fix: SSE stream parser crashes with NoneType on providers sending null choice/usage/tc entries (#2389)

* fix: SSE parser crashes with NoneType on MiniMax-M3 (and any provider sending null choice/usage/tc)

Three guards added in stream_llm:

1. choices[0] null check — MiniMax (and some other providers) send a
   choices entry as None. `_choices[0].get("delta")` raised
   AttributeError. Now checks `_choices[0] is not None` before calling
   .get().

2. usage null guard — j["usage"] can arrive as None (not a dict) on
   some providers. Added `or {}` so subsequent .get() calls don't crash.

3. tool_calls null entry skip — individual entries in the tool_calls
   array can be None. Added `if tc is None: continue` before
   tc.get("function").

All three match the `or {}` / null-guard pattern used elsewhere in the
same block. Safe for all OpenAI-compatible providers.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* fix: guard null choice in elif-choices SSE branch

The usage-chunk path already guarded _choices[0] is not None, but the
elif "choices" branch that processes content/tool-call deltas did not.
A chunk like {"choices": [null]} or {"choices": [null], "usage": null}
reaches j["choices"][0].get("delta") and crashes with:

    'NoneType' object has no attribute 'get'

Fix: extract choices[0] into _c0 and continue to the next chunk when
it is None, matching the guard already applied in the usage path.

Adds three focused regressions covering the paths the maintainer flagged:
- {"choices": [null]}
- {"choices": [null], "usage": null}
- tool_calls array containing a null entry alongside a valid call

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

---------

Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>

src/llm_core.py

@@ -1398,7 +1398,7 @@ async def stream_llm(url: str, model: str, messages: List[Dict], temperature: fl
                                 j = json.loads(data)
                                 # Usage chunk (from stream_options)
                                 _choices = j.get("choices") or []
-                                _delta0 = _choices[0].get("delta") if _choices else None
+                                _delta0 = _choices[0].get("delta") if (_choices and _choices[0] is not None) else None
                                 # Capture usage whenever the chunk carries it and
                                 # the delta has no actual output. Some gateways /
                                 # local servers attach usage to the FINAL delta,
@@ -1412,7 +1412,7 @@ async def stream_llm(url: str, model: str, messages: List[Dict], temperature: fl
                                     or _delta0.get("tool_calls")
                                 )
                                 if "usage" in j and not _delta_has_output:
-                                    u = j["usage"]
+                                    u = j["usage"] or {}
                                     _usage_data = {"input_tokens": u.get("prompt_tokens", 0), "output_tokens": u.get("completion_tokens", 0)}
                                     # llama.cpp puts a `timings` block alongside `usage` with the
                                     # TRUE generation speed (predicted_per_second) — pure decode,
@@ -1427,7 +1427,10 @@ async def stream_llm(url: str, model: str, messages: List[Dict], temperature: fl
                                             _usage_data["prefill_tps"] = round(_tm["prompt_per_second"], 2)
                                     yield f'data: {json.dumps({"type": "usage", "data": _usage_data})}\n\n'
                                 elif "choices" in j:
-                                    delta = j["choices"][0].get("delta") or {}
+                                    _c0 = (j["choices"] or [None])[0]
+                                    if _c0 is None:
+                                        continue
+                                    delta = _c0.get("delta") or {}
                                     if isinstance(delta, dict):
                                         # Text content
                                         # Reasoning tokens (VLLM --reasoning-parser, e.g. Qwen3/DeepSeek-R1, Nemotron). vLLM 0.20.2 / NIM emit the field as `reasoning`; older builds use `reasoning_content`. Accept either.
@@ -1446,6 +1449,8 @@ async def stream_llm(url: str, model: str, messages: List[Dict], temperature: fl
                                             yield f'data: {json.dumps({"delta": content})}\n\n'
                                         # Native tool calls — accumulate across chunks
                                         for tc in delta.get("tool_calls") or []:
+                                            if tc is None:
+                                                continue
                                             func = tc.get("function") or {}
                                             raw_idx = tc.get("index")
                                             if raw_idx is None: