Resolve 9508 openai compatible rerank

api/core/model_runtime/model_providers/openai_api_compatible/openai_api_compatible.yaml

@@ -8,6 +8,7 @@ supported_model_types:
   - llm
   - text-embedding
   - speech2text
+  - rerank
 configurate_methods:
   - customizable-model
 model_credential_schema:
@@ -83,6 +84,19 @@ model_credential_schema:
       placeholder:
         zh_Hans: 在此输入您的模型上下文长度
         en_US: Enter your Model context size
+    - variable: context_size
+      label:
+        zh_Hans: 模型上下文长度
+        en_US: Model context size
+      required: true
+      show_on:
+        - variable: __model_type
+          value: rerank
+      type: text-input
+      default: '4096'
+      placeholder:
+        zh_Hans: 在此输入您的模型上下文长度
+        en_US: Enter your Model context size
     - variable: max_tokens_to_sample
       label:
         zh_Hans: 最大 token 上限

api/core/model_runtime/model_providers/openai_api_compatible/rerank/rerank.py

@@ -0,0 +1,159 @@
+from json import dumps
+from typing import Optional
+
+import httpx
+from requests import post
+from yarl import URL
+
+from core.model_runtime.entities.common_entities import I18nObject
+from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelType
+from core.model_runtime.entities.rerank_entities import RerankDocument, RerankResult
+from core.model_runtime.errors.invoke import (
+    InvokeAuthorizationError,
+    InvokeBadRequestError,
+    InvokeConnectionError,
+    InvokeError,
+    InvokeRateLimitError,
+    InvokeServerUnavailableError,
+)
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.rerank_model import RerankModel
+
+
+class OAICompatRerankModel(RerankModel):
+    """
+    rerank model API is compatible with Jina rerank model API. So copy the JinaRerankModel class code here.
+    we need enhance for llama.cpp , which return raw score, not normalize score 0~1.  It seems Dify need it
+    """
+
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        query: str,
+        docs: list[str],
+        score_threshold: Optional[float] = None,
+        top_n: Optional[int] = None,
+        user: Optional[str] = None,
+    ) -> RerankResult:
+        """
+        Invoke rerank model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param query: search query
+        :param docs: docs for reranking
+        :param score_threshold: score threshold
+        :param top_n: top n documents to return
+        :param user: unique user id
+        :return: rerank result
+        """
+        if len(docs) == 0:
+            return RerankResult(model=model, docs=[])
+
+        server_url = credentials["endpoint_url"]
+        model_name = model
+
+        if not server_url:
+            raise CredentialsValidateFailedError("server_url is required")
+        if not model_name:
+            raise CredentialsValidateFailedError("model_name is required")
+
+        url = server_url
+        headers = {"Authorization": f"Bearer {credentials.get('api_key')}", "Content-Type": "application/json"}
+
+        # TODO: Do we need truncate docs to avoid llama.cpp return error?
+
+        data = {"model": model_name, "query": query, "documents": docs, "top_n": top_n}
+
+        try:
+            response = post(str(URL(url) / "rerank"), headers=headers, data=dumps(data), timeout=60)
+            response.raise_for_status()
+            results = response.json()
+
+            rerank_documents = []
+            scores = [result["relevance_score"] for result in results["results"]]
+
+            # Min-Max Normalization: Normalize scores to 0 ~ 1.0 range
+            min_score = min(scores)
+            max_score = max(scores)
+            score_range = max_score - min_score if max_score != min_score else 1.0  # Avoid division by zero
+
+            for result in results["results"]:
+                index = result["index"]
+
+                # Retrieve document text (fallback if llama.cpp rerank doesn't return it)
+                text = result.get("document", {}).get("text", docs[index])
+
+                # Normalize the score
+                normalized_score = (result["relevance_score"] - min_score) / score_range
+
+                # Create RerankDocument object with normalized score
+                rerank_document = RerankDocument(
+                    index=index,
+                    text=text,
+                    score=normalized_score,
+                )
+
+                # Apply threshold (if defined)
+                if score_threshold is None or normalized_score >= score_threshold:
+                    rerank_documents.append(rerank_document)
+
+            # Sort rerank_documents by normalized score in descending order
+            rerank_documents.sort(key=lambda doc: doc.score, reverse=True)
+
+            return RerankResult(model=model, docs=rerank_documents)
+
+        except httpx.HTTPStatusError as e:
+            raise InvokeServerUnavailableError(str(e))
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        """
+        Validate model credentials
+
+        :param model: model name
+        :param credentials: model credentials
+        :return:
+        """
+        try:
+            self._invoke(
+                model=model,
+                credentials=credentials,
+                query="What is the capital of the United States?",
+                docs=[
+                    "Carson City is the capital city of the American state of Nevada. At the 2010 United States "
+                    "Census, Carson City had a population of 55,274.",
+                    "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean that "
+                    "are a political division controlled by the United States. Its capital is Saipan.",
+                ],
+                score_threshold=0.8,
+            )
+        except Exception as ex:
+            raise CredentialsValidateFailedError(str(ex))
+
+    @property
+    def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
+        """
+        Map model invoke error to unified error
+        """
+        return {
+            InvokeConnectionError: [httpx.ConnectError],
+            InvokeServerUnavailableError: [httpx.RemoteProtocolError],
+            InvokeRateLimitError: [],
+            InvokeAuthorizationError: [httpx.HTTPStatusError],
+            InvokeBadRequestError: [httpx.RequestError],
+        }
+
+    def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity:
+        """
+        generate custom model entities from credentials
+        """
+        entity = AIModelEntity(
+            model=model,
+            label=I18nObject(en_US=model),
+            model_type=ModelType.RERANK,
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_properties={},
+        )
+
+        return entity