refactor: share execution prep for tool-call phase

Keep the current tool-call bridge contract stable while extracting shared
execution setup and tightening Anthropic forwarding regressions.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

app/http/execution_core.py

@@ -0,0 +1,148 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any, Awaitable, Callable
+
+from ..lingma_pool import LingmaPool, PoolInstance
+from ..model_map import build_model_name_map, flatten_model_keys, resolve_model
+from ..session_cache import SessionCache, hash_branch_context
+
+
+@dataclass
+class ExecutionContext:
+    ask_mode: str
+    lookup_key: str | None
+    write_key: str | None
+    cached_session_id: str | None
+    inst: PoolInstance
+    model: str
+    prompt: str
+    is_reply: bool
+    affinity: str | None
+
+
+def _resolve_ask_mode(model: str, has_tooling_context: bool, *, default_ask_mode: str) -> str:
+    model_name = (model or "").lower()
+    if model_name in {"lingma-agent", "agent"} or has_tooling_context:
+        return "agent"
+    return default_ask_mode
+
+
+async def _apply_cached_instance_or_invalidate(
+    *,
+    protocol: str,
+    logger: Any,
+    session_cache: SessionCache,
+    inst: PoolInstance,
+    cached_instance_name: str | None,
+    cached_session_id: str | None,
+    lookup_key: str | None,
+) -> str | None:
+    if cached_instance_name and inst.name != cached_instance_name:
+        logger.info(
+            "%s session cache instance %s unhealthy, falling back to %s",
+            protocol,
+            cached_instance_name,
+            inst.name,
+        )
+        if lookup_key:
+            await session_cache.invalidate(lookup_key)
+        return None
+    return cached_session_id
+
+
+async def prepare_execution_context(
+    *,
+    protocol: str,
+    requested_model: str,
+    has_tooling_context: bool,
+    tool_config: dict[str, Any] | None,
+    messages_dump: list[dict[str, Any]],
+    api_key: str,
+    affinity_key: str | None,
+    pool: LingmaPool,
+    session_cache: SessionCache,
+    logger: Any,
+    default_model: str,
+    default_ask_mode: str,
+    ensure_instance_logged_in: Callable[[PoolInstance], Awaitable[Any]],
+    last_user_text: Callable[[list[dict[str, Any]]], str],
+    messages_to_prompt: Callable[[list[dict[str, Any]]], str],
+) -> ExecutionContext:
+    ask_mode = _resolve_ask_mode(
+        requested_model,
+        has_tooling_context,
+        default_ask_mode=default_ask_mode,
+    )
+
+    reuse_eligible = (
+        session_cache.enabled
+        and ask_mode == "chat"
+        and len(messages_dump) >= 2
+        and not has_tooling_context
+    )
+    lookup_key: str | None = None
+    write_key: str | None = None
+    cached_session_id: str | None = None
+    cached_instance_name: str | None = None
+    if reuse_eligible:
+        prefix_branch_context = hash_branch_context(messages_dump[:-1])
+        lookup_key = session_cache.build_key(
+            api_key,
+            messages_dump[:-1],
+            tool_config=tool_config,
+            branch_context=prefix_branch_context,
+        )
+        write_key = session_cache.build_key(
+            api_key,
+            messages_dump,
+            tool_config=tool_config,
+            branch_context=hash_branch_context(messages_dump),
+        )
+        entry = await session_cache.get(lookup_key)
+        if entry is None:
+            legacy_lookup_key = session_cache.build_key(api_key, messages_dump[:-1], tool_config=tool_config)
+            entry = await session_cache.get(legacy_lookup_key)
+            if entry is not None:
+                lookup_key = legacy_lookup_key
+        if entry is not None:
+            cached_session_id = entry.session_id
+            cached_instance_name = entry.instance_name or None
+
+    affinity = cached_instance_name or affinity_key
+    inst = pool.pick(affinity_key=affinity)
+    cached_session_id = await _apply_cached_instance_or_invalidate(
+        protocol=protocol,
+        logger=logger,
+        session_cache=session_cache,
+        inst=inst,
+        cached_instance_name=cached_instance_name,
+        cached_session_id=cached_session_id,
+        lookup_key=lookup_key,
+    )
+
+    await ensure_instance_logged_in(inst)
+
+    models = await inst.client.query_models()
+    available = flatten_model_keys(models)
+    name_map = build_model_name_map(models)
+    model = resolve_model(requested_model, available, default_model, name_map)
+
+    if cached_session_id:
+        prompt = last_user_text(messages_dump)
+        is_reply = True
+    else:
+        prompt = messages_to_prompt(messages_dump)
+        is_reply = False
+
+    return ExecutionContext(
+        ask_mode=ask_mode,
+        lookup_key=lookup_key,
+        write_key=write_key,
+        cached_session_id=cached_session_id,
+        inst=inst,
+        model=model,
+        prompt=prompt,
+        is_reply=is_reply,
+        affinity=affinity,
+    )

app/http/tool_bridge.py

@@ -15,6 +15,49 @@ def _json_string(value: Any) -> str:
         return "{}"
 
 
+def _openai_tool_name(tool: Any) -> str | None:
+    if not isinstance(tool, dict):
+        return None
+    if tool.get("type") == "function":
+        fn = tool.get("function")
+        if isinstance(fn, dict):
+            name = fn.get("name")
+            if isinstance(name, str) and name.strip():
+                return name.strip()
+    name = tool.get("name")
+    if isinstance(name, str) and name.strip():
+        return name.strip()
+    return None
+
+
+def _anthropic_tool_name(tool: Any) -> str | None:
+    if not isinstance(tool, dict):
+        return None
+    name = tool.get("name")
+    if isinstance(name, str) and name.strip():
+        return name.strip()
+    fn = tool.get("function")
+    if isinstance(fn, dict):
+        nested_name = fn.get("name")
+        if isinstance(nested_name, str) and nested_name.strip():
+            return nested_name.strip()
+    return None
+
+
+def _tool_event_allowed(
+    tool_name: str,
+    tool_config: dict[str, Any] | None,
+    *,
+    forced_tool_name: str | None = None,
+) -> bool:
+    if not (tool_config and isinstance(tool_config.get("tools"), list) and tool_config.get("tools")):
+        return True
+    for tool in tool_config.get("tools") or []:
+        if tool_name == _anthropic_tool_name(tool) or tool_name == _openai_tool_name(tool):
+            return True
+    return bool(forced_tool_name and tool_name == forced_tool_name)
+
+
 def _openai_forced_tool_name(tool_choice: Any) -> str | None:
     if not isinstance(tool_choice, dict):
         return None