add kia ai chat tools5

src/services/kiaai/chat.py

@@ -12,100 +12,134 @@ from ...config import settings
 
 KIA_BASE_URL = "https://api.kie.ai"
 
+# Models that support reasoning_effort
+REASONING_MODELS = {
+    "gpt-5-2", "claude-sonnet-4-5", "claude-opus-4-5",
+    "gemini-3-pro", "gemini-2.5-flash", "gemini-2.5-pro",
+}
+
 
 def _to_openai_messages(messages: list[BaseMessage]) -> list[dict]:
-    """Convert LangChain messages to OpenAI-compatible dict format."""
+    """
+    Convert LangChain messages to KIA/OpenAI-compatible dict format.
+
+    Supports multimodal HumanMessages where content is already a list
+    (e.g. [{"type": "text", "text": "..."}, {"type": "image_url", ...}]).
+    """
     result = []
     for msg in messages:
         if isinstance(msg, SystemMessage):
             result.append({"role": "system", "content": msg.content})
+
         elif isinstance(msg, HumanMessage):
-            result.append({"role": "user", "content": msg.content})
+            # content may be a plain string or a multimodal list
+            if isinstance(msg.content, list):
+                result.append({"role": "user", "content": msg.content})
+            else:
+                result.append({"role": "user", "content": msg.content})
+
         elif isinstance(msg, AIMessage):
             result.append({"role": "assistant", "content": msg.content})
+
         elif isinstance(msg, ToolMessage):
-            result.append({"role": "tool", "content": msg.content, "tool_call_id": msg.tool_call_id})
+            result.append({
+                "role": "tool",
+                "content": msg.content,
+                "tool_call_id": msg.tool_call_id,
+            })
+
         else:
             result.append({"role": "user", "content": str(msg.content)})
+
     return result
 
 
 class KiaAIService:
     """
-    Service for calling KIA AI models directly via httpx (non-streaming).
+    Service for calling KIA AI models directly via httpx SSE streaming.
 
-    Bypasses LangChain's streaming infrastructure entirely to avoid the
-    'TypeError: object of type NoneType has no len()' bug caused by
-    KIA API returning chunks with choices=null.
+    KIA always responds with SSE stream regardless of stream=false in payload.
+    We collect all delta chunks and return a complete AIMessage.
 
     Endpoint pattern:
         POST https://api.kie.ai/{modelName}/v1/chat/completions
     """
 
-    async def invoke(self, model: str, messages: list[BaseMessage]) -> AIMessage:
+    async def invoke(
+        self,
+        model: str,
+        messages: list[BaseMessage],
+        reasoning_effort: str | None = None,
+    ) -> AIMessage:
         """
-        Call KIA API directly and return an AIMessage.
+        Call KIA API and return an assembled AIMessage.
 
         Args:
-            model: Model name with optional 'kia/' prefix (e.g. 'kia/gpt-5-2')
-            messages: List of LangChain BaseMessage objects
-
-        Returns:
-            AIMessage with content and usage_metadata populated
+            model:            Model name with optional 'kia/' prefix (e.g. 'kia/gpt-5-2')
+            messages:         LangChain message list
+            reasoning_effort: 'low' | 'medium' | 'high' — only for supported models
         """
         model_name = model.removeprefix("kia/")
         url = f"{KIA_BASE_URL}/{model_name}/v1/chat/completions"
 
-        payload = {
-            "model": model_name,
+        payload: dict = {
             "messages": _to_openai_messages(messages),
-            "stream": False,
         }
 
-        print(f"[KiaAI] POST {url} | messages_count={len(messages)}")
+        # Add reasoning_effort only for models that support it
+        if reasoning_effort and model_name in REASONING_MODELS:
+            payload["reasoning_effort"] = reasoning_effort
+
+        print(f"[KiaAI] POST {url}")
+        print(f"[KiaAI] messages_count={len(messages)} | reasoning_effort={reasoning_effort}")
+        print(f"[KiaAI] payload (no content): { {k: v for k, v in payload.items() if k != 'messages'} }")
+
+        full_content = ""
+        usage: dict = {}
 
         async with httpx.AsyncClient(timeout=180.0) as client:
-            resp = await client.post(
+            async with client.stream(
+                "POST",
                 url,
                 json=payload,
                 headers={
                     "Authorization": f"Bearer {settings.kia_api_key}",
                     "Content-Type": "application/json",
                 },
-            )
+            ) as resp:
+                print(f"[KiaAI] HTTP {resp.status_code}")
+                resp.raise_for_status()
 
-            print(f"[KiaAI] HTTP {resp.status_code}")
-            resp.raise_for_status()
-            raw_text = resp.text
+                async for line in resp.aiter_lines():
+                    if not line.strip():
+                        continue
 
-        print(f"[KiaAI] Raw response text:\n{raw_text}\n---")
+                    # SSE lines: "data: {...}" or "data: [DONE]" or raw JSON
+                    raw_data = line.removeprefix("data:").strip()
 
-        # KIA may return NDJSON (multiple JSON lines) instead of a single JSON object.
-        # We pick the last non-empty line that contains "choices" as the final completion chunk.
-        data = None
-        lines = [line.strip() for line in raw_text.strip().splitlines() if line.strip()]
-        for line in reversed(lines):
-            try:
-                candidate = json.loads(line)
-                if "choices" in candidate and candidate["choices"]:
-                    data = candidate
-                    break
-            except json.JSONDecodeError:
-                continue
+                    if raw_data == "[DONE]":
+                        print("[KiaAI] Stream [DONE]")
+                        break
 
-        if data is None:
-            # Fallback: try parsing the whole text as one JSON
-            data = json.loads(raw_text)
+                    try:
+                        chunk = json.loads(raw_data)
+                    except json.JSONDecodeError:
+                        print(f"[KiaAI] Skipping non-JSON line: {raw_data!r}")
+                        continue
 
-        print(f"[KiaAI] Parsed response keys: {list(data.keys())}")
+                    choices = chunk.get("choices") or []
+                    for choice in choices:
+                        delta = choice.get("delta") or {}
+                        content_piece = delta.get("content") or ""
+                        full_content += content_piece
 
-        content = data["choices"][0]["message"]["content"]
-        usage = data.get("usage") or {}
+                    if chunk.get("usage"):
+                        usage = chunk["usage"]
 
-        print(f"[KiaAI] content='{content[:200]}'")
+        print(f"[KiaAI] Final content (first 200): '{full_content[:200]}'")
         print(f"[KiaAI] usage={usage}")
 
-        ai_msg = AIMessage(content=content)
+        ai_msg = AIMessage(content=full_content)
         ai_msg.usage_metadata = {
             "input_tokens": usage.get("prompt_tokens", 0),
             "output_tokens": usage.get("completion_tokens", 0),
@@ -115,7 +149,6 @@ class KiaAIService:
         return ai_msg
 
 
-# Keep get_kia_model for compatibility — but actual chat calls should use KiaAIService.invoke()
+# Compatibility stub — KIA models bypass LangChain and use KiaAIService.invoke() directly
 def get_kia_model(model: str):
-    """Compatibility stub — returns None for KIA models since they use direct HTTP."""
     return None