ceos/unsloth
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

[pre-commit.ci] auto fixes from pre-commit.com hooks

Browse source 
for more information, see https://pre-commit.ci
This commit is contained in:
pre-commit-ci[bot] 2026-07-22 06:16:13 +00:00
commit 12907500d7
3 changed files with 90 additions and 32 deletions
Download patch file Download diff file Expand all files Collapse all files

2
studio/backend/core/research_runs.py
View file

@ -156,6 +156,7 @@ def _clean_scraped_text(text: str) -> str:
kept.append(line)
return _BLANK_RUN.sub("\n\n", "\n".join(kept)).strip()
_REPORT_SYSTEM_PROMPT = """You are writing a rigorous, self-contained research report.
Research standards:
@ -459,7 +460,6 @@ def _loaded_context_length() -> int | None:
# GGUF / llama.cpp keeps context on its own backend (checked first, like the API layer).
try:
from routes.inference import get_llama_cpp_backend
llama = get_llama_cpp_backend()
if getattr(llama, "is_loaded", False):
ctx = _positive_int_or_none(getattr(llama, "context_length", None))

96
studio/backend/tests/test_research_runs_storage.py
View file

@ -68,7 +68,8 @@ def _create(
"inferenceRequest": {"model": "local-model"},
"ragScope": rag_scope,
"instructions": instructions,
"budgets": budgets or {
"budgets": budgets
or {
"maxSteps": 5,
"maxSources": 15,
"modelTimeoutSeconds": 30,
@ -208,7 +209,9 @@ def test_loaded_context_length_reads_orchestrator(monkeypatch):
active_model_name = "Qwen2.5-14B-Instruct"
models = {"Qwen2.5-14B-Instruct": {"context_length": 8192}}
monkeypatch.setattr(core_inference, "get_inference_backend", lambda: _Orchestrator(), raising = False)
monkeypatch.setattr(
core_inference, "get_inference_backend", lambda: _Orchestrator(), raising = False
)
assert worker._loaded_context_length() == 8192
assert worker._synthesis_evidence_budget() < worker._MAX_SYNTHESIS_EVIDENCE_CHARS
@ -1342,7 +1345,15 @@ def _patch_web_rank(monkeypatch, *, retrieve = None):
``retrieve_web_chunks`` output (whose retrieval/ranking is covered in test_web_rank.py)."""
from core.rag import web_rank
def default_retrieve(pages, query, *, top_n, min_score, char_budget = None, **kwargs):
def default_retrieve(
pages,
query,
*,
top_n,
min_score,
char_budget = None,
**kwargs,
):
blocks, sources = [], []
for i, page in enumerate(pages, 1):
text = page.get("text") or ""
@ -1359,22 +1370,22 @@ def _patch_web_rank(monkeypatch, *, retrieve = None):
def _bare_supervisor(monkeypatch):
from core import research_runs as worker
supervisor = worker.ResearchSupervisor(
SimpleNamespace(state = SimpleNamespace(server_port = 1))
)
supervisor = worker.ResearchSupervisor(SimpleNamespace(state = SimpleNamespace(server_port = 1)))
return worker, supervisor
def _run_search_then_finish(monkeypatch, fake_tool, *, retrieve = None):
def _run_search_then_finish(
monkeypatch,
fake_tool,
*,
retrieve = None,
):
"""Drive one search step (which auto-scrapes) followed by finish, and return the
completed run plus the synthesis prompts the model was given."""
from core import research_runs as worker
_patch_web_rank(monkeypatch, retrieve = retrieve)
supervisor = worker.ResearchSupervisor(
SimpleNamespace(state = SimpleNamespace(server_port = 1))
)
supervisor = worker.ResearchSupervisor(SimpleNamespace(state = SimpleNamespace(server_port = 1)))
decisions = iter(
(
json.dumps({"action": "search", "title": "Find", "query": "grounding evidence"}),
@ -1384,7 +1395,14 @@ def _run_search_then_finish(monkeypatch, fake_tool, *, retrieve = None):
synthesis_prompts = []
report = "# Report\n\nGrounded finding [source](https://a.example.com)."
async def fake_stream_completion(run, messages, *, json_mode = False, report_progress = True, **kwargs):
async def fake_stream_completion(
run,
messages,
*,
json_mode = False,
report_progress = True,
**kwargs,
):
system = messages[0]["content"]
if "rigorous web research plan" in system:
return json.dumps(_plan()), "planned", "stop"
@ -1419,7 +1437,10 @@ def test_auto_scrape_retrieves_page_chunks_into_synthesis_evidence(research_home
url = arguments.get("url")
if url:
url_calls.append(url)
return {"https://a.example.com": "ALPHA_PAGE_BODY", "https://b.example.com": "BETA_PAGE_BODY"}[url]
return {
"https://a.example.com": "ALPHA_PAGE_BODY",
"https://b.example.com": "BETA_PAGE_BODY",
}[url]
return _two_source_search()
completed, synthesis_prompts = _run_search_then_finish(monkeypatch, fake_tool)
@ -1439,7 +1460,10 @@ def test_auto_scrape_persists_chunk_excerpt_for_resume(research_home, monkeypatc
def fake_tool(name, arguments, *args, **kwargs):
url = arguments.get("url")
if url:
return {"https://a.example.com": "ALPHA_PAGE_BODY", "https://b.example.com": "BETA_PAGE_BODY"}[url]
return {
"https://a.example.com": "ALPHA_PAGE_BODY",
"https://b.example.com": "BETA_PAGE_BODY",
}[url]
return _two_source_search()
completed, _ = _run_search_then_finish(monkeypatch, fake_tool)
@ -1523,9 +1547,7 @@ def test_synthesis_pass_runs_at_synthesis_phase(research_home, monkeypatch):
_create(budgets = _SCRAPE_BUDGETS)
_patch_web_rank(monkeypatch)
supervisor = worker.ResearchSupervisor(
SimpleNamespace(state = SimpleNamespace(server_port = 1))
)
supervisor = worker.ResearchSupervisor(SimpleNamespace(state = SimpleNamespace(server_port = 1)))
decisions = iter(
(
json.dumps({"action": "search", "title": "Find", "query": "q"}),
@ -1534,7 +1556,14 @@ def test_synthesis_pass_runs_at_synthesis_phase(research_home, monkeypatch):
)
captured = {}
async def fake_stream_completion(run, messages, *, json_mode = False, report_progress = True, **kwargs):
async def fake_stream_completion(
run,
messages,
*,
json_mode = False,
report_progress = True,
**kwargs,
):
system = messages[0]["content"]
if "rigorous web research plan" in system:
return json.dumps(_plan()), "p", "stop"
@ -1563,13 +1592,16 @@ def test_auto_scrape_respects_char_budgets(research_home, monkeypatch):
_patch_web_rank(monkeypatch)
# space-separated so page cleaning keeps it (a single 50k-char token is stripped as junk)
monkeypatch.setattr(worker, "execute_tool", lambda *a, **k: "yy " * 20_000)
step_sources = [
{"url": f"https://s{i}.example.com", "title": f"S{i}"} for i in range(3)
]
step_sources = [{"url": f"https://s{i}.example.com", "title": f"S{i}"} for i in range(3)]
section, fetched = asyncio.run(
supervisor._auto_scrape_sources(
{"id": "run-x"}, "question", step_sources, set(),
limit = worker._AUTO_SCRAPE_TOP_K, tool_timeout = 10, website_policy = None,
{"id": "run-x"},
"question",
step_sources,
set(),
limit = worker._AUTO_SCRAPE_TOP_K,
tool_timeout = 10,
website_policy = None,
)
)
# the folded evidence is bounded chunks, not the 150k of raw page bodies
@ -1591,8 +1623,13 @@ def test_auto_scrape_falls_back_when_no_relevant_chunks(research_home, monkeypat
step_sources = [{"url": "https://s.example.com", "title": "S"}]
section, fetched = asyncio.run(
supervisor._auto_scrape_sources(
{"id": "run-x"}, "find the special token", step_sources, set(),
limit = worker._AUTO_SCRAPE_TOP_K, tool_timeout = 10, website_policy = None,
{"id": "run-x"},
"find the special token",
step_sources,
set(),
limit = worker._AUTO_SCRAPE_TOP_K,
tool_timeout = 10,
website_policy = None,
)
)
assert section == ""
@ -1671,8 +1708,13 @@ def test_auto_scrape_honors_numeric_limit(research_home, monkeypatch):
step_sources = [{"url": f"https://s{i}.example.com", "title": f"S{i}"} for i in range(3)]
_section, fetched = asyncio.run(
supervisor._auto_scrape_sources(
{"id": "run-x"}, "question", step_sources, set(),
limit = 1, tool_timeout = 10, website_policy = None,
{"id": "run-x"},
"question",
step_sources,
set(),
limit = 1,
tool_timeout = 10,
website_policy = None,
)
)
assert len(called) == 1

24
studio/backend/tests/test_web_rank.py
View file

@ -34,7 +34,12 @@ def fake_embeddings(monkeypatch):
lambda model_name = None: (lambda text: max(1, len(text.split()))),
)
def encode(texts, *, model_name = None, normalize = True):
def encode(
texts,
*,
model_name = None,
normalize = True,
):
rows = []
for text in texts:
low = text.lower()
@ -68,8 +73,16 @@ def _scope_rows(db_file):
def test_retrieves_relevant_passages_as_chunks(rag_home):
pages = [
{"text": "LoRA is a low-rank adapter method for fine tuning.", "title": "LoRA", "url": "https://a"},
{"text": "The Apache license governs redistribution terms.", "title": "License", "url": "https://b"},
{
"text": "LoRA is a low-rank adapter method for fine tuning.",
"title": "LoRA",
"url": "https://a",
},
{
"text": "The Apache license governs redistribution terms.",
"title": "License",
"url": "https://b",
},
]
rendered, sources = web_rank.retrieve_web_chunks(pages, "what is lora", top_n = 5, min_score = 0.0)
@ -105,7 +118,10 @@ def test_empty_and_invalid_inputs_return_empty(rag_home):
assert web_rank.retrieve_web_chunks([], "lora", top_n = 5, min_score = 0.1) == ("", [])
assert web_rank.retrieve_web_chunks([{"text": " "}], "lora", top_n = 5, min_score = 0.1) == ("", [])
assert web_rank.retrieve_web_chunks([{"text": "lora"}], "", top_n = 5, min_score = 0.1) == ("", [])
assert web_rank.retrieve_web_chunks([{"text": "lora"}], "lora", top_n = 0, min_score = 0.1) == ("", [])
assert web_rank.retrieve_web_chunks([{"text": "lora"}], "lora", top_n = 0, min_score = 0.1) == (
"",
[],
)
def test_ephemeral_scope_is_cleaned_up(rag_home):