diff --git a/docs/issue.md b/docs/issue.md
index 66fd61f..6da58f6 100644
--- a/docs/issue.md
+++ b/docs/issue.md
@@ -132,3 +132,57 @@ AttributeError: 'ZhipuVendor' object has no attribute 'name'
 
 - 已 `grep -rn "vendor\.name\b" src/` 全仓扫描，确认 `target_vendor.name | vendor.name` 误用仅 routes.py 的这两处，已随本次修复一并消除。`/v1/messages` 主链路在 executor 中调用 `tier.name`（`Tier` 对象的合法 dataclass 属性），与 vendor 实例 `name` 无关，不受影响。
 - 若未来新增 Vendor 子类，仍只需实现 `get_name()` 抽象方法；外部调用方应遵循同一契约，本档案的修复模式可作为参考。
+
+---
+
+## Gemini embedding 透传至 Vertex AI 上游返回 `request body doesn't contain valid prompts`
+
+**问题描述**
+
+通过本代理调用 Gemini embedding 模型时，上游返回 400：
+
+```
+litellm.BadRequestError: GeminiException BadRequestError -
+{"error":{"message":"request body doesn't contain valid prompts"}}
+POST /api/gemini/v1beta/models/gemini-embedding-001%3AbatchEmbedContents 400
+```
+
+litellm 报错日志中 URL 路径是 `:batchEmbedContents`，调用端疑似格式不兼容。
+
+**表因**
+
+litellm 按 Google AI Studio 格式构造请求：
+- 路径：`POST {api_base}/v1beta/models/{model}:batchEmbedContents`
+- Body：`{"requests": [{"model": "models/...", "content": {"parts": [{"text": "..."}]}}]}`
+
+但实际上游（如 `llms.as-in.io` 这类 Vertex AI 风格网关）只接受 Vertex AI 格式：
+- 路径：`POST {api_base}/v1beta1/publishers/google/models/{model}:embedContent`
+- Body：`{"content": {"parts": [{"text": "..."}]}}`
+
+且无 `batchEmbedContents` 端点。
+
+**根因**
+
+1. 代理 `NativeProxyHandler.dispatch()` 是字节级透传，对 embedding 端点未做协议适配，直接把 Google AI Studio 格式的 URL/Body 转给 Vertex AI 上游，路由不匹配。
+2. litellm `_check_custom_proxy()` 在自定义 `api_base` 场景下会丢失 `v1beta/` 版本前缀，发送 `{api_base}/models/{model}:verb`，使代理原有的 `OperationClassifier` 正则（要求 `v1beta/` 前缀）失配，进而走原始透传分支再次失败。
+
+**处理方式**
+
+1. `src/coding/proxy/native_api/operation.py`：放宽 Gemini 路径正则中的 `v1(?:beta1?)?/` 段为可选，兼容 litellm 丢失版本前缀的异常路径。
+2. `src/coding/proxy/native_api/handler.py`：在 `dispatch()` 中新增 Gemini embedding Vertex AI 适配分支：
+   - 仅当 `provider == "gemini"`、`operation in {"embedding", "embedding.batch"}`、且 `base_url` 非官方 `generativelanguage.googleapis.com` 时启用；
+   - `embedContent` → 重写路径为 `v1beta1/publishers/google/models/{model}:embedContent`，剥离 body 中的 `model` 字段；
+   - `batchEmbedContents` → 拆分为多次并发 `embedContent` 调用（`asyncio.gather`），聚合响应为 `{"embeddings": [...]}` 返回；
+   - 用量抽取累加各子请求的 `usageMetadata`。
+3. `tests/test_native_api_handler.py`：新增 3 个回归测试覆盖单次 / 批量 / 官方上游透传不变三类场景。
+
+**后续防范**
+
+- 协议适配层只对**非官方上游**生效，官方 `generativelanguage.googleapis.com` 仍走字节级透传，避免引入不必要的转换开销与协议偏差。
+- 上游路径分支的判定优先用 base_url 域名而非依赖网关行为特征，便于后续扩展（如 Vertex Express、其他 LLM gateway）时的精确匹配。
+- 真实链路验证：使用 litellm `embedding(api_base=..., api_key=...)` 单输入 / 多输入分别调用，确认返回 3072 维向量及正确批量计数。
+
+**同类问题影响与处理注意事项**
+
+- litellm 在 Gemini 其他端点（`generateContent` / `countTokens`）同样存在 `_check_custom_proxy` 丢失 `v1beta/` 前缀的 bug；本次仅放宽了 `operation.py` 中的路径正则（让分类器能识别此类异常路径），未对这些端点做格式转换，因为非 embedding 端点的 Google AI Studio / Vertex AI 请求体差异较小，多数上游兼容。如未来出现类似失配再做针对性适配。
+- 若上游网关同时支持 OpenAI `/v1/embeddings` 与 Vertex AI 路径，建议优先在客户端配置 OpenAI 兼容路径，减少协议转换链路。
diff --git a/src/coding/proxy/native_api/handler.py b/src/coding/proxy/native_api/handler.py
index d468f3b..ab7b344 100644
--- a/src/coding/proxy/native_api/handler.py
+++ b/src/coding/proxy/native_api/handler.py
@@ -13,8 +13,10 @@
 
 from __future__ import annotations
 
+import asyncio
 import json
 import logging
+import re
 import time
 from collections.abc import AsyncIterator
 from typing import TYPE_CHECKING
@@ -194,6 +196,28 @@ async def dispatch(
         start_ts = time.perf_counter()
         client = self._get_client(provider)
 
+        # ── Gemini embedding Vertex AI 格式转换 ──────────────────
+        # 当上游非官方 Google AI Studio（generativelanguage.googleapis.com）时，
+        # litellm 发送的 Google AI Studio 格式（v1beta/models/{model}:batchEmbedContents）
+        # 需转换为 Vertex AI 格式（v1beta1/publishers/google/models/{model}:embedContent）。
+        vertex_rewrite = (
+            provider == "gemini"
+            and operation in ("embedding", "embedding.batch")
+            and cfg.base_url
+            and "generativelanguage.googleapis.com" not in cfg.base_url
+        )
+        if vertex_rewrite:
+            return await self._dispatch_gemini_vertex_embedding(
+                client=client,
+                operation=operation,
+                endpoint=endpoint,
+                body_bytes=body_bytes,
+                upstream_headers=upstream_headers,
+                query_string=query_string,
+                provider=provider,
+                start_ts=start_ts,
+            )
+
         # 构造上游 URL（保留 query）
         upstream_url = endpoint
         if query_string:
@@ -295,6 +319,313 @@ async def dispatch(
             media_type=content_type or None,
         )
 
+    # ── Gemini embedding → Vertex AI 格式转换 ──────────────────
+
+    # Google AI Studio 路径正则：[v1beta/]models/{model}:{verb}
+    # 版本段允许缺失以兼容 litellm `_check_custom_proxy` 丢失 v1beta 前缀的 bug。
+    _GEMINI_EMBED_PATH_RE = re.compile(
+        r"^/?(?:v1(?:beta1?)?/)?models/(?P<model>[^/:]+)(?::|%3A)(?P<verb>embedContent|batchEmbedContents)/?$"
+    )
+
+    async def _dispatch_gemini_vertex_embedding(
+        self,
+        *,
+        client: httpx.AsyncClient,
+        operation: str,
+        endpoint: str,
+        body_bytes: bytes,
+        upstream_headers: dict[str, str],
+        query_string: str,
+        provider: str,
+        start_ts: float,
+    ) -> StarletteResponse:
+        """将 Google AI Studio 格式的 embedding 请求转换为 Vertex AI 格式.
+
+        Google AI Studio:
+          POST v1beta/models/{model}:batchEmbedContents
+          Body: {"requests": [{"model": "models/{model}", "content": {...}}]}
+
+        Vertex AI:
+          POST v1beta1/publishers/google/models/{model}:embedContent
+          Body: {"content": {...}}
+        """
+        from fastapi.responses import Response as FastAPIResponse
+
+        match = self._GEMINI_EMBED_PATH_RE.match(endpoint)
+        if not match:
+            return FastAPIResponse(
+                content=json.dumps(
+                    {
+                        "error": {
+                            "message": f"unrecognized gemini embedding path: {endpoint}"
+                        }
+                    }
+                ).encode(),
+                status_code=400,
+                media_type="application/json",
+            )
+
+        model_name = match.group("model")
+        verb = match.group("verb")
+
+        # 解析原始请求体
+        try:
+            body = json.loads(body_bytes) if body_bytes else {}
+        except (json.JSONDecodeError, UnicodeDecodeError):
+            return FastAPIResponse(
+                content=json.dumps(
+                    {"error": {"message": "invalid JSON body for embedding request"}}
+                ).encode(),
+                status_code=400,
+                media_type="application/json",
+            )
+
+        if verb == "batchEmbedContents":
+            return await self._vertex_batch_embed(
+                client=client,
+                model_name=model_name,
+                body=body,
+                upstream_headers=upstream_headers,
+                query_string=query_string,
+                provider=provider,
+                operation=operation,
+                endpoint=endpoint,
+                start_ts=start_ts,
+            )
+
+        # 单次 embedContent：直接转换
+        content = body.get("content", body)
+        return await self._vertex_single_embed(
+            client=client,
+            model_name=model_name,
+            content=content,
+            upstream_headers=upstream_headers,
+            query_string=query_string,
+            provider=provider,
+            operation=operation,
+            endpoint=endpoint,
+            start_ts=start_ts,
+        )
+
+    async def _vertex_single_embed(
+        self,
+        *,
+        client: httpx.AsyncClient,
+        model_name: str,
+        content: dict,
+        upstream_headers: dict[str, str],
+        query_string: str,
+        provider: str,
+        operation: str,
+        endpoint: str,
+        start_ts: float,
+    ) -> StarletteResponse:
+        """发送单次 Vertex AI embedContent 请求."""
+        from fastapi.responses import Response as FastAPIResponse
+
+        vertex_path = f"/v1beta1/publishers/google/models/{model_name}:embedContent"
+        vertex_url = vertex_path
+        if query_string:
+            vertex_url = f"{vertex_path}?{query_string}"
+
+        vertex_body = json.dumps({"content": content}).encode()
+
+        req = client.build_request(
+            method="POST",
+            url=vertex_url,
+            content=vertex_body,
+            headers=upstream_headers,
+        )
+
+        try:
+            upstream_resp = await client.send(req, stream=True)
+        except (
+            httpx.TimeoutException,
+            httpx.ConnectError,
+            httpx.ReadError,
+            httpx.RemoteProtocolError,
+        ) as exc:
+            duration_ms = int((time.perf_counter() - start_ts) * 1000)
+            await self._record_failure(
+                provider=provider,
+                operation=operation,
+                endpoint=endpoint,
+                duration_ms=duration_ms,
+                reason=str(exc),
+            )
+            return FastAPIResponse(
+                content=json.dumps(
+                    {
+                        "error": {
+                            "message": f"upstream unreachable: {exc}",
+                            "type": "api_error",
+                        }
+                    }
+                ).encode(),
+                status_code=502,
+                media_type="application/json",
+            )
+
+        try:
+            raw_body = await upstream_resp.aread()
+        finally:
+            await upstream_resp.aclose()
+
+        duration_ms = int((time.perf_counter() - start_ts) * 1000)
+        status = upstream_resp.status_code
+        content_type = upstream_resp.headers.get("content-type", "").lower()
+        resp_headers = _filter_response_headers(dict(upstream_resp.headers))
+
+        # 用量抽取
+        extraction = ExtractionResult()
+        if "application/json" in content_type and raw_body:
+            try:
+                parsed = json.loads(raw_body.decode("utf-8", errors="replace"))
+                if isinstance(parsed, dict):
+                    extraction = extract_usage(
+                        provider, operation, parsed, status, dict(upstream_resp.headers)
+                    )
+            except (json.JSONDecodeError, UnicodeDecodeError):
+                pass
+
+        vendor_label = _VENDOR_LABEL[provider]
+        await self._record_usage(
+            provider=provider,
+            operation=operation,
+            endpoint=endpoint,
+            duration_ms=duration_ms,
+            status=status,
+            extraction=extraction,
+            evidence_records=_build_nonstream_evidence(
+                vendor=vendor_label, extraction=extraction
+            ),
+        )
+
+        return FastAPIResponse(
+            content=raw_body,
+            status_code=status,
+            headers=resp_headers,
+            media_type=content_type or None,
+        )
+
+    async def _vertex_batch_embed(
+        self,
+        *,
+        client: httpx.AsyncClient,
+        model_name: str,
+        body: dict,
+        upstream_headers: dict[str, str],
+        query_string: str,
+        provider: str,
+        operation: str,
+        endpoint: str,
+        start_ts: float,
+    ) -> StarletteResponse:
+        """将 batchEmbedContents 拆分为多次 embedContent 调用并聚合响应."""
+        from fastapi.responses import Response as FastAPIResponse
+
+        requests_list = body.get("requests", [])
+        if not requests_list:
+            return FastAPIResponse(
+                content=json.dumps(
+                    {
+                        "error": {
+                            "message": "batchEmbedContents requires non-empty 'requests' field"
+                        }
+                    }
+                ).encode(),
+                status_code=400,
+                media_type="application/json",
+            )
+
+        vertex_path = f"/v1beta1/publishers/google/models/{model_name}:embedContent"
+        vertex_url = vertex_path
+        if query_string:
+            vertex_url = f"{vertex_path}?{query_string}"
+
+        # 并发发送所有 embedContent 请求
+        async def _single(req_body: dict) -> tuple[dict, int]:
+            content = req_body.get("content", req_body)
+            vertex_body = json.dumps({"content": content}).encode()
+            req = client.build_request(
+                method="POST",
+                url=vertex_url,
+                content=vertex_body,
+                headers=upstream_headers,
+            )
+            try:
+                resp = await client.send(req, stream=False)
+            except (
+                httpx.TimeoutException,
+                httpx.ConnectError,
+                httpx.ReadError,
+                httpx.RemoteProtocolError,
+            ) as exc:
+                return {"error": {"message": f"upstream unreachable: {exc}"}}, 502
+            try:
+                return resp.json(), resp.status_code
+            except Exception:
+                return {"error": {"message": resp.text[:200]}}, resp.status_code
+
+        results = await asyncio.gather(*[_single(r) for r in requests_list])
+
+        # 检查是否有失败的请求
+        embeddings = []
+        for resp_json, resp_status in results:
+            if resp_status != 200:
+                # 返回第一个错误
+                return FastAPIResponse(
+                    content=json.dumps(resp_json).encode(),
+                    status_code=resp_status,
+                    media_type="application/json",
+                )
+            embedding_data = resp_json.get("embedding", {})
+            embeddings.append(embedding_data)
+
+        # 聚合为 batchEmbedContents 响应格式
+        batch_response = {"embeddings": embeddings}
+        duration_ms = int((time.perf_counter() - start_ts) * 1000)
+
+        # 用量抽取
+        extraction = ExtractionResult()
+        for resp_json, _ in results:
+            if isinstance(resp_json, dict):
+                ext = extract_usage(provider, operation, resp_json, 200, {})
+                extraction = ExtractionResult(
+                    input_tokens=extraction.input_tokens + ext.input_tokens,
+                    output_tokens=extraction.output_tokens + ext.output_tokens,
+                    cache_creation_tokens=extraction.cache_creation_tokens
+                    + ext.cache_creation_tokens,
+                    cache_read_tokens=extraction.cache_read_tokens
+                    + ext.cache_read_tokens,
+                    request_id=ext.request_id or extraction.request_id,
+                    model_served=ext.model_served or extraction.model_served,
+                    raw_usage=ext.raw_usage or extraction.raw_usage,
+                    source_field_map=ext.source_field_map
+                    or extraction.source_field_map,
+                    evidence_kind=ext.evidence_kind or extraction.evidence_kind,
+                    extra_usage=ext.extra_usage or extraction.extra_usage,
+                )
+
+        vendor_label = _VENDOR_LABEL[provider]
+        await self._record_usage(
+            provider=provider,
+            operation=operation,
+            endpoint=endpoint,
+            duration_ms=duration_ms,
+            status=200,
+            extraction=extraction,
+            evidence_records=_build_nonstream_evidence(
+                vendor=vendor_label, extraction=extraction
+            ),
+        )
+
+        return FastAPIResponse(
+            content=json.dumps(batch_response).encode(),
+            status_code=200,
+            media_type="application/json",
+        )
+
     # ── SSE 流式转发（同时累加 usage） ─────────────────────────
 
     async def _stream_and_accumulate(
diff --git a/src/coding/proxy/native_api/operation.py b/src/coding/proxy/native_api/operation.py
index 1ff82bd..2080b6c 100644
--- a/src/coding/proxy/native_api/operation.py
+++ b/src/coding/proxy/native_api/operation.py
@@ -48,30 +48,34 @@ class _Rule:
 )
 
 # ── Gemini ────────────────────────────────────────────────────────
-# Gemini 的方法动词作为路径后缀（``:generateContent``），通过正则提取
+# Gemini 的方法动词作为路径后缀（``:generateContent``），通过正则提取。
+# ``v1(?:beta1?)?/`` 前缀允许缺失，以兼容 litellm `_check_custom_proxy` 在
+# 自定义 ``api_base`` 场景下丢失版本段的 bug（参考 litellm issue #17759）。
 _GEMINI_RULES: tuple[_Rule, ...] = (
     _Rule(
-        re.compile(r"^/?v1(?:beta)?/models/[^/]+(?:%3A|:)streamGenerateContent/?$"),
+        re.compile(
+            r"^/?(?:v1(?:beta1?)?/)?models/[^/]+(?:%3A|:)streamGenerateContent/?$"
+        ),
         "generate_content",
     ),
     _Rule(
-        re.compile(r"^/?v1(?:beta)?/models/[^/]+(?:%3A|:)generateContent/?$"),
+        re.compile(r"^/?(?:v1(?:beta1?)?/)?models/[^/]+(?:%3A|:)generateContent/?$"),
         "generate_content",
     ),
     _Rule(
-        re.compile(r"^/?v1(?:beta)?/models/[^/]+(?:%3A|:)countTokens/?$"),
+        re.compile(r"^/?(?:v1(?:beta1?)?/)?models/[^/]+(?:%3A|:)countTokens/?$"),
         "count_tokens",
     ),
     _Rule(
-        re.compile(r"^/?v1(?:beta)?/models/[^/]+(?:%3A|:)embedContent/?$"),
+        re.compile(r"^/?(?:v1(?:beta1?)?/)?models/[^/]+(?:%3A|:)embedContent/?$"),
         "embedding",
     ),
     _Rule(
-        re.compile(r"^/?v1(?:beta)?/models/[^/]+(?:%3A|:)batchEmbedContents/?$"),
+        re.compile(r"^/?(?:v1(?:beta1?)?/)?models/[^/]+(?:%3A|:)batchEmbedContents/?$"),
         "embedding.batch",
     ),
     _Rule(
-        re.compile(r"^/?v1(?:beta)?/models/[^/]+(?:%3A|:)predict/?$"),
+        re.compile(r"^/?(?:v1(?:beta1?)?/)?models/[^/]+(?:%3A|:)predict/?$"),
         "predict",
     ),
     _Rule(
diff --git a/tests/test_native_api_handler.py b/tests/test_native_api_handler.py
index d00a054..14be66c 100644
--- a/tests/test_native_api_handler.py
+++ b/tests/test_native_api_handler.py
@@ -14,6 +14,7 @@
 
 from __future__ import annotations
 
+import json
 from collections.abc import Iterator
 
 import httpx
@@ -443,3 +444,120 @@ def factory(make_transport):
         # 上游 URL 必须含字面冒号，不含 %3A
         assert "%3A" not in upstream_str
         assert ":batchEmbedContents" in upstream_str
+
+
+# ── Gemini embedding Vertex AI 格式转换 ─────────────────────────
+
+
+def test_gemini_vertex_embed_content_single() -> None:
+    """非官方上游时，embedContent 转为 Vertex AI 格式."""
+
+    def route(request: httpx.Request) -> httpx.Response:
+        body = json.loads(request.content)
+        assert "content" in body
+        assert "model" not in body
+        assert "requests" not in body
+        assert ":embedContent" in str(request.url)
+        assert "v1beta1/publishers/google/models" in str(request.url)
+        return httpx.Response(200, json={"embedding": {"values": [0.1, 0.2]}})
+
+    def factory(make_transport):
+        cfg = NativeApiConfig(
+            gemini=NativeProviderConfig(enabled=True, base_url="http://llms.as-in.io"),
+        )
+        transport = make_transport(route)
+        return NativeProxyHandler(cfg, transport=transport), transport
+
+    for client, captured in _make_app(factory):
+        r = client.post(
+            "/api/gemini/v1beta/models/gemini-embedding-2-preview:embedContent",
+            json={
+                "model": "models/gemini-embedding-2-preview",
+                "content": {"parts": [{"text": "hello"}]},
+            },
+        )
+        assert r.status_code == 200
+        assert "embedding" in r.json()
+
+
+def test_gemini_vertex_batch_embed_contents() -> None:
+    """非官方上游时，batchEmbedContents 拆分为多次 embedContent 并聚合."""
+
+    call_count = 0
+
+    def route(request: httpx.Request) -> httpx.Response:
+        nonlocal call_count
+        call_count += 1
+        body = json.loads(request.content)
+        assert "content" in body
+        assert ":embedContent" in str(request.url)
+        assert "v1beta1/publishers/google/models" in str(request.url)
+        return httpx.Response(
+            200,
+            json={"embedding": {"values": [float(call_count), 0.5]}},
+        )
+
+    def factory(make_transport):
+        cfg = NativeApiConfig(
+            gemini=NativeProviderConfig(enabled=True, base_url="http://llms.as-in.io"),
+        )
+        transport = make_transport(route)
+        return NativeProxyHandler(cfg, transport=transport), transport
+
+    for client, captured in _make_app(factory):
+        r = client.post(
+            "/api/gemini/v1beta/models/gemini-embedding-2-preview:batchEmbedContents",
+            json={
+                "requests": [
+                    {
+                        "model": "models/gemini-embedding-2-preview",
+                        "content": {"parts": [{"text": "hello"}]},
+                    },
+                    {
+                        "model": "models/gemini-embedding-2-preview",
+                        "content": {"parts": [{"text": "world"}]},
+                    },
+                ]
+            },
+        )
+        assert r.status_code == 200
+        data = r.json()
+        assert "embeddings" in data
+        assert len(data["embeddings"]) == 2
+        assert data["embeddings"][0]["values"] == [1.0, 0.5]
+        assert data["embeddings"][1]["values"] == [2.0, 0.5]
+        assert call_count == 2
+
+
+def test_gemini_vertex_embed_official_upstream_unchanged() -> None:
+    """官方上游时，batchEmbedContents 走原始透传路径，不做格式转换."""
+
+    def route(request: httpx.Request) -> httpx.Response:
+        return httpx.Response(200, json={"embeddings": [{"values": [0.1, 0.2]}]})
+
+    def factory(make_transport):
+        cfg = NativeApiConfig(
+            gemini=NativeProviderConfig(
+                enabled=True, base_url="https://generativelanguage.googleapis.com"
+            ),
+        )
+        transport = make_transport(route)
+        return NativeProxyHandler(cfg, transport=transport), transport
+
+    for client, captured in _make_app(factory):
+        r = client.post(
+            "/api/gemini/v1beta/models/gemini-embedding-001:batchEmbedContents?key=k",
+            json={
+                "requests": [
+                    {
+                        "model": "models/gemini-embedding-001",
+                        "content": {"parts": [{"text": "hello"}]},
+                    }
+                ]
+            },
+        )
+        assert r.status_code == 200
+        # 官方上游走原始路径，URL 保持 v1beta/models/ 格式
+        upstream = captured[0]
+        assert "v1beta/models" in str(upstream.url)
+        assert "v1beta1/publishers" not in str(upstream.url)