341	_block_import("anthropic"),
342	patch.dict(sys.modules, {"openai": fake_o}),
343	_block_import("ollama"),
344	):
345	llm_mod = self._reload({})
346	result = llm_mod.discover_providers()
347	assert result == ["openai"]
348
349	def test_none_available(self):
350	with _block_import("anthropic"), _block_import("openai"), _block_import("ollama"):
351	llm_mod = self._reload({})
352	result = llm_mod.discover_providers()
353	assert result == []
354
355	def test_preserves_priority_order(self):
356	fake_a, _ = _fake_anthropic_module()
357	fake_ol, _ = _fake_ollama_module()
358	with (
359	patch.dict(sys.modules, {"anthropic": fake_a, "ollama": fake_ol}),
360	_block_import("openai"),
361	):
362	llm_mod = self._reload({})
363	result = llm_mod.discover_providers()
364	assert result == ["anthropic", "ollama"]
365
366
367	# ── get_provider ──────────────────────────────────────────────────────────────
368
369
370	class TestGetProvider:
371	def _reload(self):
372	import importlib
373
374	import navegador.llm as llm_mod
375
376	importlib.reload(llm_mod)
377	return llm_mod
378
379	def test_returns_anthropic_provider(self):
380	fake_mod, _ = _fake_anthropic_module()
381	with patch.dict(sys.modules, {"anthropic": fake_mod}):
382	llm_mod = self._reload()
383	p = llm_mod.get_provider("anthropic")
384	assert p.name == "anthropic"
385
386	def test_returns_openai_provider(self):
387	fake_mod, _ = _fake_openai_module()
388	with patch.dict(sys.modules, {"openai": fake_mod}):
389	llm_mod = self._reload()
390	p = llm_mod.get_provider("openai")
391	assert p.name == "openai"
392
393	def test_returns_ollama_provider(self):
394	fake_mod, _ = _fake_ollama_module()
395	with patch.dict(sys.modules, {"ollama": fake_mod}):
396	llm_mod = self._reload()
397	p = llm_mod.get_provider("ollama")
398	assert p.name == "ollama"
399
400	def test_passes_model_argument(self):
401	fake_mod, _ = _fake_anthropic_module()
402	with patch.dict(sys.modules, {"anthropic": fake_mod}):
403	llm_mod = self._reload()
404	p = llm_mod.get_provider("anthropic", model="claude-opus-4")
405	assert p.model == "claude-opus-4"
406
407	def test_unknown_provider_raises_value_error(self):
408	import importlib
409
410	import navegador.llm as llm_mod
411
412	importlib.reload(llm_mod)
413	with pytest.raises(ValueError, match="Unknown LLM provider"):
414	llm_mod.get_provider("grok")
415
416	def test_unknown_provider_message_includes_valid_options(self):
417	import importlib
418
419	import navegador.llm as llm_mod
420
421	importlib.reload(llm_mod)
422	with pytest.raises(ValueError, match="anthropic"):
423	llm_mod.get_provider("nonexistent")
424
425
426	# ── auto_provider ─────────────────────────────────────────────────────────────
427
428
429	class TestAutoProvider:
430	def _reload(self):
431	import importlib
432
433	import navegador.llm as llm_mod
434
435	importlib.reload(llm_mod)
436	return llm_mod
437
438	def test_prefers_anthropic_when_all_available(self):
439	fake_a, _ = _fake_anthropic_module()
440	fake_o, _ = _fake_openai_module()
441	fake_ol, _ = _fake_ollama_module()
442	with patch.dict(
443	sys.modules,
444	{"anthropic": fake_a, "openai": fake_o, "ollama": fake_ol},
445	):
446	llm_mod = self._reload()
447	p = llm_mod.auto_provider()
448	assert p.name == "anthropic"
449
450	def test_falls_back_to_openai_when_anthropic_missing(self):
451	fake_o, _ = _fake_openai_module()
452	fake_ol, _ = _fake_ollama_module()
453	with (
454	_block_import("anthropic"),
455	patch.dict(sys.modules, {"openai": fake_o, "ollama": fake_ol}),
456	):
457	llm_mod = self._reload()
458	p = llm_mod.auto_provider()
459	assert p.name == "openai"
460
461	def test_falls_back_to_ollama_when_anthropic_and_openai_missing(self):
462	fake_ol, _ = _fake_ollama_module()
463	with (
464	_block_import("anthropic"),
465	_block_import("openai"),
466	patch.dict(sys.modules, {"ollama": fake_ol}),
467	):
468	llm_mod = self._reload()
469	p = llm_mod.auto_provider()
470	assert p.name == "ollama"
471
472	def test_raises_runtime_error_when_no_sdk_available(self):
473	with _block_import("anthropic"), _block_import("openai"), _block_import("ollama"):
474	llm_mod = self._reload()
475	with pytest.raises(RuntimeError, match="No LLM SDK is installed"):
476	llm_mod.auto_provider()
477
478	def test_runtime_error_message_includes_install_hints(self):
479	with _block_import("anthropic"), _block_import("openai"), _block_import("ollama"):
480	llm_mod = self._reload()
481	with pytest.raises(RuntimeError, match="pip install"):
482	llm_mod.auto_provider()
483
484	def test_passes_model_to_provider(self):
485	fake_a, _ = _fake_anthropic_module()
486	with patch.dict(sys.modules, {"anthropic": fake_a}):
487	llm_mod = self._reload()
488	p = llm_mod.auto_provider(model="claude-opus-4")
489	assert p.model == "claude-opus-4"

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