refactor: migrate AI tree generator to provider abstraction

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

backend/app/core/ai_tree_generator_service.py

@@ -1,11 +1,11 @@
-"""AI-powered tree generation service using Anthropic Claude API.
+"""AI-powered tree generation service.
 
 Implements the 4-stage wizard flow:
   Stage 2 (scaffold): AI suggests 4-7 top-level branches
   Stage 3 (branch_detail): AI generates detailed nodes per branch
   Stage 4 (assemble): Pure assembly logic — zero AI calls
 
-System prompts are static constants to enable Anthropic prompt caching.
+Uses the provider abstraction from ai_provider.py (supports Gemini + Anthropic).
 """
 import json
 import logging
@@ -13,8 +13,7 @@ import re
 import uuid
 from typing import Any
 
-import anthropic
-
+from app.core.ai_provider import get_ai_provider
 from app.core.config import settings
 from app.core.ai_tree_validator import validate_generated_tree, count_tree_stats
 
@@ -121,15 +120,6 @@ def _strip_markdown_fences(text: str) -> str:
     return text
 
 
-def _get_client() -> anthropic.AsyncAnthropic:
-    """Get configured async Anthropic client."""
-    if not settings.ANTHROPIC_API_KEY:
-        raise RuntimeError("ANTHROPIC_API_KEY not configured")
-    return anthropic.AsyncAnthropic(
-        api_key=settings.ANTHROPIC_API_KEY,
-        timeout=settings.AI_REQUEST_TIMEOUT_SECONDS,
-    )
-
 
 def _estimate_cost(input_tokens: int, output_tokens: int) -> float:
     """Estimate USD cost from token counts."""
@@ -146,7 +136,7 @@ async def scaffold_branches(
     Returns (branches, input_tokens, output_tokens, estimated_cost).
     Raises ValueError on invalid response.
     """
-    client = _get_client()
+    provider = get_ai_provider()
 
     flow_type = wizard_state.get("flow_type", "troubleshooting")
     name = wizard_state.get("name", "")
@@ -161,16 +151,13 @@ async def scaffold_branches(
     if tags:
         user_message += f"Environment: {', '.join(tags)}\n"
 
-    response = await client.messages.create(
-        model=settings.AI_MODEL,
-        max_tokens=1024,
-        system=SCAFFOLD_SYSTEM_PROMPT,
+    raw_text, input_tokens, output_tokens = await provider.generate_json(
+        system_prompt=SCAFFOLD_SYSTEM_PROMPT,
         messages=[{"role": "user", "content": user_message}],
+        max_tokens=1024,
     )
 
-    raw_text = _strip_markdown_fences(response.content[0].text)
-    input_tokens = response.usage.input_tokens
-    output_tokens = response.usage.output_tokens
+    raw_text = _strip_markdown_fences(raw_text)
     cost = _estimate_cost(input_tokens, output_tokens)
 
     try:
@@ -196,7 +183,7 @@ async def generate_branch_detail(
     On validation failure, retries once with corrective prompt.
     Raises ValueError if both attempts fail.
     """
-    client = _get_client()
+    provider = get_ai_provider()
 
     flow_type = wizard_state.get("flow_type", "troubleshooting")
     name = wizard_state.get("name", "")
@@ -217,31 +204,22 @@ async def generate_branch_detail(
     total_output = 0
 
     for attempt in range(3):
-        response = await client.messages.create(
-            model=settings.AI_MODEL,
-            max_tokens=8192,
-            system=BRANCH_DETAIL_SYSTEM_PROMPT,
+        raw_text, input_tokens, output_tokens = await provider.generate_json(
+            system_prompt=BRANCH_DETAIL_SYSTEM_PROMPT,
             messages=messages,
+            max_tokens=8192,
         )
 
-        total_input += response.usage.input_tokens
-        total_output += response.usage.output_tokens
+        total_input += input_tokens
+        total_output += output_tokens
         logger.debug(
-            "branch_detail attempt=%d stop_reason=%s content_blocks=%d output_tokens=%d",
+            "branch_detail attempt=%d output_tokens=%d",
             attempt,
-            response.stop_reason,
-            len(response.content),
-            response.usage.output_tokens,
+            output_tokens,
         )
-        if response.stop_reason == "max_tokens":
-            logger.warning(
-                "branch_detail attempt=%d hit max_tokens limit (%d output tokens) — response may be truncated",
-                attempt,
-                response.usage.output_tokens,
-            )
-        raw_text = _strip_markdown_fences(response.content[0].text) if response.content else ""
+        raw_text = _strip_markdown_fences(raw_text) if raw_text else ""
         if not raw_text:
-            logger.warning("branch_detail attempt=%d returned empty text, stop_reason=%s", attempt, response.stop_reason)
+            logger.warning("branch_detail attempt=%d returned empty text", attempt)
 
         try:
             branch_tree = json.loads(raw_text)