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7ab7df1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 | """Tests for network and API failure scenarios."""
from unittest.mock import MagicMock, Mock, patch
import pytest
import requests
from models import create_llm, create_embeddings
from qa_chain import QAChainWrapper
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.documents import Document
@patch("models.ChatOpenAI")
def test_llm_server_unavailable(mock_chat_openai):
"""Test graceful handling when LLM server is unavailable."""
# Simulate connection error
mock_chat_openai.side_effect = requests.exceptions.ConnectionError("Connection refused")
with pytest.raises((requests.exceptions.ConnectionError, Exception)):
create_llm()
@patch("models.ChatOpenAI")
def test_llm_server_timeout(mock_chat_openai):
"""Test handling of LLM server timeout."""
mock_llm = MagicMock()
mock_llm.stream.side_effect = requests.exceptions.Timeout("Request timed out")
mock_chat_openai.return_value = mock_llm
llm = create_llm(streaming=True)
# Should raise timeout error when streaming
with pytest.raises(requests.exceptions.Timeout):
list(llm.stream("test"))
@patch("models.ChatOpenAI")
def test_llm_server_error_response(mock_chat_openai):
"""Test handling of LLM server error response."""
mock_llm = MagicMock()
mock_llm.invoke.side_effect = Exception("500 Internal Server Error")
mock_chat_openai.return_value = mock_llm
llm = create_llm(streaming=False)
with pytest.raises(Exception) as exc_info:
llm.invoke("test")
assert "500" in str(exc_info.value) or "Error" in str(exc_info.value)
@patch("models.HuggingFaceEmbeddings")
def test_embedding_model_download_failure(mock_embeddings_class):
"""Test handling of embedding model download failure."""
mock_embeddings_class.side_effect = Exception("Failed to download model")
with pytest.raises(Exception) as exc_info:
create_embeddings()
assert "Failed" in str(exc_info.value) or "download" in str(exc_info.value).lower()
@patch("qa_chain.create_llm")
@patch("qa_chain.format_chat_history")
def test_rag_query_with_llm_failure(mock_format_history, mock_create_llm, mock_vectorstore):
"""Test RAG query when LLM fails during streaming."""
from qa_chain import create_qa_chain
mock_format_history.return_value = ""
mock_llm = MagicMock()
mock_llm.stream.side_effect = requests.exceptions.ConnectionError("LLM server unavailable")
mock_create_llm.return_value = mock_llm
mock_retriever = MagicMock()
mock_doc = Document(page_content="Test", metadata={"source": "test.pdf", "page": 1})
mock_retriever.invoke.return_value = [mock_doc]
mock_vectorstore.as_retriever.return_value = mock_retriever
qa_chain = create_qa_chain(mock_vectorstore)
# Mock the chain operator
mock_chain = MagicMock()
mock_chain.stream.side_effect = requests.exceptions.ConnectionError("LLM server unavailable")
qa_chain._prompt.__or__ = MagicMock(return_value=mock_chain)
inputs = {
"question": "test question",
"chat_history": [],
}
# Should handle error gracefully
results = list(qa_chain.stream(inputs))
# Should return error message in chunk
assert len(results) > 0
chunk_text = str(results[0].get("chunk", ""))
# The error handling in qa_chain.py catches exceptions and yields error message
assert len(chunk_text) > 0 # Should have some content (either response or error)
@patch("qa_chain.create_llm")
@patch("qa_chain.format_chat_history")
def test_rag_query_with_retriever_failure(mock_format_history, mock_create_llm, mock_vectorstore):
"""Test RAG query when retriever fails."""
from qa_chain import QAChainWrapper
from langchain_core.prompts import ChatPromptTemplate
prompt = ChatPromptTemplate.from_template("Test: {question}")
qa_chain_wrapper = QAChainWrapper(mock_vectorstore, prompt)
mock_format_history.return_value = ""
mock_llm = MagicMock()
mock_chunk = MagicMock()
mock_chunk.content = "Response"
mock_llm.stream.return_value = [mock_chunk]
mock_create_llm.return_value = mock_llm
# Simulate retriever failure
mock_retriever = MagicMock()
mock_retriever.invoke.side_effect = Exception("ChromaDB connection failed")
qa_chain_wrapper._retriever = mock_retriever
mock_chain = MagicMock()
mock_chain.stream.return_value = [mock_chunk]
qa_chain_wrapper._prompt.__or__ = MagicMock(return_value=mock_chain)
inputs = {
"question": "test question",
"chat_history": [],
}
# Should handle retriever error - may raise or handle gracefully
try:
results = list(qa_chain_wrapper.stream(inputs))
# If it doesn't raise, should have error in response or empty results
if results:
chunk = results[0].get("chunk", "")
assert "Error" in str(chunk).lower() or len(results) == 0 or len(chunk) > 0
except Exception:
# Exception is acceptable for retriever failure
pass
@patch("ui.handlers.create_llm")
def test_vanilla_llm_network_failure(mock_create_llm):
"""Test vanilla LLM mode with network failure."""
from ui.handlers import create_stream_chat_response
mock_llm = MagicMock()
mock_llm.stream.side_effect = requests.exceptions.ConnectionError("Server unavailable")
mock_create_llm.return_value = mock_llm
stream_fn = create_stream_chat_response(MagicMock())
results = list(
stream_fn(
"test question",
[],
"Vanilla LLM",
)
)
# Should return error message
assert len(results) > 0
assert "Error" in results[-1][0] or "unavailable" in results[-1][0].lower()
@patch("qa_chain.create_llm")
@patch("qa_chain.format_chat_history")
def test_stream_interruption_handling(mock_format_history, mock_create_llm, mock_vectorstore):
"""Test handling of stream interruption."""
from qa_chain import QAChainWrapper
from langchain_core.prompts import ChatPromptTemplate
prompt = ChatPromptTemplate.from_template("Test: {question}")
qa_chain_wrapper = QAChainWrapper(mock_vectorstore, prompt)
mock_format_history.return_value = ""
# Simulate stream that gets interrupted
mock_llm = MagicMock()
mock_chunk1 = MagicMock()
mock_chunk1.content = "Partial "
mock_chunk2 = MagicMock()
mock_chunk2.content = "response"
def interrupted_stream(*args, **kwargs):
yield mock_chunk1
raise KeyboardInterrupt("Stream interrupted")
mock_llm.stream.side_effect = interrupted_stream
mock_create_llm.return_value = mock_llm
mock_retriever = MagicMock()
mock_retriever.invoke.return_value = [Document(page_content="Test", metadata={})]
qa_chain_wrapper._retriever = mock_retriever
mock_chain = MagicMock()
mock_chain.stream.side_effect = interrupted_stream
qa_chain_wrapper._prompt.__or__ = MagicMock(return_value=mock_chain)
inputs = {
"question": "test question",
"chat_history": [],
}
# Should handle interruption gracefully
try:
results = list(qa_chain_wrapper.stream(inputs))
# If it completes, should have partial response
if results:
assert len(results) > 0
except KeyboardInterrupt:
# Interruption is acceptable
pass
@patch("retrievers.BM25Okapi")
def test_hybrid_search_with_semantic_failure(mock_bm25, mock_vectorstore):
"""Test hybrid search when semantic search fails."""
from retrievers import HybridRetriever
from langchain_core.documents import Document
mock_vectorstore.get.return_value = {
"documents": ["Doc 1"],
"metadatas": [{"source": "test.pdf", "page": 1}],
}
# Simulate semantic search failure
mock_vectorstore.similarity_search_with_score.side_effect = Exception("Vector search failed")
mock_bm25_instance = MagicMock()
mock_bm25_instance.get_scores.return_value = [0.8]
mock_bm25.return_value = mock_bm25_instance
retriever = HybridRetriever(mock_vectorstore)
# Should handle semantic failure gracefully
results = retriever.hybrid_search("test query", k=1)
# Should still return results based on BM25 only
assert isinstance(results, list)
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