feat: layered precise generation for A4 report images

3-phase pipeline to solve LLM prompt overflow from too many OCR elements:
Phase 1 (generate_skeleton): compressed layout schema → skeleton JRXML
Phase 2 (refine_layout): sampled coordinates → pixel-level position tuning
Phase 3 (map_fields): OCR field names → replace $F{field_N} placeholders

Only triggered when layout_schema.total_rows > 0 on initial_generation intent.
Text requests and all other intents are unaffected (zero behavior change).

agent/nodes.py

@@ -378,7 +378,7 @@ def load_session_node(state: AgentState) -> Dict:
                         "current_jrxml", "final_jrxml", "compressed_history",
                         "session_name", "created_at", "history_states",
                         "ocr_extraction_result", "uploaded_file_path",
-                        "annotation_result"):
+                        "annotation_result", "layout_schema", "ocr_elements"):
                 if key in saved and key not in ("user_input", "stage"):
                     state[key] = saved[key]
             state["session_name"] = data.get("session_name", "")
@@ -402,7 +402,7 @@ def save_session_node(state: AgentState) -> Dict:
                     "current_jrxml", "final_jrxml", "compressed_history",
                     "status", "error_msg", "history_states",
                     "ocr_extraction_result", "uploaded_file_path",
-                    "annotation_result"):
+                    "annotation_result", "layout_schema", "ocr_elements"):
             if key in state:
                 persistable[key] = state[key]
         persistable["updated_at"] = _now_iso()
@@ -437,6 +437,28 @@ def _now_iso() -> str:
     return datetime.now(timezone.utc).isoformat()
 
 
+def _format_row_coordinates(row: dict) -> dict:
+    """将单行 OCR 元素格式化为紧凑的坐标描述，供阶段二 refine_layout 使用。"""
+    if not isinstance(row, dict):
+        return {}
+    elements = row.get("elements", [])
+    if not elements:
+        return {"y_center": row.get("y_center", 0), "columns": []}
+    sorted_elems = sorted(elements, key=lambda e: e.get("x", 0))
+    cols = []
+    for ci, e in enumerate(sorted_elems):
+        cols.append({
+            "col": ci,
+            "x": e.get("x", 0),
+            "y": e.get("y", 0),
+            "w": e.get("w", 0),
+            "h": e.get("h", 0),
+            "font_size": e.get("font_size", 12),
+            "text": e.get("text", ""),
+        })
+    return {"y_center": row.get("y_center", 0), "columns": cols}
+
+
 def _format_ocr_context(state: AgentState) -> str:
     """将 OCR 提取结果格式化为 LLM 可用的上下文文本。"""
     ocr_result = state.get("ocr_extraction_result")
@@ -540,6 +562,104 @@ def generate(state: AgentState) -> Dict:
     return state
 
 
+@log_node("generate_skeleton")
+def generate_skeleton(state: AgentState) -> Dict:
+    """阶段一：根据压缩的布局 schema 生成骨架 JRXML（$F{field_N} 占位）。"""
+    from langgraph.config import get_stream_writer
+
+    writer = get_stream_writer()
+    llm = get_llm(caller="generate_skeleton")
+
+    schema = state.get("layout_schema", {})
+    schema_text = schema.get("schema_text", "") if isinstance(schema, dict) else ""
+    user_request = state.get("user_input", "")
+
+    prompt = load_prompt("skeleton_generation").format(
+        layout_schema=schema_text,
+        context=state.get("retrieved_context", ""),
+        user_request=user_request,
+    )
+    full = []
+    for chunk in llm.stream(prompt):
+        full.append(chunk)
+        writer({"type": "stream", "node": "generate_skeleton", "text": chunk})
+    jrxml = _extract_jrxml("".join(full))
+    state["current_jrxml"] = jrxml
+    state["conversation_history"].append({"role": "assistant", "content": jrxml})
+    return state
+
+
+@log_node("refine_layout")
+def refine_layout(state: AgentState) -> Dict:
+    """阶段二：使用采样坐标（表头 + 首行数据 + 最后一行）精确调整元素位置。"""
+    from langgraph.config import get_stream_writer
+
+    writer = get_stream_writer()
+    llm = get_llm(caller="refine_layout")
+
+    ocr_rows = state.get("ocr_elements", [])
+    sampled = {}
+    if isinstance(ocr_rows, list) and len(ocr_rows) >= 1:
+        sampled["header_row"] = _format_row_coordinates(ocr_rows[0])
+        if len(ocr_rows) > 1:
+            sampled["first_data_row"] = _format_row_coordinates(ocr_rows[1])
+        if len(ocr_rows) > 2:
+            sampled["last_row"] = _format_row_coordinates(ocr_rows[-1])
+    sampled_text = json.dumps(sampled, ensure_ascii=False, indent=2)
+
+    prompt = load_prompt("refine_layout").format(
+        current_jrxml=state.get("current_jrxml", ""),
+        sampled_coordinates=sampled_text,
+    )
+    full = []
+    for chunk in llm.stream(prompt):
+        full.append(chunk)
+        writer({"type": "stream", "node": "refine_layout", "text": chunk})
+    jrxml = _extract_jrxml("".join(full))
+    state["current_jrxml"] = jrxml
+    state["conversation_history"].append({"role": "assistant", "content": jrxml})
+    return state
+
+
+@log_node("map_fields")
+def map_fields(state: AgentState) -> Dict:
+    """阶段三：将占位字段名替换为 OCR 提取的真实字段名。"""
+    from langgraph.config import get_stream_writer
+
+    writer = get_stream_writer()
+    llm = get_llm(caller="map_fields")
+
+    ocr_result = state.get("ocr_extraction_result", {})
+    fields_text = ""
+    if isinstance(ocr_result, dict) and ocr_result.get("fields"):
+        field_descs = []
+        for f in ocr_result["fields"]:
+            fname = f.get("field_name", "")
+            fval = f.get("field_value", "")
+            if fname:
+                field_descs.append(f"  - {fname}: {fval}")
+        if field_descs:
+            fields_text = "提取的字段:\n" + "\n".join(field_descs)
+    if not fields_text:
+        elements = ocr_result.get("elements", []) if isinstance(ocr_result, dict) else []
+        if elements:
+            texts = [e.get("text", "") for e in elements if e.get("text")]
+            fields_text = "OCR 文本内容:\n" + "\n".join(f"  - {t}" for t in texts[:50])
+
+    prompt = load_prompt("field_mapping").format(
+        current_jrxml=state.get("current_jrxml", ""),
+        ocr_fields=fields_text,
+    )
+    full = []
+    for chunk in llm.stream(prompt):
+        full.append(chunk)
+        writer({"type": "stream", "node": "map_fields", "text": chunk})
+    jrxml = _extract_jrxml("".join(full))
+    state["current_jrxml"] = jrxml
+    state["conversation_history"].append({"role": "assistant", "content": jrxml})
+    return state
+
+
 @log_node("modify_jrxml")
 def modify_jrxml(state: AgentState) -> Dict:
     """根据用户的修改请求修改现有 JRXML。"""