chore: import upstream snapshot with attribution

backend/python/common/python_utils.py

@@ -0,0 +1,76 @@
+"""Generic utilities shared across Python gRPC backends.
+
+These helpers don't depend on any specific inference framework and can be
+imported by any backend that needs to parse LocalAI gRPC options or build a
+chat-template-compatible message list from proto Message objects.
+"""
+import json
+
+
+def parse_options(options_list):
+    """Parse Options[] list of ``key:value`` strings into a dict.
+
+    Supports type inference for common cases (bool, int, float). Unknown or
+    mixed-case values are returned as strings.
+
+    Used by LoadModel to extract backend-specific options passed via
+    ``ModelOptions.Options`` in ``backend.proto``.
+    """
+    opts = {}
+    for opt in options_list:
+        if ":" not in opt:
+            continue
+        key, value = opt.split(":", 1)
+        key = key.strip()
+        value = value.strip()
+        # Try type conversion
+        if value.lower() in ("true", "false"):
+            opts[key] = value.lower() == "true"
+        else:
+            try:
+                opts[key] = int(value)
+            except ValueError:
+                try:
+                    opts[key] = float(value)
+                except ValueError:
+                    opts[key] = value
+    return opts
+
+
+def messages_to_dicts(proto_messages):
+    """Convert proto ``Message`` objects to dicts suitable for ``apply_chat_template``.
+
+    Handles: ``role``, ``content``, ``name``, ``tool_call_id``,
+    ``reasoning_content``, ``tool_calls`` (JSON string → Python list).
+
+    HuggingFace chat templates (and their MLX/vLLM wrappers) expect a list of
+    plain dicts — proto Message objects don't work directly with Jinja, so
+    this conversion is needed before every ``apply_chat_template`` call.
+    """
+    result = []
+    for msg in proto_messages:
+        d = {"role": msg.role, "content": msg.content or ""}
+        if msg.name:
+            d["name"] = msg.name
+        if msg.tool_call_id:
+            d["tool_call_id"] = msg.tool_call_id
+        if msg.reasoning_content:
+            d["reasoning_content"] = msg.reasoning_content
+        if msg.tool_calls:
+            try:
+                tool_calls = json.loads(msg.tool_calls)
+                # Chat templates (e.g. Qwen) iterate function.arguments as a
+                # mapping, but the OpenAI wire format carries it as a JSON
+                # string — decode it back so the template's .items() works.
+                for tc in tool_calls:
+                    fn = tc.get("function") if isinstance(tc, dict) else None
+                    if isinstance(fn, dict) and isinstance(fn.get("arguments"), str):
+                        try:
+                            fn["arguments"] = json.loads(fn["arguments"])
+                        except json.JSONDecodeError:
+                            pass
+                d["tool_calls"] = tool_calls
+            except json.JSONDecodeError:
+                pass
+        result.append(d)
+    return result