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DocsGPT/tests/llm/test_llama_cpp.py
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Alex 574f96341e refactor: rename the application package to docsgpt
The backend import package is now docsgpt, the name it will carry on PyPI;
application was far too generic to install into anyone's site-packages.
git mv plus a mechanical rewrite of every import, dotted string and path
reference: 734 Python files, the compose files, Dockerfile, workflows, docs,
setup scripts, devcontainer, k8s manifests, vscode config, pytest and coverage
config, .gitignore. Behaviour is unchanged.

Kept for one release:
- A top-level application package whose meta-path finder resolves
  application.x.y to the already-imported docsgpt.x.y object, so old imports
  and entry points (celery -A application.app.celery,
  uvicorn application.asgi:asgi_app) keep working with a FutureWarning.
- Celery registers every application.* task name as an alias of its
  docsgpt.* task on start-up, so messages queued by the previous release still
  run. The redbeat key prefix moves to redbeat:docsgpt:v2: so schedule entries
  the previous release wrote are left unread instead of firing twice.

The backend image builds from the repository root (docker build -f
docsgpt/Dockerfile .) so it can ship the alias package; a root .dockerignore
allow-lists docsgpt/ and application/ and keeps caches, local data, .env
files, the sample index files and the Dockerfile out. Compose and the image
workflows point at the new context.
2026-09-07 10:20:43 +01:00

194 lines
6.0 KiB
Python

"""Unit tests for docsgpt/llm/llama_cpp.py — LlamaCpp and LlamaSingleton.
Covers:
- LlamaSingleton: get_instance, query_model (thread-safe)
- LlamaCpp constructor
- _raw_gen: prompt format and result extraction
- _raw_gen_stream: streaming iteration
"""
import sys
import types
import pytest
# ---------------------------------------------------------------------------
# Fake llama_cpp module
# ---------------------------------------------------------------------------
class FakeLlama:
def __init__(self, model_path=None, n_ctx=None):
self.model_path = model_path
self.n_ctx = n_ctx
self.last_call = None
def __call__(self, prompt, **kwargs):
self.last_call = {"prompt": prompt, **kwargs}
if kwargs.get("stream"):
return iter(
[
{"choices": [{"text": "chunk1"}]},
{"choices": [{"text": "chunk2"}]},
]
)
return {"choices": [{"text": "prefix ### Answer \nthe answer"}]}
@pytest.fixture(autouse=True)
def patch_llama_cpp(monkeypatch):
fake_mod = types.ModuleType("llama_cpp")
fake_mod.Llama = FakeLlama
sys.modules["llama_cpp"] = fake_mod
# Clear any cached instances
if "docsgpt.llm.llama_cpp" in sys.modules:
del sys.modules["docsgpt.llm.llama_cpp"]
yield
sys.modules.pop("llama_cpp", None)
if "docsgpt.llm.llama_cpp" in sys.modules:
del sys.modules["docsgpt.llm.llama_cpp"]
@pytest.fixture
def fresh_singleton():
from docsgpt.llm.llama_cpp import LlamaSingleton
LlamaSingleton._instances = {}
return LlamaSingleton
@pytest.fixture
def llm(fresh_singleton):
from docsgpt.llm.llama_cpp import LlamaCpp
instance = LlamaCpp(api_key="k", user_api_key=None, llm_name="/path/to/model")
return instance
# ---------------------------------------------------------------------------
# LlamaSingleton
# ---------------------------------------------------------------------------
@pytest.mark.unit
class TestLlamaSingleton:
def test_get_instance_creates_llama(self, fresh_singleton):
instance = fresh_singleton.get_instance("/model/path")
assert isinstance(instance, FakeLlama)
assert instance.model_path == "/model/path"
def test_get_instance_caches(self, fresh_singleton):
inst1 = fresh_singleton.get_instance("/model")
inst2 = fresh_singleton.get_instance("/model")
assert inst1 is inst2
def test_different_names_different_instances(self, fresh_singleton):
inst1 = fresh_singleton.get_instance("/model_a")
inst2 = fresh_singleton.get_instance("/model_b")
assert inst1 is not inst2
def test_query_model_thread_safe(self, fresh_singleton):
instance = fresh_singleton.get_instance("/model")
result = fresh_singleton.query_model(instance, "prompt", max_tokens=10)
assert "choices" in result
def test_import_error_raised(self, fresh_singleton, monkeypatch):
# Remove the fake module to simulate import failure
sys.modules.pop("llama_cpp", None)
fresh_singleton._instances = {}
with pytest.raises(ImportError, match="llama_cpp"):
fresh_singleton.get_instance("/new_model")
# ---------------------------------------------------------------------------
# LlamaCpp constructor
# ---------------------------------------------------------------------------
@pytest.mark.unit
class TestLlamaCppConstructor:
def test_sets_api_key(self, llm):
assert llm.api_key == "k"
def test_sets_user_api_key(self):
from docsgpt.llm.llama_cpp import LlamaCpp, LlamaSingleton
LlamaSingleton._instances = {}
instance = LlamaCpp(
api_key="k", user_api_key="uk", llm_name="/path/model"
)
assert instance.user_api_key == "uk"
def test_creates_llama_instance(self, llm):
assert isinstance(llm.llama, FakeLlama)
# ---------------------------------------------------------------------------
# _raw_gen
# ---------------------------------------------------------------------------
@pytest.mark.unit
class TestRawGen:
def test_returns_answer(self, llm):
msgs = [
{"content": "context text"},
{"content": "user question"},
]
result = llm._raw_gen(llm, model="local", messages=msgs)
assert result == "the answer"
def test_prompt_contains_instruction_and_context(self, llm):
msgs = [
{"content": "my context"},
{"content": "my question"},
]
llm._raw_gen(llm, model="local", messages=msgs)
prompt = llm.llama.last_call["prompt"]
assert "### Instruction" in prompt
assert "### Context" in prompt
assert "my question" in prompt
assert "my context" in prompt
def test_max_tokens_passed(self, llm):
msgs = [{"content": "c"}, {"content": "q"}]
llm._raw_gen(llm, model="local", messages=msgs)
assert llm.llama.last_call["max_tokens"] == 150
assert llm.llama.last_call["echo"] is False
# ---------------------------------------------------------------------------
# _raw_gen_stream
# ---------------------------------------------------------------------------
@pytest.mark.unit
class TestRawGenStream:
def test_yields_text_chunks(self, llm):
msgs = [{"content": "c"}, {"content": "q"}]
chunks = list(llm._raw_gen_stream(llm, model="local", messages=msgs))
assert chunks == ["chunk1", "chunk2"]
def test_prompt_format(self, llm):
msgs = [{"content": "ctx"}, {"content": "question"}]
list(llm._raw_gen_stream(llm, model="local", messages=msgs))
prompt = llm.llama.last_call["prompt"]
assert "### Instruction" in prompt
assert "### Answer" in prompt
def test_stream_flag_passed(self, llm):
msgs = [{"content": "c"}, {"content": "q"}]
list(
llm._raw_gen_stream(llm, model="local", messages=msgs, stream=True)
)
assert llm.llama.last_call["stream"] is True