fix(providers): resolve lint and unit test failures in OllamaProvider and MistralProvider
- Fix import order in ollama_provider.py (langchain_community before langchain_core) - Remove duplicate shared step definitions from ollama_provider_steps.py - Remove duplicate shared step definitions from mistral_provider_steps.py The duplicate @given step definitions caused AmbiguousStep errors when running the full Behave test suite. Shared steps (provider domain inputs, plan generation graph setup) are already defined in openai_provider_steps.py and loaded by Behave for all feature files.
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HAL9000
Forgejo
@@ -7,12 +7,6 @@ from unittest.mock import MagicMock, patch
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from behave import given, then, when
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from cleveragents.domain.models.core import (
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Context,
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OperationType,
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Plan,
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Project,
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)
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from cleveragents.providers.llm.mistral_provider import MistralChatProvider
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@@ -85,61 +79,6 @@ def _setup_plan_generation_graph(context) -> MagicMock:
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return mock_graph_instance
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@given("I have sample provider domain inputs")
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def step_sample_provider_inputs(context):
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context.project = MagicMock(spec=Project)
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context.plan = MagicMock(spec=Plan)
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context.plan.prompt = "Add placeholder coverage"
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context.contexts = [MagicMock(spec=Context)]
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context.contexts[0].content = "Sample context entry"
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@given('the plan generation graph returns a generated change for "{file_path}"')
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def step_plan_generation_returns_change(context, file_path):
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change = {
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"plan_id": 1,
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"file_path": file_path,
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"operation": OperationType.MODIFY.value,
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"new_content": "# updated content",
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}
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context.plan_generation_state_override = {
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"generated_changes": [change],
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"validation_result": {"status": "PASS"},
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"error": None,
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}
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@given('the plan generation graph emits streaming nodes "{node_list}"')
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def step_plan_generation_stream_nodes(context, node_list):
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nodes = [node.strip() for node in node_list.split(",") if node.strip()]
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state = getattr(context, "plan_generation_state_override", {}) or {}
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events: list[dict[str, Any]] = []
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for node in nodes:
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payload: dict[str, Any] = {"status": "completed"}
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if node == "generate_plan" and isinstance(state.get("generated_changes"), list):
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payload = {
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"generated_changes": state["generated_changes"],
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"status": "completed",
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}
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elif node == "validate" and isinstance(state.get("validation_result"), dict):
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payload = {
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"validation_result": state["validation_result"],
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"status": "completed",
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}
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events.append({node: payload})
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context.plan_generation_stream_events = events
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@given('the plan generation graph raises ValueError "{message}"')
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def step_plan_generation_raises_value_error(context, message):
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context.plan_generation_invoke_side_effect = ValueError(message)
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@given('the plan generation graph raises RuntimeError "{message}"')
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def step_plan_generation_runtime_error(context, message):
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context.plan_generation_invoke_side_effect = RuntimeError(message)
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@given('I set the MISTRAL_API_KEY environment variable to "{api_key}"')
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def step_set_mistral_env_var(context, api_key):
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context.mistral_env_api_key = api_key
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@@ -152,12 +91,8 @@ def step_set_mistral_provider_kwargs(context, kwargs_string):
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context.mistral_provider_kwargs = _parse_kwargs_string(kwargs_string)
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@given(
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'I create a Mistral chat provider with API key "{api_key}" and model "{model}"'
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)
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@when(
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'I create a Mistral chat provider with API key "{api_key}" and model "{model}"'
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)
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@given('I create a Mistral chat provider with API key "{api_key}" and model "{model}"')
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@when('I create a Mistral chat provider with API key "{api_key}" and model "{model}"')
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def step_create_mistral_provider(context, api_key, model):
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patcher = patch("cleveragents.providers.llm.mistral_provider.ChatMistralAI")
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context.chat_mistral_patcher = patcher
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@@ -178,9 +113,7 @@ def step_create_mistral_provider(context, api_key, model):
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context.chat_mistral_instance = mock_chat_mistral_instance
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@when(
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'I create a Mistral chat provider without explicit API key and model "{model}"'
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)
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@when('I create a Mistral chat provider without explicit API key and model "{model}"')
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def step_create_mistral_provider_from_env(context, model):
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patcher = patch("cleveragents.providers.llm.mistral_provider.ChatMistralAI")
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context.chat_mistral_patcher = patcher
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@@ -242,7 +175,9 @@ def step_stream_plan_generation(context):
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'the Mistral provider should construct ChatMistralAI with api key "{api_key}" and model "{model}"'
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)
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def step_assert_chat_mistral_constructor(context, api_key, model):
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assert context.chat_mistral_call is not None, "ChatMistralAI should have been called"
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assert context.chat_mistral_call is not None, (
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"ChatMistralAI should have been called"
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)
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call_args, call_kwargs = context.chat_mistral_call
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assert call_args == (), "ChatMistralAI should be called with keyword arguments"
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assert call_kwargs["api_key"] == api_key
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@@ -259,7 +194,9 @@ def step_assert_chat_mistral_constructor(context, api_key, model):
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'the Mistral provider should include kwargs "{kwargs_string}" in the ChatMistralAI call'
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)
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def step_assert_chat_mistral_kwargs(context, kwargs_string):
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assert context.chat_mistral_call is not None, "ChatMistralAI should have been called"
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assert context.chat_mistral_call is not None, (
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"ChatMistralAI should have been called"
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)
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_, call_kwargs = context.chat_mistral_call
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expected_kwargs = _parse_kwargs_string(kwargs_string)
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for key, value in expected_kwargs.items():
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@@ -6,12 +6,6 @@ from unittest.mock import MagicMock, patch
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from behave import given, then, when
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from cleveragents.domain.models.core import (
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Context,
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OperationType,
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Plan,
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Project,
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)
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from cleveragents.providers.llm.ollama_provider import OllamaChatProvider
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@@ -84,61 +78,6 @@ def _setup_plan_generation_graph(context) -> MagicMock:
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return mock_graph_instance
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@given("I have sample provider domain inputs")
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def step_sample_provider_inputs(context):
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context.project = MagicMock(spec=Project)
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context.plan = MagicMock(spec=Plan)
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context.plan.prompt = "Add placeholder coverage"
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context.contexts = [MagicMock(spec=Context)]
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context.contexts[0].content = "Sample context entry"
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@given('the plan generation graph returns a generated change for "{file_path}"')
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def step_plan_generation_returns_change(context, file_path):
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change = {
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"plan_id": 1,
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"file_path": file_path,
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"operation": OperationType.MODIFY.value,
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"new_content": "# updated content",
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}
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context.plan_generation_state_override = {
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"generated_changes": [change],
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"validation_result": {"status": "PASS"},
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"error": None,
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}
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@given('the plan generation graph emits streaming nodes "{node_list}"')
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def step_plan_generation_stream_nodes(context, node_list):
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nodes = [node.strip() for node in node_list.split(",") if node.strip()]
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state = getattr(context, "plan_generation_state_override", {}) or {}
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events: list[dict[str, Any]] = []
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for node in nodes:
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payload: dict[str, Any] = {"status": "completed"}
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if node == "generate_plan" and isinstance(state.get("generated_changes"), list):
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payload = {
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"generated_changes": state["generated_changes"],
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"status": "completed",
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}
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elif node == "validate" and isinstance(state.get("validation_result"), dict):
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payload = {
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"validation_result": state["validation_result"],
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"status": "completed",
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}
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events.append({node: payload})
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context.plan_generation_stream_events = events
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@given('the plan generation graph raises ValueError "{message}"')
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def step_plan_generation_raises_value_error(context, message):
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context.plan_generation_invoke_side_effect = ValueError(message)
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@given('the plan generation graph raises RuntimeError "{message}"')
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def step_plan_generation_runtime_error(context, message):
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context.plan_generation_invoke_side_effect = RuntimeError(message)
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@given('I set the Ollama provider extra kwargs "{kwargs_string}"')
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def step_set_ollama_provider_kwargs(context, kwargs_string):
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context.ollama_provider_kwargs = _parse_kwargs_string(kwargs_string)
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@@ -147,9 +86,7 @@ def step_set_ollama_provider_kwargs(context, kwargs_string):
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@given(
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'I create an Ollama chat provider with model "{model}" and base_url "{base_url}"'
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)
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@when(
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'I create an Ollama chat provider with model "{model}" and base_url "{base_url}"'
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)
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@when('I create an Ollama chat provider with model "{model}" and base_url "{base_url}"')
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def step_create_ollama_provider(context, model, base_url):
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patcher = patch("cleveragents.providers.llm.ollama_provider.ChatOllama")
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context.chat_ollama_patcher = patcher
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@@ -2,8 +2,8 @@ from __future__ import annotations
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from typing import Any, cast
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from langchain_core.language_models import BaseLanguageModel
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from langchain_community.chat_models import ChatOllama
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from langchain_core.language_models import BaseLanguageModel
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from cleveragents.providers.llm.langchain_chat_provider import LangChainChatProvider
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