feat: add generate_text method to AIProvider for non-JSON responses

The AI Chat Builder needs conversational text responses, not JSON-only.
Gemini's generate_json forces response_mime_type='application/json'
which is incompatible. The new generate_text method omits this constraint.

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

backend/app/core/ai_chat_service.py

@@ -242,7 +242,7 @@ async def start_chat_session(
     provider_name = settings.AI_PROVIDER
 
     messages = [{"role": "user", "content": primer}]
-    response_text, input_tokens, output_tokens = await provider.generate_json(
+    response_text, input_tokens, output_tokens = await provider.generate_text(
         system_prompt=system_prompt,
         messages=messages,
         max_tokens=1500,
@@ -291,7 +291,7 @@ async def send_message(
     ]
 
     provider = get_ai_provider()
-    response_text, input_tokens, output_tokens = await provider.generate_json(
+    response_text, input_tokens, output_tokens = await provider.generate_text(
         system_prompt=system_prompt,
         messages=provider_messages,
         max_tokens=2000,
@@ -371,7 +371,7 @@ Also provide metadata as a separate JSON object after the tree:
     provider = get_ai_provider()
 
     for attempt in range(2):  # One try + one retry
-        response_text, input_tokens, output_tokens = await provider.generate_json(
+        response_text, input_tokens, output_tokens = await provider.generate_text(
             system_prompt=system_prompt,
             messages=provider_messages,
             max_tokens=8000,

backend/app/core/ai_provider.py

@@ -35,6 +35,25 @@ class AIProvider(ABC):
         """
         ...
 
+    @abstractmethod
+    async def generate_text(
+        self,
+        system_prompt: str,
+        messages: list[dict[str, str]],
+        max_tokens: int = 4096,
+    ) -> tuple[str, int, int]:
+        """Generate a text response from the AI model (no JSON constraint).
+
+        Args:
+            system_prompt: System-level instruction for the model.
+            messages: List of message dicts with "role" and "content" keys.
+            max_tokens: Maximum output tokens.
+
+        Returns:
+            Tuple of (response_text, input_tokens, output_tokens).
+        """
+        ...
+
 
 class GeminiProvider(AIProvider):
     """Google Gemini provider using the google-genai SDK."""
@@ -95,6 +114,56 @@ class GeminiProvider(AIProvider):
 
         return text, input_tokens, output_tokens
 
+    async def generate_text(
+        self,
+        system_prompt: str,
+        messages: list[dict[str, str]],
+        max_tokens: int = 4096,
+    ) -> tuple[str, int, int]:
+        from google import genai
+        from google.genai import types as genai_types
+
+        client = genai.Client(api_key=self._api_key)
+
+        contents: list[genai_types.Content] = []
+        for msg in messages:
+            role = "model" if msg["role"] == "assistant" else "user"
+            contents.append(
+                genai_types.Content(
+                    role=role,
+                    parts=[genai_types.Part(text=msg["content"])],
+                )
+            )
+
+        config = genai_types.GenerateContentConfig(
+            system_instruction=system_prompt,
+            max_output_tokens=max_tokens,
+            # No response_mime_type — allow free-form text
+        )
+
+        response = await client.aio.models.generate_content(
+            model=self._model,
+            contents=contents,
+            config=config,
+        )
+
+        if response.candidates:
+            finish_reason = getattr(response.candidates[0], "finish_reason", None)
+            logger.info("Gemini finish_reason=%s model=%s", finish_reason, self._model)
+            if str(finish_reason) == "MAX_TOKENS":
+                logger.warning(
+                    "Gemini output truncated (MAX_TOKENS). max_output_tokens=%d",
+                    max_tokens,
+                )
+
+        text = response.text or ""
+        input_tokens = getattr(response.usage_metadata, "prompt_token_count", 0) or 0
+        output_tokens = (
+            getattr(response.usage_metadata, "candidates_token_count", 0) or 0
+        )
+
+        return text, input_tokens, output_tokens
+
 
 class AnthropicProvider(AIProvider):
     """Anthropic Claude provider using the anthropic SDK."""
@@ -130,6 +199,15 @@ class AnthropicProvider(AIProvider):
 
         return text, input_tokens, output_tokens
 
+    async def generate_text(
+        self,
+        system_prompt: str,
+        messages: list[dict[str, str]],
+        max_tokens: int = 4096,
+    ) -> tuple[str, int, int]:
+        # Anthropic doesn't differentiate between JSON and text mode
+        return await self.generate_json(system_prompt, messages, max_tokens)
+
 
 def get_ai_provider() -> AIProvider:
     """Factory that returns the configured AI provider.