98a8aba3be
The PR rewrote collect_all() to call the new create_extractor_from_env_or_config() helper, but the existing tests still mocked the old direct create_extractor() symbol and the old silent-skip-on-missing-config behavior. Five tests in TestCorporateMemoryCollector and one in TestCollectorExtractorIntegration were red on the PR branch. Changes: - Tests now mock connectors.llm.create_extractor_from_env_or_config (the symbol the collector imports lazily). - Renamed test_collect_all_no_ai_config_skips -> test_collect_all_no_ai_config_or_env_raises and test_collector_handles_invalid_config -> test_collector_raises_on_invalid_config. Both assert pytest.raises(ValueError) — the explicit fail-fast semantics defect 5 of #176 was supposed to enforce. - collect_all() no longer swallows the factory's ValueError into stats["errors"]; it propagates so the scheduler / admin endpoint surface the actionable misconfiguration message instead of pretending the run was a no-op. - /api/admin/run-corporate-memory translates the propagated ValueError into a 500 with the factory's message, matching /api/admin/run-verification-detector.
1348 lines
51 KiB
Python
1348 lines
51 KiB
Python
"""
|
|
Tests for LLM connector module and Corporate Memory collector integration.
|
|
|
|
Covers:
|
|
- Factory (create_extractor) with various configs
|
|
- AnthropicExtractor (mock anthropic SDK)
|
|
- OpenAICompatExtractor (mock openai SDK) with fallback strategies
|
|
- Security (no secrets in logs)
|
|
- Corporate Memory collector using the LLM connector
|
|
"""
|
|
|
|
import json
|
|
import logging
|
|
from pathlib import Path
|
|
from unittest.mock import MagicMock, patch
|
|
|
|
import anthropic
|
|
import openai
|
|
import pytest
|
|
|
|
from connectors.llm.anthropic_provider import AnthropicExtractor
|
|
from connectors.llm.exceptions import (
|
|
LLMAuthError,
|
|
LLMFormatError,
|
|
LLMRateLimitError,
|
|
LLMRefusalError,
|
|
LLMTimeoutError,
|
|
LLMUnsupportedError,
|
|
)
|
|
from connectors.llm.factory import DEFAULT_MODEL, create_extractor
|
|
from connectors.llm.openai_compat import (
|
|
OpenAICompatExtractor,
|
|
_extract_json_from_text,
|
|
_sanitize_url,
|
|
)
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Helpers: mock response builders
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
def _anthropic_response(text: str, stop_reason: str = "end_turn"):
|
|
"""Build a mock Anthropic API response."""
|
|
block = MagicMock()
|
|
block.text = text
|
|
response = MagicMock()
|
|
response.content = [block]
|
|
response.stop_reason = stop_reason
|
|
return response
|
|
|
|
|
|
def _openai_response(content: str | None, finish_reason: str = "stop"):
|
|
"""Build a mock OpenAI chat completion response."""
|
|
message = MagicMock()
|
|
message.content = content
|
|
choice = MagicMock()
|
|
choice.message = message
|
|
choice.finish_reason = finish_reason
|
|
response = MagicMock()
|
|
response.choices = [choice]
|
|
return response
|
|
|
|
|
|
# ===================================================================
|
|
# Factory tests
|
|
# ===================================================================
|
|
|
|
|
|
class TestCreateExtractor:
|
|
"""Tests for connectors.llm.factory.create_extractor."""
|
|
|
|
@patch("connectors.llm.anthropic_provider.anthropic.Anthropic")
|
|
def test_anthropic_config(self, mock_client_cls):
|
|
"""Anthropic provider config returns AnthropicExtractor."""
|
|
config = {"provider": "anthropic", "api_key": "sk-ant-test123"}
|
|
ext = create_extractor(config)
|
|
assert isinstance(ext, AnthropicExtractor)
|
|
|
|
@patch("connectors.llm.openai_compat.openai.OpenAI")
|
|
def test_openai_compat_config(self, mock_client_cls):
|
|
"""openai_compat provider config returns OpenAICompatExtractor."""
|
|
config = {
|
|
"provider": "openai_compat",
|
|
"api_key": "sk-test",
|
|
"base_url": "https://api.example.com/v1",
|
|
}
|
|
ext = create_extractor(config)
|
|
assert isinstance(ext, OpenAICompatExtractor)
|
|
|
|
@patch("connectors.llm.anthropic_provider.anthropic.Anthropic")
|
|
def test_legacy_config(self, mock_client_cls):
|
|
"""Legacy config with anthropic_api_key returns AnthropicExtractor."""
|
|
config = {"anthropic_api_key": "sk-ant-legacy"}
|
|
ext = create_extractor(config)
|
|
assert isinstance(ext, AnthropicExtractor)
|
|
|
|
def test_empty_config_raises(self):
|
|
"""Empty config dict raises ValueError."""
|
|
with pytest.raises(ValueError, match="non-empty dict"):
|
|
create_extractor({})
|
|
|
|
def test_none_config_raises(self):
|
|
"""None config raises ValueError."""
|
|
with pytest.raises(ValueError, match="non-empty dict"):
|
|
create_extractor(None) # type: ignore[arg-type]
|
|
|
|
def test_missing_api_key_raises(self):
|
|
"""Config with provider but empty api_key raises ValueError."""
|
|
with pytest.raises(ValueError, match="must not be empty"):
|
|
create_extractor({"provider": "anthropic", "api_key": ""})
|
|
|
|
def test_missing_api_key_whitespace_raises(self):
|
|
"""Config with whitespace-only api_key raises ValueError."""
|
|
with pytest.raises(ValueError, match="must not be empty"):
|
|
create_extractor({"provider": "anthropic", "api_key": " "})
|
|
|
|
def test_openai_compat_missing_base_url_raises(self):
|
|
"""openai_compat without base_url raises ValueError."""
|
|
with pytest.raises(ValueError, match="base_url is required"):
|
|
create_extractor({
|
|
"provider": "openai_compat",
|
|
"api_key": "sk-test",
|
|
})
|
|
|
|
def test_unknown_provider_raises(self):
|
|
"""Unknown provider string raises ValueError."""
|
|
with pytest.raises(ValueError, match="Unknown ai.provider"):
|
|
create_extractor({
|
|
"provider": "gemini",
|
|
"api_key": "sk-test",
|
|
})
|
|
|
|
@patch("connectors.llm.anthropic_provider.anthropic.Anthropic")
|
|
def test_default_model(self, mock_client_cls):
|
|
"""Default model is claude-haiku-4-5-20251001."""
|
|
config = {"provider": "anthropic", "api_key": "sk-ant-test"}
|
|
ext = create_extractor(config)
|
|
assert ext._model == DEFAULT_MODEL
|
|
assert ext._model == "claude-haiku-4-5-20251001"
|
|
|
|
@patch("connectors.llm.anthropic_provider.anthropic.Anthropic")
|
|
def test_custom_model(self, mock_client_cls):
|
|
"""Custom model from config is used."""
|
|
config = {
|
|
"provider": "anthropic",
|
|
"api_key": "sk-ant-test",
|
|
"model": "claude-sonnet-4-20250514",
|
|
}
|
|
ext = create_extractor(config)
|
|
assert ext._model == "claude-sonnet-4-20250514"
|
|
|
|
def test_invalid_structured_output_raises(self):
|
|
"""Invalid structured_output value raises ValueError."""
|
|
with pytest.raises(ValueError, match="strict.*json.*auto"):
|
|
create_extractor({
|
|
"provider": "openai_compat",
|
|
"api_key": "sk-test",
|
|
"base_url": "https://api.example.com/v1",
|
|
"structured_output": "whatever",
|
|
})
|
|
|
|
|
|
# ===================================================================
|
|
# AnthropicExtractor tests
|
|
# ===================================================================
|
|
|
|
|
|
class TestAnthropicExtractor:
|
|
"""Tests for connectors.llm.anthropic_provider.AnthropicExtractor."""
|
|
|
|
SCHEMA = {"type": "object", "properties": {"items": {"type": "array"}}}
|
|
|
|
@patch("connectors.llm.anthropic_provider.anthropic.Anthropic")
|
|
def test_extract_json_success(self, mock_client_cls):
|
|
"""Successful extraction returns parsed dict."""
|
|
mock_client = MagicMock()
|
|
mock_client_cls.return_value = mock_client
|
|
|
|
payload = {"items": [{"name": "test"}]}
|
|
mock_client.messages.create.return_value = _anthropic_response(
|
|
json.dumps(payload)
|
|
)
|
|
|
|
ext = AnthropicExtractor(api_key="sk-ant-test", model="claude-haiku-4-5-20251001")
|
|
result = ext.extract_json(
|
|
prompt="Extract items",
|
|
max_tokens=1024,
|
|
json_schema=self.SCHEMA,
|
|
schema_name="test_schema",
|
|
)
|
|
|
|
assert result == payload
|
|
mock_client.messages.create.assert_called_once()
|
|
|
|
@patch("connectors.llm.anthropic_provider.anthropic.Anthropic")
|
|
def test_auth_error_raises_immediately(self, mock_client_cls):
|
|
"""AuthenticationError raises LLMAuthError without retries."""
|
|
mock_client = MagicMock()
|
|
mock_client_cls.return_value = mock_client
|
|
mock_client.messages.create.side_effect = anthropic.AuthenticationError(
|
|
message="bad key",
|
|
response=MagicMock(status_code=401),
|
|
body=None,
|
|
)
|
|
|
|
ext = AnthropicExtractor(api_key="sk-bad", model="claude-haiku-4-5-20251001")
|
|
with pytest.raises(LLMAuthError, match="authentication failed"):
|
|
ext.extract_json("test", 1024, self.SCHEMA, "test_schema")
|
|
|
|
# Only one call - no retries for auth errors
|
|
assert mock_client.messages.create.call_count == 1
|
|
|
|
@patch("connectors.llm.anthropic_provider.time.sleep")
|
|
@patch("connectors.llm.anthropic_provider.anthropic.Anthropic")
|
|
def test_rate_limit_retries_then_succeeds(self, mock_client_cls, mock_sleep):
|
|
"""RateLimitError retries and succeeds on second attempt."""
|
|
mock_client = MagicMock()
|
|
mock_client_cls.return_value = mock_client
|
|
|
|
payload = {"items": []}
|
|
mock_client.messages.create.side_effect = [
|
|
anthropic.RateLimitError(
|
|
message="rate limited",
|
|
response=MagicMock(status_code=429),
|
|
body=None,
|
|
),
|
|
_anthropic_response(json.dumps(payload)),
|
|
]
|
|
|
|
ext = AnthropicExtractor(api_key="sk-ant-test", model="claude-haiku-4-5-20251001")
|
|
result = ext.extract_json("test", 1024, self.SCHEMA, "test_schema")
|
|
|
|
assert result == payload
|
|
assert mock_client.messages.create.call_count == 2
|
|
mock_sleep.assert_called_once()
|
|
|
|
@patch("connectors.llm.anthropic_provider.time.sleep")
|
|
@patch("connectors.llm.anthropic_provider.anthropic.Anthropic")
|
|
def test_rate_limit_exhausts_retries(self, mock_client_cls, mock_sleep):
|
|
"""RateLimitError after max retries raises LLMRateLimitError."""
|
|
mock_client = MagicMock()
|
|
mock_client_cls.return_value = mock_client
|
|
mock_client.messages.create.side_effect = anthropic.RateLimitError(
|
|
message="rate limited",
|
|
response=MagicMock(status_code=429),
|
|
body=None,
|
|
)
|
|
|
|
ext = AnthropicExtractor(api_key="sk-ant-test", model="claude-haiku-4-5-20251001")
|
|
with pytest.raises(LLMRateLimitError):
|
|
ext.extract_json("test", 1024, self.SCHEMA, "test_schema")
|
|
|
|
assert mock_client.messages.create.call_count == 3 # MAX_RETRIES
|
|
|
|
@patch("connectors.llm.anthropic_provider.time.sleep")
|
|
@patch("connectors.llm.anthropic_provider.anthropic.Anthropic")
|
|
def test_timeout_retries(self, mock_client_cls, mock_sleep):
|
|
"""APITimeoutError retries with backoff then succeeds."""
|
|
mock_client = MagicMock()
|
|
mock_client_cls.return_value = mock_client
|
|
|
|
payload = {"items": []}
|
|
mock_client.messages.create.side_effect = [
|
|
anthropic.APITimeoutError(request=MagicMock()),
|
|
_anthropic_response(json.dumps(payload)),
|
|
]
|
|
|
|
ext = AnthropicExtractor(api_key="sk-ant-test", model="claude-haiku-4-5-20251001")
|
|
result = ext.extract_json("test", 1024, self.SCHEMA, "test_schema")
|
|
|
|
assert result == payload
|
|
mock_sleep.assert_called_once_with(2) # INITIAL_BACKOFF_SECONDS
|
|
|
|
@patch("connectors.llm.anthropic_provider.anthropic.Anthropic")
|
|
def test_truncation_raises_format_error(self, mock_client_cls):
|
|
"""stop_reason='max_tokens' raises LLMFormatError immediately (no recursion)."""
|
|
mock_client = MagicMock()
|
|
mock_client_cls.return_value = mock_client
|
|
|
|
truncated = _anthropic_response('{"items": [', stop_reason="max_tokens")
|
|
mock_client.messages.create.return_value = truncated
|
|
|
|
ext = AnthropicExtractor(api_key="sk-ant-test", model="claude-haiku-4-5-20251001")
|
|
with pytest.raises(LLMFormatError, match="truncated"):
|
|
ext.extract_json("test", 1024, self.SCHEMA, "test_schema")
|
|
|
|
@patch("connectors.llm.anthropic_provider.anthropic.Anthropic")
|
|
def test_invalid_json_raises_format_error(self, mock_client_cls):
|
|
"""Non-JSON response raises LLMFormatError."""
|
|
mock_client = MagicMock()
|
|
mock_client_cls.return_value = mock_client
|
|
mock_client.messages.create.return_value = _anthropic_response(
|
|
"This is not JSON at all"
|
|
)
|
|
|
|
ext = AnthropicExtractor(api_key="sk-ant-test", model="claude-haiku-4-5-20251001")
|
|
with pytest.raises(LLMFormatError, match="Failed to parse"):
|
|
ext.extract_json("test", 1024, self.SCHEMA, "test_schema")
|
|
|
|
@patch("connectors.llm.anthropic_provider.anthropic.Anthropic")
|
|
def test_empty_content_raises_refusal(self, mock_client_cls):
|
|
"""Empty content list with end_turn raises LLMRefusalError."""
|
|
mock_client = MagicMock()
|
|
mock_client_cls.return_value = mock_client
|
|
|
|
response = MagicMock()
|
|
response.content = []
|
|
response.stop_reason = "end_turn"
|
|
mock_client.messages.create.return_value = response
|
|
|
|
ext = AnthropicExtractor(api_key="sk-ant-test", model="claude-haiku-4-5-20251001")
|
|
with pytest.raises(LLMRefusalError, match="refused"):
|
|
ext.extract_json("test", 1024, self.SCHEMA, "test_schema")
|
|
|
|
@patch("connectors.llm.anthropic_provider.time.sleep")
|
|
@patch("connectors.llm.anthropic_provider.anthropic.Anthropic")
|
|
def test_backoff_multiplier(self, mock_client_cls, mock_sleep):
|
|
"""Exponential backoff doubles the delay on each retry."""
|
|
mock_client = MagicMock()
|
|
mock_client_cls.return_value = mock_client
|
|
mock_client.messages.create.side_effect = anthropic.RateLimitError(
|
|
message="limited",
|
|
response=MagicMock(status_code=429),
|
|
body=None,
|
|
)
|
|
|
|
ext = AnthropicExtractor(api_key="sk-ant-test", model="claude-haiku-4-5-20251001")
|
|
with pytest.raises(LLMRateLimitError):
|
|
ext.extract_json("test", 1024, self.SCHEMA, "test_schema")
|
|
|
|
# 3 attempts, 2 sleeps: delay(1)=2, delay(2)=4
|
|
assert mock_sleep.call_count == 2
|
|
mock_sleep.assert_any_call(2)
|
|
mock_sleep.assert_any_call(4)
|
|
|
|
|
|
# ===================================================================
|
|
# OpenAICompatExtractor tests
|
|
# ===================================================================
|
|
|
|
|
|
class TestOpenAICompatExtractor:
|
|
"""Tests for connectors.llm.openai_compat.OpenAICompatExtractor."""
|
|
|
|
SCHEMA = {"type": "object", "properties": {"items": {"type": "array"}}}
|
|
BASE_URL = "https://api.example.com/v1"
|
|
|
|
@patch("connectors.llm.openai_compat.openai.OpenAI")
|
|
def test_json_schema_success(self, mock_client_cls):
|
|
"""json_schema strategy returns parsed dict on success."""
|
|
mock_client = MagicMock()
|
|
mock_client_cls.return_value = mock_client
|
|
|
|
payload = {"items": [{"id": 1}]}
|
|
mock_client.chat.completions.create.return_value = _openai_response(
|
|
json.dumps(payload)
|
|
)
|
|
|
|
ext = OpenAICompatExtractor(
|
|
api_key="sk-test", base_url=self.BASE_URL,
|
|
model="gpt-4o", structured_output="auto",
|
|
)
|
|
result = ext.extract_json("Extract", 1024, self.SCHEMA, "test")
|
|
|
|
assert result == payload
|
|
call_kwargs = mock_client.chat.completions.create.call_args[1]
|
|
assert call_kwargs["response_format"]["type"] == "json_schema"
|
|
|
|
@patch("connectors.llm.openai_compat.openai.OpenAI")
|
|
def test_fallback_json_schema_to_json_object(self, mock_client_cls):
|
|
"""Auto mode falls back from json_schema to json_object."""
|
|
mock_client = MagicMock()
|
|
mock_client_cls.return_value = mock_client
|
|
|
|
payload = {"items": []}
|
|
# First call (json_schema) fails with BadRequestError, second (json_object) succeeds
|
|
mock_client.chat.completions.create.side_effect = [
|
|
openai.BadRequestError(
|
|
message="response_format json_schema not supported",
|
|
response=MagicMock(status_code=400),
|
|
body=None,
|
|
),
|
|
_openai_response(json.dumps(payload)),
|
|
]
|
|
|
|
ext = OpenAICompatExtractor(
|
|
api_key="sk-test", base_url=self.BASE_URL,
|
|
model="local-model", structured_output="auto",
|
|
)
|
|
result = ext.extract_json("Extract", 1024, self.SCHEMA, "test")
|
|
|
|
assert result == payload
|
|
assert mock_client.chat.completions.create.call_count == 2
|
|
|
|
# Second call should use json_object format
|
|
second_call_kwargs = mock_client.chat.completions.create.call_args_list[1][1]
|
|
assert second_call_kwargs["response_format"]["type"] == "json_object"
|
|
|
|
@patch("connectors.llm.openai_compat.openai.OpenAI")
|
|
def test_fallback_to_text_mode(self, mock_client_cls):
|
|
"""Auto mode falls back to text when both json_schema and json_object fail."""
|
|
mock_client = MagicMock()
|
|
mock_client_cls.return_value = mock_client
|
|
|
|
payload = {"items": [{"x": 1}]}
|
|
mock_client.chat.completions.create.side_effect = [
|
|
openai.BadRequestError(
|
|
message="response_format json_schema not supported",
|
|
response=MagicMock(status_code=400),
|
|
body=None,
|
|
),
|
|
openai.BadRequestError(
|
|
message="response_format json_object not supported",
|
|
response=MagicMock(status_code=400),
|
|
body=None,
|
|
),
|
|
_openai_response(json.dumps(payload)),
|
|
]
|
|
|
|
ext = OpenAICompatExtractor(
|
|
api_key="sk-test", base_url=self.BASE_URL,
|
|
model="local-model", structured_output="auto",
|
|
)
|
|
result = ext.extract_json("Extract", 1024, self.SCHEMA, "test")
|
|
|
|
assert result == payload
|
|
assert mock_client.chat.completions.create.call_count == 3
|
|
|
|
# Third call should NOT have response_format (text fallback)
|
|
third_call_kwargs = mock_client.chat.completions.create.call_args_list[2][1]
|
|
assert "response_format" not in third_call_kwargs
|
|
|
|
@patch("connectors.llm.openai_compat.openai.OpenAI")
|
|
def test_strict_mode_raises_unsupported(self, mock_client_cls):
|
|
"""strict mode raises LLMUnsupportedError when json_schema fails."""
|
|
mock_client = MagicMock()
|
|
mock_client_cls.return_value = mock_client
|
|
mock_client.chat.completions.create.side_effect = openai.BadRequestError(
|
|
message="response_format json_schema not supported",
|
|
response=MagicMock(status_code=400),
|
|
body=None,
|
|
)
|
|
|
|
ext = OpenAICompatExtractor(
|
|
api_key="sk-test", base_url=self.BASE_URL,
|
|
model="local-model", structured_output="strict",
|
|
)
|
|
with pytest.raises(LLMUnsupportedError, match="No supported structured output"):
|
|
ext.extract_json("Extract", 1024, self.SCHEMA, "test")
|
|
|
|
@patch("connectors.llm.openai_compat.openai.OpenAI")
|
|
def test_json_mode_no_text_fallback(self, mock_client_cls):
|
|
"""json mode tries json_schema + json_object but NOT text."""
|
|
mock_client = MagicMock()
|
|
mock_client_cls.return_value = mock_client
|
|
mock_client.chat.completions.create.side_effect = openai.BadRequestError(
|
|
message="response_format json_schema not supported",
|
|
response=MagicMock(status_code=400),
|
|
body=None,
|
|
)
|
|
|
|
ext = OpenAICompatExtractor(
|
|
api_key="sk-test", base_url=self.BASE_URL,
|
|
model="local-model", structured_output="json",
|
|
)
|
|
with pytest.raises(LLMUnsupportedError, match="No supported structured output"):
|
|
ext.extract_json("Extract", 1024, self.SCHEMA, "test")
|
|
|
|
# json mode: json_schema -> LLMUnsupportedError (skip), json_object -> same
|
|
assert mock_client.chat.completions.create.call_count == 2
|
|
|
|
@patch("connectors.llm.openai_compat.openai.OpenAI")
|
|
def test_text_fallback_strips_markdown_fences(self, mock_client_cls):
|
|
"""Text fallback strips markdown code fences from response."""
|
|
mock_client = MagicMock()
|
|
mock_client_cls.return_value = mock_client
|
|
|
|
payload = {"items": [{"v": 42}]}
|
|
fenced_json = f"```json\n{json.dumps(payload)}\n```"
|
|
|
|
mock_client.chat.completions.create.side_effect = [
|
|
openai.BadRequestError(
|
|
message="response_format json_schema not supported",
|
|
response=MagicMock(status_code=400),
|
|
body=None,
|
|
),
|
|
openai.BadRequestError(
|
|
message="response_format json_object not supported",
|
|
response=MagicMock(status_code=400),
|
|
body=None,
|
|
),
|
|
_openai_response(fenced_json),
|
|
]
|
|
|
|
ext = OpenAICompatExtractor(
|
|
api_key="sk-test", base_url=self.BASE_URL,
|
|
model="local-model", structured_output="auto",
|
|
)
|
|
result = ext.extract_json("Extract", 1024, self.SCHEMA, "test")
|
|
|
|
assert result == payload
|
|
|
|
@patch("connectors.llm.openai_compat.openai.OpenAI")
|
|
def test_auth_error(self, mock_client_cls):
|
|
"""AuthenticationError raises LLMAuthError without retries."""
|
|
mock_client = MagicMock()
|
|
mock_client_cls.return_value = mock_client
|
|
mock_client.chat.completions.create.side_effect = openai.AuthenticationError(
|
|
message="invalid key",
|
|
response=MagicMock(status_code=401),
|
|
body=None,
|
|
)
|
|
|
|
ext = OpenAICompatExtractor(
|
|
api_key="sk-bad", base_url=self.BASE_URL,
|
|
model="gpt-4o", structured_output="auto",
|
|
)
|
|
with pytest.raises(LLMAuthError, match="authentication failed"):
|
|
ext.extract_json("Extract", 1024, self.SCHEMA, "test")
|
|
|
|
assert mock_client.chat.completions.create.call_count == 1
|
|
|
|
@patch("connectors.llm.openai_compat.openai.OpenAI")
|
|
def test_truncation_raises_format_error(self, mock_client_cls):
|
|
"""finish_reason='length' raises LLMFormatError immediately (no recursion)."""
|
|
mock_client = MagicMock()
|
|
mock_client_cls.return_value = mock_client
|
|
|
|
mock_client.chat.completions.create.return_value = _openai_response(
|
|
'{"items": [', finish_reason="length",
|
|
)
|
|
|
|
ext = OpenAICompatExtractor(
|
|
api_key="sk-test", base_url=self.BASE_URL,
|
|
model="gpt-4o", structured_output="auto",
|
|
)
|
|
with pytest.raises(LLMFormatError, match="truncated"):
|
|
ext.extract_json("Extract", 1024, self.SCHEMA, "test")
|
|
|
|
@patch("connectors.llm.openai_compat.openai.OpenAI")
|
|
def test_empty_content_raises_refusal(self, mock_client_cls):
|
|
"""Empty content raises LLMRefusalError."""
|
|
mock_client = MagicMock()
|
|
mock_client_cls.return_value = mock_client
|
|
|
|
mock_client.chat.completions.create.return_value = _openai_response(
|
|
None, finish_reason="stop"
|
|
)
|
|
|
|
ext = OpenAICompatExtractor(
|
|
api_key="sk-test", base_url=self.BASE_URL,
|
|
model="gpt-4o", structured_output="auto",
|
|
)
|
|
with pytest.raises(LLMRefusalError, match="refused"):
|
|
ext.extract_json("Extract", 1024, self.SCHEMA, "test")
|
|
|
|
@patch("connectors.llm.openai_compat.time.sleep")
|
|
@patch("connectors.llm.openai_compat.openai.OpenAI")
|
|
def test_rate_limit_retries(self, mock_client_cls, mock_sleep):
|
|
"""RateLimitError retries with backoff then succeeds."""
|
|
mock_client = MagicMock()
|
|
mock_client_cls.return_value = mock_client
|
|
|
|
payload = {"items": []}
|
|
mock_client.chat.completions.create.side_effect = [
|
|
openai.RateLimitError(
|
|
message="too many requests",
|
|
response=MagicMock(status_code=429),
|
|
body=None,
|
|
),
|
|
_openai_response(json.dumps(payload)),
|
|
]
|
|
|
|
ext = OpenAICompatExtractor(
|
|
api_key="sk-test", base_url=self.BASE_URL,
|
|
model="gpt-4o", structured_output="auto",
|
|
)
|
|
result = ext.extract_json("Extract", 1024, self.SCHEMA, "test")
|
|
|
|
assert result == payload
|
|
mock_sleep.assert_called_once_with(2)
|
|
|
|
@patch("connectors.llm.openai_compat.openai.OpenAI")
|
|
def test_bad_request_non_format_raises_format_error(self, mock_client_cls):
|
|
"""BadRequestError not about response_format raises LLMFormatError."""
|
|
mock_client = MagicMock()
|
|
mock_client_cls.return_value = mock_client
|
|
mock_client.chat.completions.create.side_effect = openai.BadRequestError(
|
|
message="invalid model parameter",
|
|
response=MagicMock(status_code=400),
|
|
body=None,
|
|
)
|
|
|
|
ext = OpenAICompatExtractor(
|
|
api_key="sk-test", base_url=self.BASE_URL,
|
|
model="gpt-4o", structured_output="auto",
|
|
)
|
|
with pytest.raises(LLMFormatError, match="Bad request"):
|
|
ext.extract_json("Extract", 1024, self.SCHEMA, "test")
|
|
|
|
|
|
# ===================================================================
|
|
# URL sanitization tests
|
|
# ===================================================================
|
|
|
|
|
|
class TestURLSanitization:
|
|
"""Tests for URL sanitization in logging."""
|
|
|
|
def test_sanitize_url_removes_path(self):
|
|
"""Sanitized URL has no path component."""
|
|
result = _sanitize_url("https://api.example.com/v1/chat/completions")
|
|
assert result == "https://api.example.com"
|
|
|
|
def test_sanitize_url_removes_query(self):
|
|
"""Sanitized URL has no query params."""
|
|
result = _sanitize_url("https://api.example.com/v1?token=secret123")
|
|
assert result == "https://api.example.com"
|
|
|
|
def test_sanitize_url_preserves_port(self):
|
|
"""Sanitized URL preserves port number."""
|
|
result = _sanitize_url("http://localhost:8080/v1")
|
|
assert result == "http://localhost:8080"
|
|
|
|
|
|
# ===================================================================
|
|
# _extract_json_from_text tests
|
|
# ===================================================================
|
|
|
|
|
|
class TestExtractJsonFromText:
|
|
"""Tests for the text-based JSON extraction helper."""
|
|
|
|
def test_direct_json(self):
|
|
"""Plain JSON parses directly."""
|
|
result = _extract_json_from_text('{"key": "value"}')
|
|
assert result == {"key": "value"}
|
|
|
|
def test_markdown_fence_json(self):
|
|
"""JSON wrapped in ```json fences is extracted."""
|
|
text = '```json\n{"key": "value"}\n```'
|
|
result = _extract_json_from_text(text)
|
|
assert result == {"key": "value"}
|
|
|
|
def test_markdown_fence_no_lang(self):
|
|
"""JSON wrapped in ``` fences (no language) is extracted."""
|
|
text = '```\n{"key": "value"}\n```'
|
|
result = _extract_json_from_text(text)
|
|
assert result == {"key": "value"}
|
|
|
|
def test_brace_extraction_fallback(self):
|
|
"""Fallback: extract JSON between first { and last }."""
|
|
text = 'Here is the result: {"key": "value"} -- done'
|
|
result = _extract_json_from_text(text)
|
|
assert result == {"key": "value"}
|
|
|
|
def test_no_json_raises_format_error(self):
|
|
"""No valid JSON raises LLMFormatError."""
|
|
with pytest.raises(LLMFormatError, match="Could not extract valid JSON"):
|
|
_extract_json_from_text("This is just plain text without braces")
|
|
|
|
def test_invalid_json_in_braces_raises(self):
|
|
"""Malformed JSON in braces raises LLMFormatError."""
|
|
with pytest.raises(LLMFormatError):
|
|
_extract_json_from_text("{not: valid json}")
|
|
|
|
|
|
# ===================================================================
|
|
# Security tests (no secrets in logs)
|
|
# ===================================================================
|
|
|
|
|
|
class TestSecurity:
|
|
"""Verify that API keys, prompts, and responses never appear in log output."""
|
|
|
|
SECRET_KEY = "sk-ant-SUPER-SECRET-KEY-12345"
|
|
PROMPT_TEXT = "Extract the following secret data from documents"
|
|
RESPONSE_TEXT = '{"items": [{"classified": "top-secret-info"}]}'
|
|
|
|
@patch("connectors.llm.anthropic_provider.anthropic.Anthropic")
|
|
def test_api_key_not_in_logs(self, mock_client_cls, caplog):
|
|
"""API key must never appear in log messages."""
|
|
mock_client = MagicMock()
|
|
mock_client_cls.return_value = mock_client
|
|
mock_client.messages.create.return_value = _anthropic_response(self.RESPONSE_TEXT)
|
|
|
|
ext = AnthropicExtractor(api_key=self.SECRET_KEY, model="claude-haiku-4-5-20251001")
|
|
with caplog.at_level(logging.DEBUG, logger="connectors.llm"):
|
|
ext.extract_json(
|
|
self.PROMPT_TEXT, 1024,
|
|
{"type": "object"}, "test_schema",
|
|
)
|
|
|
|
full_log = caplog.text
|
|
assert self.SECRET_KEY not in full_log
|
|
|
|
@patch("connectors.llm.anthropic_provider.anthropic.Anthropic")
|
|
def test_prompt_not_in_logs(self, mock_client_cls, caplog):
|
|
"""Prompt content must never appear in log messages."""
|
|
mock_client = MagicMock()
|
|
mock_client_cls.return_value = mock_client
|
|
mock_client.messages.create.return_value = _anthropic_response(self.RESPONSE_TEXT)
|
|
|
|
ext = AnthropicExtractor(api_key=self.SECRET_KEY, model="claude-haiku-4-5-20251001")
|
|
with caplog.at_level(logging.DEBUG, logger="connectors.llm"):
|
|
ext.extract_json(
|
|
self.PROMPT_TEXT, 1024,
|
|
{"type": "object"}, "test_schema",
|
|
)
|
|
|
|
full_log = caplog.text
|
|
assert self.PROMPT_TEXT not in full_log
|
|
|
|
@patch("connectors.llm.anthropic_provider.anthropic.Anthropic")
|
|
def test_response_not_in_logs(self, mock_client_cls, caplog):
|
|
"""Response content must never appear in log messages."""
|
|
mock_client = MagicMock()
|
|
mock_client_cls.return_value = mock_client
|
|
mock_client.messages.create.return_value = _anthropic_response(self.RESPONSE_TEXT)
|
|
|
|
ext = AnthropicExtractor(api_key=self.SECRET_KEY, model="claude-haiku-4-5-20251001")
|
|
with caplog.at_level(logging.DEBUG, logger="connectors.llm"):
|
|
ext.extract_json(
|
|
self.PROMPT_TEXT, 1024,
|
|
{"type": "object"}, "test_schema",
|
|
)
|
|
|
|
full_log = caplog.text
|
|
assert "top-secret-info" not in full_log
|
|
assert self.RESPONSE_TEXT not in full_log
|
|
|
|
@patch("connectors.llm.openai_compat.openai.OpenAI")
|
|
def test_openai_api_key_not_in_logs(self, mock_client_cls, caplog):
|
|
"""OpenAI-compat API key must never appear in log messages."""
|
|
mock_client = MagicMock()
|
|
mock_client_cls.return_value = mock_client
|
|
mock_client.chat.completions.create.return_value = _openai_response(
|
|
self.RESPONSE_TEXT
|
|
)
|
|
|
|
ext = OpenAICompatExtractor(
|
|
api_key=self.SECRET_KEY,
|
|
base_url="https://api.example.com/v1",
|
|
model="gpt-4o",
|
|
structured_output="auto",
|
|
)
|
|
with caplog.at_level(logging.DEBUG, logger="connectors.llm"):
|
|
ext.extract_json(
|
|
self.PROMPT_TEXT, 1024,
|
|
{"type": "object"}, "test_schema",
|
|
)
|
|
|
|
full_log = caplog.text
|
|
assert self.SECRET_KEY not in full_log
|
|
|
|
@patch("connectors.llm.openai_compat.openai.OpenAI")
|
|
def test_openai_url_path_not_in_logs(self, mock_client_cls, caplog):
|
|
"""URL paths (may contain tokens) must not appear in logs."""
|
|
mock_client = MagicMock()
|
|
mock_client_cls.return_value = mock_client
|
|
mock_client.chat.completions.create.return_value = _openai_response(
|
|
'{"ok": true}'
|
|
)
|
|
|
|
sensitive_url = "https://api.example.com/v1/secret-path?token=abc123"
|
|
ext = OpenAICompatExtractor(
|
|
api_key="sk-test",
|
|
base_url=sensitive_url,
|
|
model="gpt-4o",
|
|
structured_output="auto",
|
|
)
|
|
with caplog.at_level(logging.DEBUG, logger="connectors.llm"):
|
|
ext.extract_json("test", 1024, {"type": "object"}, "test_schema")
|
|
|
|
full_log = caplog.text
|
|
assert "secret-path" not in full_log
|
|
assert "token=abc123" not in full_log
|
|
# But the host SHOULD be present (safe to log)
|
|
assert "api.example.com" in full_log
|
|
|
|
|
|
# ===================================================================
|
|
# Corporate Memory collector tests
|
|
# ===================================================================
|
|
|
|
|
|
class TestCorporateMemoryCollector:
|
|
"""Tests for services.corporate_memory.collector integration with LLM connector."""
|
|
|
|
def test_collect_all_no_files_skips(self, tmp_path):
|
|
"""collect_all skips when no CLAUDE.local.md files found."""
|
|
from services.corporate_memory.collector import collect_all
|
|
|
|
# Use an empty directory as HOME_BASE
|
|
empty_home = tmp_path / "empty_home"
|
|
empty_home.mkdir()
|
|
|
|
with patch("services.corporate_memory.collector.HOME_BASE", empty_home):
|
|
stats = collect_all(dry_run=True)
|
|
|
|
assert stats["skipped"] is True
|
|
assert stats["files_found"] == 0
|
|
|
|
def test_collect_all_no_changes_skips(self, tmp_path):
|
|
"""collect_all skips when hashes match (no changes)."""
|
|
import hashlib as hl
|
|
|
|
from services.corporate_memory.collector import collect_all
|
|
|
|
# Set up a user directory with CLAUDE.local.md
|
|
home = tmp_path / "home"
|
|
home.mkdir()
|
|
user_dir = home / "alice"
|
|
user_dir.mkdir()
|
|
claude_file = user_dir / "CLAUDE.local.md"
|
|
claude_file.write_text("Some knowledge content")
|
|
|
|
content_hash = hl.md5("Some knowledge content".encode()).hexdigest()
|
|
|
|
# Stored hashes match current hashes -> no changes
|
|
with (
|
|
patch("services.corporate_memory.collector.HOME_BASE", home),
|
|
patch(
|
|
"services.corporate_memory.collector._read_json",
|
|
return_value={"hashes": {"alice": content_hash}},
|
|
),
|
|
):
|
|
stats = collect_all(dry_run=True)
|
|
|
|
assert stats["skipped"] is True
|
|
assert stats["files_found"] == 1
|
|
|
|
def test_collect_all_with_changes(self, tmp_path):
|
|
"""collect_all processes files when changes detected."""
|
|
from services.corporate_memory.collector import collect_all
|
|
|
|
home = tmp_path / "home"
|
|
home.mkdir()
|
|
user_dir = home / "bob"
|
|
user_dir.mkdir()
|
|
claude_file = user_dir / "CLAUDE.local.md"
|
|
claude_file.write_text("## Useful DuckDB trick\nUse QUALIFY for window filters")
|
|
|
|
# Mock extractor
|
|
mock_extractor = MagicMock()
|
|
mock_extractor.extract_json.side_effect = [
|
|
# First call: catalog refresh
|
|
{
|
|
"items": [
|
|
{
|
|
"existing_id": None,
|
|
"title": "DuckDB QUALIFY clause",
|
|
"content": "Use QUALIFY for window function filtering",
|
|
"category": "data_analysis",
|
|
"tags": ["duckdb", "sql"],
|
|
"source_users": ["bob"],
|
|
},
|
|
],
|
|
},
|
|
# Second call: sensitivity check
|
|
{"safe": True},
|
|
]
|
|
|
|
with (
|
|
patch("services.corporate_memory.collector.HOME_BASE", home),
|
|
patch("services.corporate_memory.collector._read_json", return_value={}),
|
|
patch("services.corporate_memory.collector._write_json"),
|
|
patch(
|
|
"config.loader.load_instance_config",
|
|
return_value={"ai": {"provider": "anthropic", "api_key": "sk-test"}},
|
|
),
|
|
patch(
|
|
"connectors.llm.create_extractor_from_env_or_config",
|
|
return_value=mock_extractor,
|
|
),
|
|
):
|
|
stats = collect_all(dry_run=True)
|
|
|
|
assert stats["skipped"] is False
|
|
assert stats["items_extracted"] == 1
|
|
assert stats["items_new"] == 1
|
|
assert stats["items_filtered"] == 0
|
|
assert stats["errors"] == []
|
|
|
|
def test_collect_all_filters_sensitive_items(self, tmp_path):
|
|
"""collect_all filters items that fail sensitivity check."""
|
|
from services.corporate_memory.collector import collect_all
|
|
|
|
home = tmp_path / "home"
|
|
home.mkdir()
|
|
user_dir = home / "carol"
|
|
user_dir.mkdir()
|
|
(user_dir / "CLAUDE.local.md").write_text("API key: sk-secret-123")
|
|
|
|
mock_extractor = MagicMock()
|
|
mock_extractor.extract_json.side_effect = [
|
|
# Catalog refresh
|
|
{
|
|
"items": [
|
|
{
|
|
"existing_id": None,
|
|
"title": "API credentials",
|
|
"content": "Use sk-secret-123 for auth",
|
|
"category": "api_integration",
|
|
"tags": ["api", "auth"],
|
|
"source_users": ["carol"],
|
|
},
|
|
],
|
|
},
|
|
# Sensitivity check: NOT safe
|
|
{"safe": False, "reason": "Contains API key"},
|
|
]
|
|
|
|
with (
|
|
patch("services.corporate_memory.collector.HOME_BASE", home),
|
|
patch("services.corporate_memory.collector._read_json", return_value={}),
|
|
patch("services.corporate_memory.collector._write_json"),
|
|
patch(
|
|
"config.loader.load_instance_config",
|
|
return_value={"ai": {"provider": "anthropic", "api_key": "sk-test"}},
|
|
),
|
|
patch(
|
|
"connectors.llm.create_extractor_from_env_or_config",
|
|
return_value=mock_extractor,
|
|
),
|
|
):
|
|
stats = collect_all(dry_run=True)
|
|
|
|
assert stats["items_extracted"] == 1
|
|
assert stats["items_new"] == 0
|
|
assert stats["items_filtered"] == 1
|
|
|
|
def test_collect_all_preserves_existing_items(self, tmp_path):
|
|
"""Existing items (by ID) skip sensitivity check and are preserved."""
|
|
from services.corporate_memory.collector import collect_all
|
|
|
|
home = tmp_path / "home"
|
|
home.mkdir()
|
|
user_dir = home / "dave"
|
|
user_dir.mkdir()
|
|
(user_dir / "CLAUDE.local.md").write_text("Updated knowledge")
|
|
|
|
existing = {
|
|
"items": {
|
|
"km_abc123": {
|
|
"id": "km_abc123",
|
|
"title": "Existing item",
|
|
"content": "This is already validated",
|
|
"category": "workflow",
|
|
"tags": ["existing"],
|
|
"source_users": ["dave"],
|
|
"extracted_at": "2026-01-01T00:00:00+00:00",
|
|
},
|
|
},
|
|
"metadata": {},
|
|
}
|
|
|
|
def read_json_side_effect(path):
|
|
path_str = str(path)
|
|
if "user_hashes" in path_str:
|
|
return {} # No stored hashes -> force change detection
|
|
if "knowledge" in path_str:
|
|
return existing
|
|
return {}
|
|
|
|
mock_extractor = MagicMock()
|
|
# HAIKU returns the existing item (preserving ID)
|
|
mock_extractor.extract_json.return_value = {
|
|
"items": [
|
|
{
|
|
"existing_id": "km_abc123",
|
|
"title": "Existing item (updated)",
|
|
"content": "This is already validated with updates",
|
|
"category": "workflow",
|
|
"tags": ["existing"],
|
|
"source_users": ["dave"],
|
|
},
|
|
],
|
|
}
|
|
|
|
with (
|
|
patch("services.corporate_memory.collector.HOME_BASE", home),
|
|
patch(
|
|
"services.corporate_memory.collector._read_json",
|
|
side_effect=read_json_side_effect,
|
|
),
|
|
patch("services.corporate_memory.collector._write_json"),
|
|
patch(
|
|
"config.loader.load_instance_config",
|
|
return_value={"ai": {"provider": "anthropic", "api_key": "sk-test"}},
|
|
),
|
|
patch(
|
|
"connectors.llm.create_extractor_from_env_or_config",
|
|
return_value=mock_extractor,
|
|
),
|
|
):
|
|
stats = collect_all(dry_run=True)
|
|
|
|
assert stats["items_preserved"] == 1
|
|
assert stats["items_new"] == 0
|
|
# extract_json called ONCE (catalog refresh only, no sensitivity for existing)
|
|
assert mock_extractor.extract_json.call_count == 1
|
|
|
|
def test_collect_all_handles_llm_error(self, tmp_path):
|
|
"""collect_all captures LLMError and returns it in stats."""
|
|
from services.corporate_memory.collector import collect_all
|
|
|
|
home = tmp_path / "home"
|
|
home.mkdir()
|
|
user_dir = home / "eve"
|
|
user_dir.mkdir()
|
|
(user_dir / "CLAUDE.local.md").write_text("Some content")
|
|
|
|
mock_extractor = MagicMock()
|
|
mock_extractor.extract_json.side_effect = LLMRateLimitError("too many requests")
|
|
|
|
with (
|
|
patch("services.corporate_memory.collector.HOME_BASE", home),
|
|
patch("services.corporate_memory.collector._read_json", return_value={}),
|
|
patch(
|
|
"config.loader.load_instance_config",
|
|
return_value={"ai": {"provider": "anthropic", "api_key": "sk-test"}},
|
|
),
|
|
patch(
|
|
"connectors.llm.create_extractor_from_env_or_config",
|
|
return_value=mock_extractor,
|
|
),
|
|
):
|
|
stats = collect_all(dry_run=True)
|
|
|
|
assert len(stats["errors"]) == 1
|
|
assert "LLM error" in stats["errors"][0]
|
|
|
|
def test_collect_all_no_ai_config_or_env_raises(self, tmp_path, monkeypatch):
|
|
"""collect_all fails fast when neither ai: config nor LLM env keys exist (#176)."""
|
|
from services.corporate_memory.collector import collect_all
|
|
|
|
home = tmp_path / "home"
|
|
home.mkdir()
|
|
user_dir = home / "frank"
|
|
user_dir.mkdir()
|
|
(user_dir / "CLAUDE.local.md").write_text("Some content")
|
|
|
|
# Make sure no env-var fallback is available so the factory raises.
|
|
monkeypatch.delenv("ANTHROPIC_API_KEY", raising=False)
|
|
monkeypatch.delenv("LLM_API_KEY", raising=False)
|
|
|
|
with (
|
|
patch("services.corporate_memory.collector.HOME_BASE", home),
|
|
patch("services.corporate_memory.collector._read_json", return_value={}),
|
|
patch(
|
|
"config.loader.load_instance_config",
|
|
return_value={"server": {"host": "example.com"}},
|
|
),
|
|
pytest.raises(ValueError, match="LLM not configured"),
|
|
):
|
|
collect_all(dry_run=True)
|
|
|
|
|
|
# ===================================================================
|
|
# Corporate Memory collector - helper function tests
|
|
# ===================================================================
|
|
|
|
|
|
class TestCollectorHelpers:
|
|
"""Tests for collector helper functions."""
|
|
|
|
def test_generate_id_deterministic(self):
|
|
"""_generate_id returns consistent IDs for same content."""
|
|
from services.corporate_memory.collector import _generate_id
|
|
|
|
id1 = _generate_id("test content")
|
|
id2 = _generate_id("test content")
|
|
assert id1 == id2
|
|
assert id1.startswith("km_")
|
|
assert len(id1) == 15 # "km_" + 12 hex chars
|
|
|
|
def test_generate_id_different_for_different_content(self):
|
|
"""_generate_id returns different IDs for different content."""
|
|
from services.corporate_memory.collector import _generate_id
|
|
|
|
id1 = _generate_id("content A")
|
|
id2 = _generate_id("content B")
|
|
assert id1 != id2
|
|
|
|
def test_format_existing_catalog_empty(self):
|
|
"""Empty catalog formats as fresh message."""
|
|
from services.corporate_memory.collector import _format_existing_catalog
|
|
|
|
result = _format_existing_catalog({})
|
|
assert "fresh catalog" in result.lower() or "No existing items" in result
|
|
|
|
def test_format_existing_catalog_with_items(self):
|
|
"""Catalog with items formats each item."""
|
|
from services.corporate_memory.collector import _format_existing_catalog
|
|
|
|
existing = {
|
|
"items": {
|
|
"km_abc": {
|
|
"title": "Test Item",
|
|
"content": "Test content",
|
|
"category": "workflow",
|
|
"tags": ["test"],
|
|
"source_users": ["alice"],
|
|
},
|
|
},
|
|
}
|
|
|
|
result = _format_existing_catalog(existing)
|
|
assert "km_abc" in result
|
|
assert "Test Item" in result
|
|
assert "workflow" in result
|
|
assert "alice" in result
|
|
|
|
def test_format_user_files(self):
|
|
"""User files are formatted with username headers."""
|
|
from services.corporate_memory.collector import _format_user_files
|
|
|
|
user_files = {
|
|
"alice": ("Knowledge from alice", "hash_a"),
|
|
"bob": ("Knowledge from bob", "hash_b"),
|
|
}
|
|
|
|
result = _format_user_files(user_files)
|
|
assert "### User: alice" in result
|
|
assert "### User: bob" in result
|
|
assert "Knowledge from alice" in result
|
|
assert "Knowledge from bob" in result
|
|
|
|
def test_process_catalog_response_new_items(self):
|
|
"""New items get generated IDs."""
|
|
from services.corporate_memory.collector import _process_catalog_response
|
|
|
|
items = [
|
|
{
|
|
"existing_id": None,
|
|
"title": "New Knowledge",
|
|
"content": "Fresh insight",
|
|
"category": "data_analysis",
|
|
"tags": ["new"],
|
|
"source_users": ["alice"],
|
|
},
|
|
]
|
|
|
|
result = _process_catalog_response(items, {"items": {}})
|
|
|
|
assert len(result) == 1
|
|
item_id = list(result.keys())[0]
|
|
assert item_id.startswith("km_")
|
|
item = result[item_id]
|
|
assert item["title"] == "New Knowledge"
|
|
assert item["content"] == "Fresh insight"
|
|
assert "extracted_at" in item
|
|
assert "updated_at" in item
|
|
|
|
def test_process_catalog_response_preserves_existing(self):
|
|
"""Existing items keep their original ID and extracted_at."""
|
|
from services.corporate_memory.collector import _process_catalog_response
|
|
|
|
existing = {
|
|
"items": {
|
|
"km_existing": {
|
|
"title": "Old Title",
|
|
"content": "Old content",
|
|
"extracted_at": "2026-01-01T00:00:00+00:00",
|
|
},
|
|
},
|
|
}
|
|
|
|
items = [
|
|
{
|
|
"existing_id": "km_existing",
|
|
"title": "Updated Title",
|
|
"content": "Updated content",
|
|
"category": "workflow",
|
|
"tags": ["updated"],
|
|
"source_users": ["alice"],
|
|
},
|
|
]
|
|
|
|
result = _process_catalog_response(items, existing)
|
|
|
|
assert "km_existing" in result
|
|
assert result["km_existing"]["title"] == "Updated Title"
|
|
assert result["km_existing"]["extracted_at"] == "2026-01-01T00:00:00+00:00"
|
|
|
|
def test_process_catalog_response_handles_collision(self):
|
|
"""ID collision for new items is resolved."""
|
|
from services.corporate_memory.collector import _process_catalog_response
|
|
|
|
# Two items with identical title+content will produce same hash
|
|
items = [
|
|
{
|
|
"existing_id": None,
|
|
"title": "Same",
|
|
"content": "Same",
|
|
"category": "workflow",
|
|
"tags": [],
|
|
"source_users": ["a"],
|
|
},
|
|
{
|
|
"existing_id": None,
|
|
"title": "Same",
|
|
"content": "Same",
|
|
"category": "workflow",
|
|
"tags": [],
|
|
"source_users": ["b"],
|
|
},
|
|
]
|
|
|
|
result = _process_catalog_response(items, {"items": {}})
|
|
|
|
# Both items should be present (collision resolved)
|
|
assert len(result) == 2
|
|
|
|
|
|
# ===================================================================
|
|
# Corporate Memory - check_sensitivity tests
|
|
# ===================================================================
|
|
|
|
|
|
class TestCheckSensitivity:
|
|
"""Tests for the sensitivity check function."""
|
|
|
|
def test_safe_item_returns_true(self):
|
|
"""Safe items return True."""
|
|
from services.corporate_memory.collector import check_sensitivity
|
|
|
|
mock_extractor = MagicMock()
|
|
mock_extractor.extract_json.return_value = {"safe": True}
|
|
|
|
item = {
|
|
"title": "SQL Tip",
|
|
"content": "Use GROUP BY for aggregation",
|
|
"tags": ["sql"],
|
|
}
|
|
|
|
assert check_sensitivity(mock_extractor, item) is True
|
|
|
|
def test_unsafe_item_returns_false(self):
|
|
"""Unsafe items return False."""
|
|
from services.corporate_memory.collector import check_sensitivity
|
|
|
|
mock_extractor = MagicMock()
|
|
mock_extractor.extract_json.return_value = {
|
|
"safe": False,
|
|
"reason": "Contains API key",
|
|
}
|
|
|
|
item = {
|
|
"title": "Auth setup",
|
|
"content": "Use key sk-12345",
|
|
"tags": ["auth"],
|
|
}
|
|
|
|
assert check_sensitivity(mock_extractor, item) is False
|
|
|
|
def test_llm_error_assumes_unsafe(self):
|
|
"""LLMError during sensitivity check assumes item is unsafe."""
|
|
from services.corporate_memory.collector import check_sensitivity
|
|
|
|
mock_extractor = MagicMock()
|
|
mock_extractor.extract_json.side_effect = LLMRateLimitError("rate limited")
|
|
|
|
item = {"title": "Test", "content": "Content", "tags": []}
|
|
|
|
assert check_sensitivity(mock_extractor, item) is False
|
|
|
|
def test_llm_format_error_assumes_unsafe(self):
|
|
"""LLMFormatError during sensitivity check assumes item is unsafe."""
|
|
from services.corporate_memory.collector import check_sensitivity
|
|
|
|
mock_extractor = MagicMock()
|
|
mock_extractor.extract_json.side_effect = LLMFormatError("bad json")
|
|
|
|
item = {"title": "Test", "content": "Content", "tags": []}
|
|
|
|
assert check_sensitivity(mock_extractor, item) is False
|
|
|
|
|
|
# ===================================================================
|
|
# Integration: collector uses create_extractor
|
|
# ===================================================================
|
|
|
|
|
|
class TestCollectorExtractorIntegration:
|
|
"""Verify collector properly initializes the LLM extractor."""
|
|
|
|
@patch("connectors.llm.anthropic_provider.anthropic.Anthropic")
|
|
def test_collector_creates_anthropic_extractor(self, mock_client_cls, tmp_path):
|
|
"""Collector creates AnthropicExtractor from instance.yaml config."""
|
|
from services.corporate_memory.collector import collect_all
|
|
|
|
home = tmp_path / "home"
|
|
home.mkdir()
|
|
user_dir = home / "alice"
|
|
user_dir.mkdir()
|
|
(user_dir / "CLAUDE.local.md").write_text("Some knowledge")
|
|
|
|
mock_client = MagicMock()
|
|
mock_client_cls.return_value = mock_client
|
|
|
|
catalog_response = _anthropic_response(json.dumps({"items": []}))
|
|
mock_client.messages.create.return_value = catalog_response
|
|
|
|
with (
|
|
patch("services.corporate_memory.collector.HOME_BASE", home),
|
|
patch("services.corporate_memory.collector._read_json", return_value={}),
|
|
patch("services.corporate_memory.collector._write_json"),
|
|
patch(
|
|
"config.loader.load_instance_config",
|
|
return_value={
|
|
"ai": {
|
|
"provider": "anthropic",
|
|
"api_key": "sk-ant-integration-test",
|
|
"model": "claude-haiku-4-5-20251001",
|
|
},
|
|
},
|
|
),
|
|
):
|
|
stats = collect_all(dry_run=True)
|
|
|
|
# Verify Anthropic client was initialized
|
|
mock_client_cls.assert_called_once_with(api_key="sk-ant-integration-test")
|
|
assert stats["items_extracted"] == 0
|
|
assert stats["errors"] == []
|
|
|
|
def test_collector_raises_on_invalid_config(self, tmp_path):
|
|
"""Collector fail-fasts (raises ValueError) when ai: config is invalid (#176)."""
|
|
from services.corporate_memory.collector import collect_all
|
|
|
|
home = tmp_path / "home"
|
|
home.mkdir()
|
|
user_dir = home / "alice"
|
|
user_dir.mkdir()
|
|
(user_dir / "CLAUDE.local.md").write_text("Some knowledge")
|
|
|
|
with (
|
|
patch("services.corporate_memory.collector.HOME_BASE", home),
|
|
patch("services.corporate_memory.collector._read_json", return_value={}),
|
|
patch(
|
|
"config.loader.load_instance_config",
|
|
return_value={"ai": {"provider": "anthropic", "api_key": ""}},
|
|
),
|
|
pytest.raises(ValueError, match="must not be empty"),
|
|
):
|
|
collect_all(dry_run=True)
|