diff --git a/app/main.py b/app/main.py
index 03cbcb5..0d8fed0 100644
--- a/app/main.py
+++ b/app/main.py
@@ -57,7 +57,7 @@ def create_app() -> FastAPI:
         "/v1/import/analyze",
         response_model=DataImportAnalysisJobAck,
         summary="Schedule async import analysis and notify via callback",
-        status_code=200,
+        status_code=202,
     )
     async def analyze_import_data(
         payload: DataImportAnalysisJobRequest,
diff --git a/app/services/import_analysis.py b/app/services/import_analysis.py
index e249066..aa86c1f 100644
--- a/app/services/import_analysis.py
+++ b/app/services/import_analysis.py
@@ -1,6 +1,5 @@
 from __future__ import annotations
 
-import base64
 import csv
 import json
 import logging
@@ -11,21 +10,26 @@ from pathlib import Path
 from typing import Any, Dict, List, Sequence, Tuple
 
 import httpx
+from pydantic import ValidationError
 
 from app.exceptions import ProviderAPICallError
 from app.models import (
     DataImportAnalysisJobRequest,
     DataImportAnalysisRequest,
-    LLMChoice,
     LLMMessage,
     LLMProvider,
     LLMResponse,
     LLMRole,
 )
+from app.settings import DEFAULT_IMPORT_MODEL, get_supported_import_models
 
 logger = logging.getLogger(__name__)
 
-OPENAI_RESPONSES_URL = "https://api.openai.com/v1/responses"
+IMPORT_GATEWAY_BASE_URL = os.getenv(
+    "IMPORT_GATEWAY_BASE_URL", "http://localhost:8000"
+)
+
+SUPPORTED_IMPORT_MODELS = get_supported_import_models()
 
 
 def resolve_provider_from_model(llm_model: str) -> Tuple[LLMProvider, str]:
@@ -113,17 +117,32 @@ def load_import_template() -> str:
     return template_path.read_text(encoding="utf-8").strip()
 
 
-def derive_headers(rows: Sequence[Any], provided_headers: Sequence[str] | None) -> List[str]:
+def derive_headers(
+    rows: Sequence[Any], provided_headers: Sequence[str] | None
+) -> List[str]:
     if provided_headers:
-        return list(provided_headers)
+        return [str(header) for header in provided_headers]
+
+    collected: List[str] = []
+    list_lengths: List[int] = []
 
-    seen: List[str] = []
     for row in rows:
         if isinstance(row, dict):
             for key in row.keys():
-                if key not in seen:
-                    seen.append(str(key))
-    return seen
+                key_str = str(key)
+                if key_str not in collected:
+                    collected.append(key_str)
+        elif isinstance(row, (list, tuple)):
+            list_lengths.append(len(row))
+
+    if collected:
+        return collected
+
+    if list_lengths:
+        max_len = max(list_lengths)
+        return [f"column_{idx + 1}" for idx in range(max_len)]
+
+    return ["column_1"]
 
 
 def _stringify_cell(value: Any) -> str:
@@ -137,14 +156,18 @@ def _stringify_cell(value: Any) -> str:
         return str(value)
 
 
-def rows_to_csv_bytes(rows: Sequence[Any], headers: List[str]) -> bytes:
+def rows_to_csv_text(
+    rows: Sequence[Any], headers: Sequence[str], *, max_rows: int = 50
+) -> str:
     buffer = StringIO()
     writer = csv.writer(buffer)
 
     if headers:
         writer.writerow(headers)
 
-    for row in rows:
+    for idx, row in enumerate(rows):
+        if max_rows and idx >= max_rows:
+            break
         if isinstance(row, dict):
             writer.writerow([_stringify_cell(row.get(header)) for header in headers])
         elif isinstance(row, (list, tuple)):
@@ -152,192 +175,153 @@ def rows_to_csv_bytes(rows: Sequence[Any], headers: List[str]) -> bytes:
         else:
             writer.writerow([_stringify_cell(row)])
 
-    return buffer.getvalue().encode("utf-8")
+    return buffer.getvalue().strip()
 
 
-def build_file_part(filename: str, payload: bytes, mime_type: str) -> Dict[str, Any]:
-    encoded = base64.b64encode(payload).decode("ascii")
-    return {
-        "type": "input_file",
-        "input_file": {
-            "file_data": {
-                "filename": filename,
-                "file_name": filename,
-                "mime_type": mime_type,
-                "b64_json": encoded,
-            }
-        },
-    }
+def format_table_schema(schema: Any) -> str:
+    if schema is None:
+        return ""
+    if isinstance(schema, str):
+        return schema.strip()
+    try:
+        return json.dumps(schema, ensure_ascii=False, indent=2)
+    except (TypeError, ValueError):
+        return str(schema)
 
 
-def build_schema_part(
-    request: DataImportAnalysisJobRequest, headers: List[str]
-) -> Dict[str, Any] | None:
-    if request.table_schema is not None:
-        if isinstance(request.table_schema, str):
-            schema_bytes = request.table_schema.encode("utf-8")
-            return build_file_part("table_schema.txt", schema_bytes, "text/plain")
-
-        try:
-            schema_serialised = json.dumps(
-                request.table_schema, ensure_ascii=False, indent=2
-            )
-        except (TypeError, ValueError) as exc:
-            logger.warning("Failed to serialise table_schema for %s: %s", request.import_record_id, exc)
-            schema_serialised = str(request.table_schema)
-
-        return build_file_part(
-            "table_schema.json",
-            schema_serialised.encode("utf-8"),
-            "application/json",
-        )
-
-    if headers:
-        headers_payload = json.dumps({"headers": headers}, ensure_ascii=False, indent=2)
-        return build_file_part(
-            "table_headers.json",
-            headers_payload.encode("utf-8"),
-            "application/json",
-        )
-
-    return None
-
-
-def build_openai_input_payload(
+def build_analysis_request(
     request: DataImportAnalysisJobRequest,
-) -> Dict[str, Any]:
+) -> DataImportAnalysisRequest:
     headers = derive_headers(request.rows, request.headers)
-    csv_bytes = rows_to_csv_bytes(request.rows, headers)
-    csv_part = build_file_part("sample_rows.csv", csv_bytes, "text/csv")
-    schema_part = build_schema_part(request, headers)
 
-    prompt = load_import_template()
-
-    context_lines = [
-        f"导入记录ID: {request.import_record_id}",
-        f"样本数据行数: {len(request.rows)}",
-        "请参考附件 `sample_rows.csv` 获取原始样本数据。",
-    ]
-
-    if schema_part:
-        context_lines.append("附加结构信息来自第二个附件，请结合使用。")
+    if request.raw_csv:
+        csv_text = request.raw_csv.strip()
     else:
-        context_lines.append("未提供表头或Schema，请依据数据自行推断字段信息。")
+        csv_text = rows_to_csv_text(request.rows, headers)
 
-    user_content = [
-        {"type": "input_text", "text": "\n".join(context_lines)},
-        csv_part,
-    ]
+    sections: List[str] = []
+    if csv_text:
+        sections.append("CSV样本预览:\n" + csv_text)
+    schema_text = format_table_schema(request.table_schema)
+    if schema_text:
+        sections.append("附加结构信息:\n" + schema_text)
 
-    if schema_part:
-        user_content.append(schema_part)
+    example_data = "\n\n".join(sections) if sections else "未提供样本数据。"
 
-    payload: Dict[str, Any] = {
-        "model": request.llm_model,
-        "input": [
-            {"role": "system", "content": [{"type": "input_text", "text": prompt}]},
-            {"role": "user", "content": user_content},
-        ],
-    }
+    max_length = 30_000
+    if len(example_data) > max_length:
+        example_data = example_data[: max_length - 3] + "..."
 
-    if request.temperature is not None:
-        payload["temperature"] = request.temperature
-    if request.max_output_tokens is not None:
-        payload["max_output_tokens"] = request.max_output_tokens
-
-    return payload
-
-
-def parse_openai_responses_payload(
-    data: Dict[str, Any], fallback_model: str
-) -> LLMResponse:
-    output_blocks = data.get("output", [])
-    choices: List[LLMChoice] = []
-
-    for idx, block in enumerate(output_blocks):
-        if block.get("type") != "message":
-            continue
-        content_items = block.get("content", [])
-        text_fragments: List[str] = []
-        for item in content_items:
-            if item.get("type") == "output_text":
-                text_fragments.append(item.get("text", ""))
-
-        if not text_fragments and data.get("output_text"):
-            text_fragments.append(data.get("output_text", ""))
-
-        message = LLMMessage(role=LLMRole.ASSISTANT, content="\n".join(text_fragments))
-        choices.append(LLMChoice(index=idx, message=message))
-
-    if not choices and data.get("output_text"):
-        message = LLMMessage(role=LLMRole.ASSISTANT, content=data.get("output_text", ""))
-        choices.append(LLMChoice(index=0, message=message))
-
-    return LLMResponse(
-        provider=LLMProvider.OPENAI,
-        model=data.get("model", fallback_model),
-        choices=choices,
-        raw=data,
+    return DataImportAnalysisRequest(
+        import_record_id=request.import_record_id,
+        example_data=example_data,
+        table_headers=headers,
+        llm_model=request.llm_model or DEFAULT_IMPORT_MODEL,
     )
 
 
-async def call_openai_import_analysis(
-    request: DataImportAnalysisJobRequest,
-    client: httpx.AsyncClient,
-    *,
-    api_key: str | None = None,
-) -> LLMResponse:
-    openai_api_key = api_key or os.getenv("OPENAI_API_KEY")
-    if not openai_api_key:
-        raise ProviderAPICallError("OPENAI_API_KEY must be set to process import analysis.")
+def build_chat_payload(request: DataImportAnalysisJobRequest) -> Dict[str, Any]:
+    llm_input = request.llm_model or DEFAULT_IMPORT_MODEL
+    provider, model_name = resolve_provider_from_model(llm_input)
+    normalized_model = f"{provider.value}:{model_name}"
 
-    payload = build_openai_input_payload(request)
-    headers = {
-        "Authorization": f"Bearer {openai_api_key}",
-        "Content-Type": "application/json",
+    if SUPPORTED_IMPORT_MODELS and normalized_model not in SUPPORTED_IMPORT_MODELS:
+        raise ProviderAPICallError(
+            "Model '{model}' is not allowed. Allowed models: {allowed}".format(
+                model=normalized_model,
+                allowed=", ".join(sorted(SUPPORTED_IMPORT_MODELS)),
+            )
+        )
+
+    analysis_request = build_analysis_request(request)
+
+    messages = build_import_messages(analysis_request)
+
+    payload: Dict[str, Any] = {
+        "provider": provider.value,
+        "model": model_name,
+        "messages": [message.model_dump() for message in messages],
+        "temperature": request.temperature if request.temperature is not None else 0.2,
     }
 
-    response = await client.post(OPENAI_RESPONSES_URL, json=payload, headers=headers)
-    try:
-        response.raise_for_status()
-    except httpx.HTTPError as exc:
-        raise ProviderAPICallError(f"OpenAI response API call failed: {exc}") from exc
+    if request.max_output_tokens is not None:
+        payload["max_tokens"] = request.max_output_tokens
 
-    data: Dict[str, Any] = response.json()
-    return parse_openai_responses_payload(data, request.llm_model)
+    return payload
 
 
 async def dispatch_import_analysis_job(
     request: DataImportAnalysisJobRequest,
     client: httpx.AsyncClient,
-    *,
-    api_key: str | None = None,
 ) -> Dict[str, Any]:
     logger.info("Starting import analysis job %s", request.import_record_id)
-    llm_response = await call_openai_import_analysis(request, client, api_key=api_key)
 
-    result = {
+    payload = build_chat_payload(request)
+    url = f"{IMPORT_GATEWAY_BASE_URL.rstrip('/')}/v1/chat/completions"
+
+    print(
+        f"[ImportAnalysis] Dispatching import {request.import_record_id} to {url}: "
+        f"{json.dumps(payload, ensure_ascii=False)}"
+    )
+
+    try:
+        response = await client.post(url, json=payload)
+        response.raise_for_status()
+    except httpx.HTTPStatusError as exc:
+        body_preview = ""
+        if exc.response is not None:
+            body_preview = exc.response.text[:400]
+        raise ProviderAPICallError(
+            f"Failed to invoke chat completions endpoint: {exc}. Response body: {body_preview}"
+        ) from exc
+    except httpx.HTTPError as exc:
+        raise ProviderAPICallError(
+            f"HTTP call to chat completions endpoint failed: {exc}"
+        ) from exc
+
+    try:
+        response_data = response.json()
+    except ValueError as exc:
+        raise ProviderAPICallError("Chat completions endpoint returned invalid JSON") from exc
+
+    print(
+        f"[ImportAnalysis] LLM HTTP status for {request.import_record_id}: "
+        f"{response.status_code}"
+    )
+    print(
+        f"[ImportAnalysis] LLM response for {request.import_record_id}: "
+        f"{json.dumps(response_data, ensure_ascii=False)}"
+    )
+
+    try:
+        llm_response = LLMResponse.model_validate(response_data)
+    except ValidationError as exc:
+        raise ProviderAPICallError(
+            "Chat completions endpoint returned unexpected schema"
+        ) from exc
+
+    logger.info("Completed import analysis job %s", request.import_record_id)
+    return {
         "import_record_id": request.import_record_id,
         "status": "succeeded",
         "llm_response": llm_response.model_dump(),
     }
 
-    logger.info("Completed import analysis job %s", request.import_record_id)
-    return result
-
 
 async def notify_import_analysis_callback(
     callback_url: str,
     payload: Dict[str, Any],
     client: httpx.AsyncClient,
 ) -> None:
+    callback_target = str(callback_url)
+
     try:
-        response = await client.post(callback_url, json=payload)
+        response = await client.post(callback_target, json=payload)
         response.raise_for_status()
     except httpx.HTTPError as exc:
         logger.error(
             "Failed to deliver import analysis callback to %s: %s",
-            callback_url,
+            callback_target,
             exc,
         )
 
@@ -345,16 +329,13 @@ async def notify_import_analysis_callback(
 async def process_import_analysis_job(
     request: DataImportAnalysisJobRequest,
     client: httpx.AsyncClient,
-    *,
-    api_key: str | None = None,
 ) -> None:
     try:
-        payload = await dispatch_import_analysis_job(
-            request,
-            client,
-            api_key=api_key,
-        )
+        payload = await dispatch_import_analysis_job(request, client)
     except ProviderAPICallError as exc:
+        print(
+            f"[ImportAnalysis] LLM call failed for {request.import_record_id}: {exc}"
+        )
         payload = {
             "import_record_id": request.import_record_id,
             "status": "failed",
@@ -365,6 +346,9 @@ async def process_import_analysis_job(
             "Unexpected failure while processing import analysis job %s",
             request.import_record_id,
         )
+        print(
+            f"[ImportAnalysis] Unexpected error for {request.import_record_id}: {exc}"
+        )
         payload = {
             "import_record_id": request.import_record_id,
             "status": "failed",