feat: add AI Settings tab for managing providers and models

services/ai.py

@@ -1,113 +1,247 @@
+from __future__ import annotations
+
 import httpx
-from typing import List
-from services.config import ConfigCenter
+from typing import List, Sequence, Tuple
+
+from models.database import AIModel, AIProvider
+from services.ai_providers import AIProviderService
+
+
+provider_service = AIProviderService()
+
+
+class MissingModelError(RuntimeError):
+    pass
 
 
 async def describe_image_base64(base64_image: str, detail: str = "high") -> str:
     """
-    传入base64图片和文本提示，返回图片描述文本。
+    传入 base64 图片并返回描述文本。缺省时返回错误提示。
     """
-    OAI_API_URL = await ConfigCenter.get("AI_VISION_API_URL")
-    VISION_MODEL = await ConfigCenter.get("AI_VISION_MODEL")
-    API_KEY = await ConfigCenter.get("AI_VISION_API_KEY")
-    payload = {
-        "model": VISION_MODEL,
-        "messages": [
-            {"role": "user", "content": [
-                {
-                    "type": "image_url",
-                    "image_url": {
-                        "url": f"data:image/jpeg;base64,{base64_image}",
-                        "detail": detail
-                    }
-                },
-                {
-                    "type": "text",
-                    "text": "描述这个图片"
-                }
-            ]}
-        ]
-    }
-    headers = {
-        "Authorization": f"Bearer {API_KEY}",
-        "Content-Type": "application/json"
-    }
     try:
-        async with httpx.AsyncClient(timeout=60.0) as client:
-            resp = await client.post(OAI_API_URL, headers=headers, json=payload)
-            resp.raise_for_status()
-            result = resp.json()
-            return result["choices"][0]["message"]["content"]
+        model, provider = await _require_model("vision")
+        if provider.api_format == "openai":
+            return await _describe_with_openai(provider, model, base64_image, detail)
+        return await _describe_with_gemini(provider, model, base64_image, detail)
+    except MissingModelError as exc:
+        return str(exc)
     except httpx.ReadTimeout:
         return "请求超时，请稍后重试。"
-    except Exception as e:
-        return f"请求失败: {str(e)}"
+    except Exception as exc:  # noqa: BLE001
+        return f"请求失败: {exc}"
 
 
 async def get_text_embedding(text: str) -> List[float]:
     """
-    传入文本，返回嵌入向量。
+    传入文本，返回嵌入向量。若未配置模型则抛出异常。
     """
-    OAI_API_URL = await ConfigCenter.get("AI_EMBED_API_URL")
-    EMBED_MODEL = await ConfigCenter.get("AI_EMBED_MODEL")
-    API_KEY = await ConfigCenter.get("AI_EMBED_API_KEY")
-    payload = {
-        "model": EMBED_MODEL,
-        "input": text
-    }
-    headers = {
-        "Authorization": f"Bearer {API_KEY}",
-        "Content-Type": "application/json"
-    }
-    async with httpx.AsyncClient() as client:
-        if OAI_API_URL.endswith("chat/completions"):
-            url = OAI_API_URL.replace("chat/completions", "embeddings")
-        else:
-            url = OAI_API_URL
-        resp = await client.post(url, headers=headers, json=payload)
-        resp.raise_for_status()
-        result = resp.json()
-        return result["data"][0]["embedding"]
+    model, provider = await _require_model("embedding")
+    if provider.api_format == "openai":
+        return await _embedding_with_openai(provider, model, text)
+    return await _embedding_with_gemini(provider, model, text)
 
 
-async def rerank_texts(query: str, documents: List[str]) -> List[float]:
+async def rerank_texts(query: str, documents: Sequence[str]) -> List[float]:
     """调用重排序模型，为一组文档返回得分。未配置时返回空列表。"""
     if not documents:
         return []
-
-    api_url = await ConfigCenter.get("AI_RERANK_API_URL")
-    model = await ConfigCenter.get("AI_RERANK_MODEL")
-    api_key = await ConfigCenter.get("AI_RERANK_API_KEY")
-
-    if not api_url or not model or not api_key:
+    try:
+        model, provider = await _require_model("rerank")
+    except MissingModelError:
         return []
 
+    try:
+        if provider.api_format == "openai":
+            return await _rerank_with_openai(provider, model, query, documents)
+        return await _rerank_with_gemini(provider, model, query, documents)
+    except Exception:  # noqa: BLE001
+        return []
+
+
+async def _require_model(ability: str) -> Tuple[AIModel, AIProvider]:
+    model = await provider_service.get_default_model(ability)
+    if not model:
+        raise MissingModelError(f"未配置默认 {ability} 模型，请前往系统设置完成配置。")
+    provider = getattr(model, "provider", None)
+    if provider is None:
+        await model.fetch_related("provider")
+        provider = model.provider
+    if provider is None:
+        raise MissingModelError("模型缺少关联的提供商配置。")
+    if not provider.base_url:
+        raise MissingModelError("该提供商未设置 API 地址。")
+    return model, provider
+
+
+def _openai_endpoint(provider: AIProvider, path: str) -> str:
+    base = (provider.base_url or "").rstrip("/")
+    if not base:
+        raise MissingModelError("提供商 API 地址未配置。")
+    return f"{base}/{path.lstrip('/')}"
+
+
+def _openai_headers(provider: AIProvider) -> dict:
+    headers = {"Content-Type": "application/json"}
+    if provider.api_key:
+        headers["Authorization"] = f"Bearer {provider.api_key}"
+    return headers
+
+
+def _gemini_endpoint(provider: AIProvider, path: str) -> str:
+    base = (provider.base_url or "").rstrip("/")
+    if not base:
+        raise MissingModelError("提供商 API 地址未配置。")
+    url = f"{base}/{path.lstrip('/')}"
+    if provider.api_key:
+        connector = "&" if "?" in url else "?"
+        url = f"{url}{connector}key={provider.api_key}"
+    return url
+
+
+async def _describe_with_openai(provider: AIProvider, model: AIModel, base64_image: str, detail: str) -> str:
+    url = _openai_endpoint(provider, "/chat/completions")
     payload = {
-        "model": model,
-        "query": query,
-        "documents": documents,
-    }
-    headers = {
-        "Authorization": f"Bearer {api_key}",
-        "Content-Type": "application/json",
+        "model": model.name,
+        "messages": [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image_url",
+                        "image_url": {
+                            "url": f"data:image/jpeg;base64,{base64_image}",
+                            "detail": detail,
+                        },
+                    },
+                    {"type": "text", "text": "描述这个图片"},
+                ],
+            }
+        ],
     }
+    async with httpx.AsyncClient(timeout=60.0) as client:
+        response = await client.post(url, headers=_openai_headers(provider), json=payload)
+        response.raise_for_status()
+        body = response.json()
+        return body["choices"][0]["message"]["content"]
 
-    async with httpx.AsyncClient() as client:
+
+async def _describe_with_gemini(provider: AIProvider, model: AIModel, base64_image: str, detail: str) -> str:
+    detail_text = f"描述这个图片，细节等级：{detail}"
+    model_name = model.name if model.name.startswith("models/") else f"models/{model.name}"
+    url = _gemini_endpoint(provider, f"{model_name}:generateContent")
+    payload = {
+        "contents": [
+            {
+                "role": "user",
+                "parts": [
+                    {
+                        "inline_data": {
+                            "mime_type": "image/jpeg",
+                            "data": base64_image,
+                        }
+                    },
+                    {"text": detail_text},
+                ],
+            }
+        ]
+    }
+    async with httpx.AsyncClient(timeout=60.0) as client:
+        response = await client.post(url, json=payload)
+        response.raise_for_status()
+        body = response.json()
+        candidates = body.get("candidates") or []
+        if not candidates:
+            return ""
+        parts = candidates[0].get("content", {}).get("parts", [])
+        text_parts = [part.get("text") for part in parts if isinstance(part, dict) and part.get("text")]
+        return "\n".join(text_parts)
+
+
+async def _embedding_with_openai(provider: AIProvider, model: AIModel, text: str) -> List[float]:
+    url = _openai_endpoint(provider, "/embeddings")
+    payload = {
+        "model": model.name,
+        "input": text,
+    }
+    async with httpx.AsyncClient(timeout=30.0) as client:
+        response = await client.post(url, headers=_openai_headers(provider), json=payload)
+        response.raise_for_status()
+        body = response.json()
+        return body["data"][0]["embedding"]
+
+
+async def _embedding_with_gemini(provider: AIProvider, model: AIModel, text: str) -> List[float]:
+    model_name = model.name if model.name.startswith("models/") else f"models/{model.name}"
+    url = _gemini_endpoint(provider, f"{model_name}:embedContent")
+    payload = {
+        "model": model_name,
+        "content": {
+            "parts": [{"text": text}],
+        },
+    }
+    async with httpx.AsyncClient(timeout=30.0) as client:
+        response = await client.post(url, json=payload)
+        response.raise_for_status()
+        body = response.json()
+        embedding = body.get("embedding") or {}
+        return embedding.get("values") or []
+
+
+async def _rerank_with_openai(
+    provider: AIProvider,
+    model: AIModel,
+    query: str,
+    documents: Sequence[str],
+) -> List[float]:
+    url = _openai_endpoint(provider, "/rerank")
+    payload = {
+        "model": model.name,
+        "query": query,
+        "documents": [
+            {"id": str(idx), "text": content}
+            for idx, content in enumerate(documents)
+        ],
+    }
+    async with httpx.AsyncClient(timeout=30.0) as client:
+        response = await client.post(url, headers=_openai_headers(provider), json=payload)
+        response.raise_for_status()
+        body = response.json()
+        results = body.get("results") or body.get("data") or []
+        scores: List[float] = []
+        for item in results:
+            try:
+                scores.append(float(item.get("score", 0.0)))
+            except (TypeError, ValueError):
+                scores.append(0.0)
+        return scores
+
+
+async def _rerank_with_gemini(
+    provider: AIProvider,
+    model: AIModel,
+    query: str,
+    documents: Sequence[str],
+) -> List[float]:
+    model_name = model.name if model.name.startswith("models/") else f"models/{model.name}"
+    url = _gemini_endpoint(provider, f"{model_name}:rankContent")
+    payload = {
+        "query": {"text": query},
+        "documents": [
+            {"id": str(idx), "content": {"parts": [{"text": content}]}}
+            for idx, content in enumerate(documents)
+        ],
+    }
+    async with httpx.AsyncClient(timeout=30.0) as client:
+        response = await client.post(url, json=payload)
+        response.raise_for_status()
+        body = response.json()
+
+    scores: List[float] = []
+    ranked = body.get("rankedDocuments") or body.get("results") or []
+    for item in ranked:
+        raw_score = item.get("relevanceScore") or item.get("score") or item.get("confidenceScore")
         try:
-            resp = await client.post(api_url, headers=headers, json=payload)
-            resp.raise_for_status()
-        except httpx.HTTPStatusError:
-            return []
-        data = resp.json()
-        if isinstance(data, dict):
-            results = data.get("results")
-            if isinstance(results, list):
-                scores = []
-                for item in results:
-                    if isinstance(item, dict) and "score" in item:
-                        try:
-                            scores.append(float(item["score"]))
-                        except (TypeError, ValueError):
-                            scores.append(0.0)
-                return scores
-        return []
+            scores.append(float(raw_score))
+        except (TypeError, ValueError):
+            scores.append(0.0)
+    return scores

services/ai_providers.py

@@ -0,0 +1,347 @@
+from __future__ import annotations
+
+from collections.abc import Iterable
+from typing import Any, Dict, List, Optional, Tuple
+
+import httpx
+from tortoise.exceptions import DoesNotExist
+from tortoise.transactions import in_transaction
+
+from models.database import AIDefaultModel, AIModel, AIProvider
+
+
+ABILITIES = ["chat", "vision", "embedding", "rerank", "voice", "tools"]
+
+OPENAI_EMBEDDING_DIMS = {
+    "text-embedding-3-large": 3072,
+    "text-embedding-3-small": 1536,
+    "text-embedding-ada-002": 1536,
+}
+
+
+def _normalize_embedding_dim(value: Any) -> Optional[int]:
+    if value is None:
+        return None
+    try:
+        casted = int(value)
+    except (TypeError, ValueError):
+        return None
+    return casted if casted > 0 else None
+
+
+def _apply_embedding_dim_to_metadata(
+    data: Dict[str, Any],
+    embedding_dim: Optional[int],
+    base_metadata: Optional[Dict[str, Any]] = None,
+) -> Dict[str, Any]:
+    source = base_metadata if isinstance(base_metadata, dict) else {}
+    metadata: Dict[str, Any] = dict(source)
+    override = data.get("metadata")
+    if isinstance(override, dict) and override:
+        metadata.update(override)
+    if embedding_dim is None:
+        metadata.pop("embedding_dimensions", None)
+    else:
+        metadata["embedding_dimensions"] = embedding_dim
+    data["metadata"] = metadata or None
+    return data
+
+
+def normalize_capabilities(items: Optional[Iterable[str]]) -> List[str]:
+    if not items:
+        return []
+    normalized = []
+    for cap in items:
+        key = str(cap).strip().lower()
+        if key in ABILITIES and key not in normalized:
+            normalized.append(key)
+    return normalized
+
+
+def infer_openai_capabilities(model_id: str) -> Tuple[List[str], Optional[int]]:
+    lower = model_id.lower()
+    caps = set()
+
+    if any(keyword in lower for keyword in ["gpt", "chat", "turbo", "o1", "sonnet", "haiku", "thinking"]):
+        caps.update({"chat", "tools"})
+
+    if any(keyword in lower for keyword in ["vision", "gpt-4o", "gpt-4.1", "o1", "vision-preview", "omni"]):
+        caps.add("vision")
+
+    if any(keyword in lower for keyword in ["embed", "embedding"]):
+        caps.add("embedding")
+
+    if "rerank" in lower or "re-rank" in lower:
+        caps.add("rerank")
+
+    if any(keyword in lower for keyword in ["tts", "speech", "audio"]):
+        caps.add("voice")
+
+    embedding_dim = OPENAI_EMBEDDING_DIMS.get(model_id)
+    return normalize_capabilities(caps), embedding_dim
+
+
+def infer_gemini_capabilities(methods: Iterable[str]) -> List[str]:
+    caps = set()
+    for method in methods:
+        m = method.lower()
+        if m in {"generatecontent", "counttokens"}:
+            caps.update({"chat", "tools", "vision"})
+        if m == "embedcontent":
+            caps.add("embedding")
+        if m in {"generatespeech", "audiogeneration"}:
+            caps.add("voice")
+        if m == "rerank":
+            caps.add("rerank")
+    return normalize_capabilities(caps)
+
+
+def serialize_provider(provider: AIProvider) -> Dict[str, Any]:
+    return {
+        "id": provider.id,
+        "name": provider.name,
+        "identifier": provider.identifier,
+        "provider_type": provider.provider_type,
+        "api_format": provider.api_format,
+        "base_url": provider.base_url,
+        "api_key": provider.api_key,
+        "logo_url": provider.logo_url,
+        "extra_config": provider.extra_config or {},
+        "created_at": provider.created_at,
+        "updated_at": provider.updated_at,
+    }
+
+
+def model_to_dict(model: AIModel, provider: Optional[AIProvider] = None) -> Dict[str, Any]:
+    provider_obj = provider or getattr(model, "provider", None)
+    provider_data = serialize_provider(provider_obj) if provider_obj else None
+    return {
+        "id": model.id,
+        "provider_id": model.provider_id,
+        "name": model.name,
+        "display_name": model.display_name,
+        "description": model.description,
+        "capabilities": normalize_capabilities(model.capabilities),
+        "context_window": model.context_window,
+        "embedding_dimensions": model.embedding_dimensions,
+        "metadata": model.metadata or {},
+        "created_at": model.created_at,
+        "updated_at": model.updated_at,
+        "provider": provider_data,
+    }
+
+
+def provider_to_dict(provider: AIProvider, models: Optional[List[AIModel]] = None) -> Dict[str, Any]:
+    data = serialize_provider(provider)
+    if models is not None:
+        data["models"] = [model_to_dict(m, provider=provider) for m in models]
+    return data
+
+
+class AIProviderService:
+    async def list_providers(self) -> List[Dict[str, Any]]:
+        providers = await AIProvider.all().order_by("id").prefetch_related("models")
+        return [provider_to_dict(p, models=list(p.models)) for p in providers]
+
+    async def get_provider(self, provider_id: int, with_models: bool = False) -> Dict[str, Any]:
+        if with_models:
+            provider = await AIProvider.get(id=provider_id)
+            models = await provider.models.all()
+            return provider_to_dict(provider, models=models)
+        else:
+            provider = await AIProvider.get(id=provider_id)
+            return provider_to_dict(provider)
+
+    async def create_provider(self, payload: Dict[str, Any]) -> Dict[str, Any]:
+        data = payload.copy()
+        data.setdefault("extra_config", {})
+        provider = await AIProvider.create(**data)
+        return provider_to_dict(provider)
+
+    async def update_provider(self, provider_id: int, payload: Dict[str, Any]) -> Dict[str, Any]:
+        provider = await AIProvider.get(id=provider_id)
+        for field, value in payload.items():
+            setattr(provider, field, value)
+        await provider.save()
+        return provider_to_dict(provider)
+
+    async def delete_provider(self, provider_id: int) -> None:
+        await AIProvider.filter(id=provider_id).delete()
+
+    async def list_models(self, provider_id: int) -> List[Dict[str, Any]]:
+        models = await AIModel.filter(provider_id=provider_id).order_by("id").prefetch_related("provider")
+        return [model_to_dict(m) for m in models]
+
+    async def create_model(self, provider_id: int, payload: Dict[str, Any]) -> Dict[str, Any]:
+        data = payload.copy()
+        data["provider_id"] = provider_id
+        data["capabilities"] = normalize_capabilities(data.get("capabilities"))
+        embedding_dim = _normalize_embedding_dim(data.pop("embedding_dimensions", None))
+        data = _apply_embedding_dim_to_metadata(data, embedding_dim)
+        model = await AIModel.create(**data)
+        await model.fetch_related("provider")
+        return model_to_dict(model)
+
+    async def update_model(self, model_id: int, payload: Dict[str, Any]) -> Dict[str, Any]:
+        model = await AIModel.get(id=model_id)
+        data = payload.copy()
+        if "capabilities" in data:
+            data["capabilities"] = normalize_capabilities(data.get("capabilities"))
+        embedding_dim = None
+        if "embedding_dimensions" in data:
+            embedding_dim = _normalize_embedding_dim(data.pop("embedding_dimensions", None))
+            _apply_embedding_dim_to_metadata(data, embedding_dim, base_metadata=model.metadata)
+        for field, value in data.items():
+            setattr(model, field, value)
+        if embedding_dim is not None or ("embedding_dimensions" in payload and embedding_dim is None):
+            model.embedding_dimensions = embedding_dim
+        await model.save()
+        await model.fetch_related("provider")
+        return model_to_dict(model)
+
+    async def delete_model(self, model_id: int) -> None:
+        await AIModel.filter(id=model_id).delete()
+
+    async def fetch_remote_models(self, provider_id: int) -> List[Dict[str, Any]]:
+        provider = await AIProvider.get(id=provider_id)
+        return await self._get_remote_models(provider)
+
+    async def _get_remote_models(self, provider: AIProvider) -> List[Dict[str, Any]]:
+        if not provider.base_url:
+            raise ValueError("Provider base_url is required for syncing models")
+
+        fmt = (provider.api_format or "").lower()
+        if fmt not in {"openai", "gemini"}:
+            raise ValueError(f"Unsupported api_format '{provider.api_format}' for syncing models")
+
+        if fmt == "openai":
+            return await self._fetch_openai_models(provider)
+        return await self._fetch_gemini_models(provider)
+
+    async def sync_models(self, provider_id: int) -> Dict[str, int]:
+        provider = await AIProvider.get(id=provider_id)
+        remote_models = await self._get_remote_models(provider)
+
+        created = 0
+        updated = 0
+        for entry in remote_models:
+            defaults = entry.copy()
+            model_id = defaults.pop("name")
+            defaults["capabilities"] = normalize_capabilities(defaults.get("capabilities"))
+            embedding_dim = _normalize_embedding_dim(defaults.pop("embedding_dimensions", None))
+            defaults = _apply_embedding_dim_to_metadata(defaults, embedding_dim)
+            obj, is_created = await AIModel.get_or_create(
+                provider_id=provider.id,
+                name=model_id,
+                defaults=defaults,
+            )
+            if is_created:
+                created += 1
+                continue
+            for field, value in defaults.items():
+                setattr(obj, field, value)
+            if embedding_dim is not None or ("embedding_dimensions" in entry and embedding_dim is None):
+                obj.embedding_dimensions = embedding_dim
+            await obj.save()
+            updated += 1
+
+        return {"created": created, "updated": updated}
+
+    async def get_default_models(self) -> Dict[str, Optional[Dict[str, Any]]]:
+        defaults = await AIDefaultModel.all().prefetch_related("model__provider")
+        result: Dict[str, Optional[Dict[str, Any]]] = {ability: None for ability in ABILITIES}
+        for item in defaults:
+            result[item.ability] = model_to_dict(item.model, provider=item.model.provider)  # type: ignore[attr-defined]
+        return result
+
+    async def set_default_models(self, mapping: Dict[str, Optional[int]]) -> Dict[str, Optional[Dict[str, Any]]]:
+        normalized = {ability: mapping.get(ability) for ability in ABILITIES}
+        async with in_transaction() as connection:
+            for ability, model_id in normalized.items():
+                record = await AIDefaultModel.get_or_none(ability=ability)
+                if model_id:
+                    try:
+                        model = await AIModel.get(id=model_id)
+                    except DoesNotExist:
+                        raise ValueError(f"Model {model_id} not found")
+                    if record:
+                        record.model_id = model_id
+                        await record.save(using_db=connection)
+                    else:
+                        await AIDefaultModel.create(ability=ability, model_id=model_id)
+                elif record:
+                    await record.delete(using_db=connection)
+        return await self.get_default_models()
+
+    async def get_default_model(self, ability: str) -> Optional[AIModel]:
+        ability_key = ability.lower()
+        if ability_key not in ABILITIES:
+            return None
+        record = await AIDefaultModel.get_or_none(ability=ability_key)
+        if not record:
+            return None
+        model = await AIModel.get_or_none(id=record.model_id)
+        if model:
+            await model.fetch_related("provider")
+        return model
+
+    async def _fetch_openai_models(self, provider: AIProvider) -> List[Dict[str, Any]]:
+        base_url = provider.base_url.rstrip("/")
+        url = f"{base_url}/models"
+        headers = {}
+        if provider.api_key:
+            headers["Authorization"] = f"Bearer {provider.api_key}"
+
+        async with httpx.AsyncClient(timeout=30.0) as client:
+            response = await client.get(url, headers=headers)
+            response.raise_for_status()
+            payload = response.json()
+
+        data = payload.get("data", [])
+        entries: List[Dict[str, Any]] = []
+        for item in data:
+            model_id = item.get("id")
+            if not model_id:
+                continue
+            capabilities, embedding_dim = infer_openai_capabilities(model_id)
+            entries.append({
+                "name": model_id,
+                "display_name": item.get("display_name"),
+                "description": item.get("description"),
+                "capabilities": capabilities,
+                "context_window": item.get("context_window"),
+                "embedding_dimensions": embedding_dim,
+                "metadata": item,
+            })
+        return entries
+
+    async def _fetch_gemini_models(self, provider: AIProvider) -> List[Dict[str, Any]]:
+        base_url = provider.base_url.rstrip("/")
+        suffix = "/models"
+        if provider.api_key:
+            suffix += f"?key={provider.api_key}"
+        url = f"{base_url}{suffix}"
+
+        async with httpx.AsyncClient(timeout=30.0) as client:
+            response = await client.get(url)
+            response.raise_for_status()
+            payload = response.json()
+
+        data = payload.get("models", [])
+        entries: List[Dict[str, Any]] = []
+        for item in data:
+            model_id = item.get("name")
+            if not model_id:
+                continue
+            methods = item.get("supportedGenerationMethods") or []
+            capabilities = infer_gemini_capabilities(methods)
+            entries.append({
+                "name": model_id,
+                "display_name": item.get("displayName"),
+                "description": item.get("description"),
+                "capabilities": capabilities,
+                "context_window": item.get("inputTokenLimit"),
+                "embedding_dimensions": item.get("embeddingDimensions"),
+                "metadata": item,
+            })
+        return entries

services/processors/vector_index.py

@@ -5,15 +5,11 @@ import mimetypes
 import os
 from io import BytesIO
 
-from services.ai import describe_image_base64, get_text_embedding
+from services.ai import describe_image_base64, get_text_embedding, provider_service
 from services.vector_db import VectorDBService, DEFAULT_VECTOR_DIMENSION
 from services.logging import LogService
-from services.config import ConfigCenter
+from PIL import Image
 
-try:  # Pillow is optional but bundled with the project dependencies
-    from PIL import Image
-except ImportError:  # pragma: no cover - fallback when pillow missing
-    Image = None
 
 
 CHUNK_SIZE = 800
@@ -150,13 +146,15 @@ class VectorIndexProcessor:
         file_ext = path.split('.')[-1].lower()
         details: Dict[str, Any] = {"path": path, "action": "create", "index_type": "vector"}
 
-        raw_dim = await ConfigCenter.get('AI_EMBED_DIM', DEFAULT_VECTOR_DIMENSION)
-        try:
-            vector_dim = int(raw_dim)
-        except (TypeError, ValueError):
-            vector_dim = DEFAULT_VECTOR_DIMENSION
-        if vector_dim <= 0:
-            vector_dim = DEFAULT_VECTOR_DIMENSION
+        embedding_model = await provider_service.get_default_model("embedding")
+        vector_dim = DEFAULT_VECTOR_DIMENSION
+        if embedding_model and getattr(embedding_model, "embedding_dimensions", None):
+            try:
+                vector_dim = int(embedding_model.embedding_dimensions)
+            except (TypeError, ValueError):
+                vector_dim = DEFAULT_VECTOR_DIMENSION
+            if vector_dim <= 0:
+                vector_dim = DEFAULT_VECTOR_DIMENSION
 
         await vector_db.ensure_collection(collection_name, vector=True, dim=vector_dim)
         await vector_db.delete_vector(collection_name, path)