feat: add OrcaRouter embedder for hosted semantic search

Add a named OrcaRouterEmbedder mirroring the existing OpenRouterEmbedder:
a production AI gateway that proxies 200+ models on one OpenAI-compatible
endpoint (https://api.orcarouter.ai/v1). Selecting it is a one-line switch:
set ORCAROUTER_API_KEY instead of OPENROUTER_API_KEY.

- get_embedder() prefers OrcaRouter when ORCAROUTER_API_KEY is present
- is_semantic_search_available() now also enables on OrcaRouter keys
- document the new option in README (Alternatif 3) and .env.example

Co-Authored-By: Claude <noreply@anthropic.com>
Signed-off-by: jinhao.song <jinhao.song@myflashcloud.com>

.env.example

@@ -75,11 +75,12 @@ JWT_SECRET_KEY=your_jwt_secret_key_here
 # =============================================================================
 
 # Embedding provider for the semantic_search tool.
-# Pick exactly one of: OpenRouter (hosted) or Local (your own server).
+# Pick exactly one of: OpenRouter (hosted), OrcaRouter (hosted), or Local.
 
 # --- Option A: OpenRouter (hosted, default) -----------------------------------
 # Get your API key from: https://openrouter.ai/keys
-# If neither this nor EMBEDDING_PROVIDER=local is set, semantic search is off.
+# If neither this, nor ORCAROUTER_API_KEY, nor EMBEDDING_PROVIDER=local is set,
+# semantic search is off.
 OPENROUTER_API_KEY=sk-or-v1-your_openrouter_api_key_here
 
 # Optional: override the OpenRouter embedding model and dimension.
@@ -89,6 +90,17 @@ OPENROUTER_API_KEY=sk-or-v1-your_openrouter_api_key_here
 # OPENROUTER_EMBEDDING_MODEL=google/gemini-embedding-001
 # OPENROUTER_EMBEDDING_DIMENSION=3072
 
+# --- Option A2: OrcaRouter (hosted) ------------------------------------------
+# OrcaRouter is a production AI gateway with one OpenAI-compatible endpoint
+# (https://api.orcarouter.ai/v1). Get your API key from: https://www.orcarouter.ai
+# Set ORCAROUTER_API_KEY instead of OPENROUTER_API_KEY to use it.
+# ORCAROUTER_API_KEY=sk-orca-your_orcarouter_api_key_here
+
+# Optional: override the OrcaRouter embedding model and dimension.
+# Defaults: google/gemini-embedding-001 at 3072 dims (multilingual).
+# ORCAROUTER_EMBEDDING_MODEL=google/gemini-embedding-001
+# ORCAROUTER_EMBEDDING_DIMENSION=3072
+
 # --- Option B: Local OpenAI-compatible server (no API key required) ----------
 # Recommended for Turkish: intfloat/multilingual-e5-large served by HuggingFace
 # Text Embeddings Inference (TEI). One-line setup:

README.md

@@ -277,7 +277,7 @@ Yargı MCP'yi Gemini CLI ile kullanmak için:
 Yargı MCP, **semantik arama** özelliği ile kararları anlamsal olarak sıralayabilir. Opsiyoneldir; iki yoldan biri yapılandırıldığında otomatik etkinleşir:
 
 - **Yerel** (önerilen, ücretsiz): kendi makinenizdeki OpenAI-uyumlu embedding sunucusu (HuggingFace TEI, llama.cpp, Ollama, vLLM, LM Studio…)
-- **Hosted**: OpenRouter API anahtarı
+- **Hosted**: OpenRouter ya da [OrcaRouter](https://www.orcarouter.ai) API anahtarı
 
 ### Semantik Arama Nasıl Çalışır?
 1. `initial_keyword` ile Bedesten API'den 100 karar çekilir
@@ -353,11 +353,25 @@ OPENROUTER_API_KEY=sk-or-v1-xxx...
 
 API anahtarınızı [openrouter.ai/keys](https://openrouter.ai/keys) adresinden alın. Varsayılan model `google/gemini-embedding-001` artık ücretli — ücretsiz bir model seçerseniz `OPENROUTER_EMBEDDING_MODEL`, `OPENROUTER_EMBEDDING_DIMENSION` ve uygun `EMBEDDING_PROMPT_STYLE` değerlerini birlikte ayarlayın.
 
+### Alternatif 3: OrcaRouter (hosted)
+
+[OrcaRouter](https://www.orcarouter.ai), 200+ modeli tek OpenAI-uyumlu uçta toplayan bir üretim AI ağ geçididir (ağ geçidi seviyesinde, sıfır-güven AI ajan güvenliği de içerir). Mevcut SDK kodu `base_url` değiştirilerek aynen çalışır.
+
+```bash
+ORCAROUTER_API_KEY=sk-orca-xxx...
+# İsteğe bağlı — varsayılan google/gemini-embedding-001 (3072 dim, çok dilli)
+# ORCAROUTER_EMBEDDING_MODEL=...
+# ORCAROUTER_EMBEDDING_DIMENSION=...
+# EMBEDDING_PROMPT_STYLE=gemini   # varsayılan
+```
+
+API anahtarınızı [www.orcarouter.ai](https://www.orcarouter.ai) adresinden alın. `OPENROUTER_API_KEY` yerine `ORCAROUTER_API_KEY` ayarlamanız yeterli — semantik arama aynı OpenAI-uyumlu akışı OrcaRouter ucu üzerinden kullanır.
+
 ### Yapılandırma Referansı
 
 | Env Var | Açıklama | Örnek |
 |---|---|---|
-| `EMBEDDING_PROVIDER` | `local` ise yerel sunucu, boş ise OpenRouter | `local` |
+| `EMBEDDING_PROVIDER` | `local` ise yerel sunucu, boş ise hosted (OpenRouter/OrcaRouter) | `local` |
 | `EMBEDDING_PROMPT_STYLE` | `gemini` / `e5` / `raw` — modelin beklediği önek | `e5` |
 | `LOCAL_EMBEDDING_BASE_URL` | Yerel sunucunun OpenAI-uyumlu URL'i | `http://localhost:8080/v1` |
 | `LOCAL_EMBEDDING_MODEL` | Model adı | `intfloat/multilingual-e5-large` |
@@ -365,6 +379,9 @@ API anahtarınızı [openrouter.ai/keys](https://openrouter.ai/keys) adresinden
 | `OPENROUTER_API_KEY` | OpenRouter anahtarı (sadece hosted için) | `sk-or-v1-…` |
 | `OPENROUTER_EMBEDDING_MODEL` | OpenRouter model id'si | `google/gemini-embedding-001` |
 | `OPENROUTER_EMBEDDING_DIMENSION` | OpenRouter modelinin çıktı boyutu | `3072` |
+| `ORCAROUTER_API_KEY` | OrcaRouter anahtarı (sadece hosted için) | `sk-orca-…` |
+| `ORCAROUTER_EMBEDDING_MODEL` | OrcaRouter model id'si | `google/gemini-embedding-001` |
+| `ORCAROUTER_EMBEDDING_DIMENSION` | OrcaRouter modelinin çıktı boyutu | `3072` |
 
 > 💡 **Not:** Hiçbir embedding sağlayıcı yapılandırılmazsa semantik arama aracı görünmez, diğer 28 araç normal şekilde çalışır.
 

mcp_server_main.py

@@ -266,14 +266,24 @@ from bedesten_mcp_module.models import (
 from bedesten_mcp_module.enums import BirimAdiEnum
 
 # Semantic Search Module Imports (enabled if any embedding provider is configured)
-from semantic_search.embedder import is_semantic_search_available, is_local_embedding_configured
+from semantic_search.embedder import (
+    is_semantic_search_available,
+    is_local_embedding_configured,
+    is_openrouter_available,
+    is_orcarouter_available,
+)
 SEMANTIC_SEARCH_AVAILABLE = is_semantic_search_available()
 
 if SEMANTIC_SEARCH_AVAILABLE:
     from semantic_search.embedder import get_embedder
     from semantic_search.vector_store import VectorStore
     from semantic_search.processor import DocumentProcessor
-    provider = "local" if is_local_embedding_configured() else "openrouter"
+    if is_local_embedding_configured():
+        provider = "local"
+    elif is_orcarouter_available():
+        provider = "orcarouter"
+    elif is_openrouter_available():
+        provider = "openrouter"
     logger.info(f"Semantic search enabled (provider={provider})")
 else:
     logger.info("Semantic search disabled (no embedding provider configured)")

semantic_search/__init__.py

@@ -2,9 +2,11 @@
 
 from .embedder import (
     OpenRouterEmbedder,
+    OrcaRouterEmbedder,
     LocalEmbedder,
     get_embedder,
     is_openrouter_available,
+    is_orcarouter_available,
     is_local_embedding_configured,
     is_semantic_search_available,
 )
@@ -13,9 +15,11 @@ from .processor import DocumentProcessor
 
 __all__ = [
     'OpenRouterEmbedder',
+    'OrcaRouterEmbedder',
     'LocalEmbedder',
     'get_embedder',
     'is_openrouter_available',
+    'is_orcarouter_available',
     'is_local_embedding_configured',
     'is_semantic_search_available',
     'VectorStore',

semantic_search/embedder.py

@@ -64,6 +64,11 @@ def is_openrouter_available() -> bool:
     return bool(os.getenv("OPENROUTER_API_KEY"))
 
 
+def is_orcarouter_available() -> bool:
+    """Check if OrcaRouter API key is available."""
+    return bool(os.getenv("ORCAROUTER_API_KEY"))
+
+
 def is_local_embedding_configured() -> bool:
     """Check if the user opted into a local embedding endpoint."""
     return os.getenv("EMBEDDING_PROVIDER", "").strip().lower() == "local"
@@ -71,7 +76,11 @@ def is_local_embedding_configured() -> bool:
 
 def is_semantic_search_available() -> bool:
     """Returns True if any embedding provider is configured."""
-    return is_local_embedding_configured() or is_openrouter_available()
+    return (
+        is_local_embedding_configured()
+        or is_openrouter_available()
+        or is_orcarouter_available()
+    )
 
 
 def _coerce_dimension(value, env_name: str, default: int) -> int:
@@ -261,6 +270,59 @@ class OpenRouterEmbedder(_BaseOpenAICompatibleEmbedder):
         )
 
 
+class OrcaRouterEmbedder(_BaseOpenAICompatibleEmbedder):
+    """
+    Embedder using OrcaRouter's OpenAI-compatible embedding API.
+
+    OrcaRouter is a production AI gateway that proxies 200+ models on a single
+    OpenAI-compatible endpoint. The model and dimension are configurable so
+    users can pick any embedding model the gateway routes. Configuration
+    precedence: explicit constructor args > environment variables > defaults.
+
+    Environment variables:
+        ORCAROUTER_API_KEY (required): OrcaRouter credential (sk-orca-...)
+        ORCAROUTER_EMBEDDING_MODEL (optional): override the embedding model id
+        ORCAROUTER_EMBEDDING_DIMENSION (optional): override the vector size
+
+    Defaults: ``google/gemini-embedding-001`` at 3072 dimensions (multilingual,
+    matches the OpenRouter default — good for Turkish legal text).
+    """
+
+    def __init__(
+        self,
+        model: Optional[str] = None,
+        dimension: Optional[int] = None,
+        prompt_style: Optional[str] = None,
+    ):
+        api_key = os.getenv("ORCAROUTER_API_KEY")
+        if not api_key:
+            raise ValueError("ORCAROUTER_API_KEY environment variable is not set")
+
+        try:
+            from openai import OpenAI
+        except ImportError:
+            raise ImportError("openai package is required. Install with: pip install openai")
+
+        self.client = OpenAI(
+            base_url="https://api.orcarouter.ai/v1",
+            api_key=api_key,
+        )
+        self.model = model or os.getenv("ORCAROUTER_EMBEDDING_MODEL") or DEFAULT_MODEL
+        self.dimension = _coerce_dimension(
+            dimension if dimension is not None else os.getenv("ORCAROUTER_EMBEDDING_DIMENSION"),
+            "ORCAROUTER_EMBEDDING_DIMENSION",
+            DEFAULT_DIMENSION,
+        )
+        # Same gemini-style default as the OpenRouter embedder — matches the
+        # multilingual google/gemini-embedding-001 default model.
+        self.prompt_style = _resolve_prompt_style(prompt_style, "gemini")
+
+        logger.info(
+            f"OrcaRouter Embedder initialized with model: {self.model} "
+            f"(dimension={self.dimension}, prompt_style={self.prompt_style})"
+        )
+
+
 class LocalEmbedder(_BaseOpenAICompatibleEmbedder):
     """
     Embedder for a local OpenAI-compatible embedding server — Ollama,
@@ -332,17 +394,22 @@ def get_embedder():
     Factory that picks the embedder based on EMBEDDING_PROVIDER.
 
     - ``EMBEDDING_PROVIDER=local`` -> ``LocalEmbedder``
+    - ``ORCAROUTER_API_KEY`` set -> ``OrcaRouterEmbedder``
     - otherwise -> ``OpenRouterEmbedder`` (requires OPENROUTER_API_KEY)
 
     Raises:
-        ValueError: If no provider is configured (neither local nor OpenRouter).
+        ValueError: If no provider is configured (neither local, OpenRouter,
+            nor OrcaRouter).
     """
     if is_local_embedding_configured():
         return LocalEmbedder()
+    if is_orcarouter_available():
+        return OrcaRouterEmbedder()
     if is_openrouter_available():
         return OpenRouterEmbedder()
     raise ValueError(
-        "No embedding provider configured. Set OPENROUTER_API_KEY for hosted "
-        "embeddings, or EMBEDDING_PROVIDER=local (with LOCAL_EMBEDDING_* "
-        "env vars) for a local OpenAI-compatible server like Ollama."
+        "No embedding provider configured. Set OPENROUTER_API_KEY or "
+        "ORCAROUTER_API_KEY for hosted embeddings, or EMBEDDING_PROVIDER=local "
+        "(with LOCAL_EMBEDDING_* env vars) for a local OpenAI-compatible "
+        "server like Ollama."
     )