feat: 支持xinference Rerank模型

apps/setting/models_provider/impl/xinference_model_provider/credential/reranker.py

@@ -0,0 +1,47 @@
+# coding=utf-8
+"""
+    @project: MaxKB
+    @Author：虎
+    @file： reranker.py
+    @date：2024/9/10 9:46
+    @desc:
+"""
+from typing import Dict
+
+from langchain_core.documents import Document
+
+from common import forms
+from common.exception.app_exception import AppApiException
+from common.forms import BaseForm
+from setting.models_provider.base_model_provider import BaseModelCredential, ValidCode
+
+
+class XInferenceRerankerModelCredential(BaseForm, BaseModelCredential):
+    def is_valid(self, model_type: str, model_name, model_credential: Dict[str, object], provider,
+                 raise_exception=True):
+        if not model_type == 'RERANKER':
+            raise AppApiException(ValidCode.valid_error.value, f'{model_type} 模型类型不支持')
+        for key in ['server_url']:
+            if key not in model_credential:
+                if raise_exception:
+                    raise AppApiException(ValidCode.valid_error.value, f'{key} 字段为必填字段')
+                else:
+                    return False
+        try:
+            model = provider.get_model(model_type, model_name, model_credential)
+            model.compress_documents([Document(page_content='你好')], '你好')
+        except Exception as e:
+            if isinstance(e, AppApiException):
+                raise e
+            if raise_exception:
+                raise AppApiException(ValidCode.valid_error.value, f'校验失败,请检查参数是否正确: {str(e)}')
+            else:
+                return False
+        return True
+
+    def encryption_dict(self, model_info: Dict[str, object]):
+        return model_info
+
+    server_url = forms.TextInputField('API 域名', required=True)
+
+    api_key = forms.PasswordInputField('API Key', required=False)

apps/setting/models_provider/impl/xinference_model_provider/model/reranker.py

@@ -0,0 +1,73 @@
+# coding=utf-8
+"""
+    @project: MaxKB
+    @Author：虎
+    @file： reranker.py
+    @date：2024/9/10 9:45
+    @desc:
+"""
+from typing import Sequence, Optional, Any, Dict
+
+from langchain_core.callbacks import Callbacks
+from langchain_core.documents import BaseDocumentCompressor, Document
+from xinference_client.client.restful.restful_client import RESTfulRerankModelHandle
+
+from setting.models_provider.base_model_provider import MaxKBBaseModel
+
+
+class XInferenceReranker(MaxKBBaseModel, BaseDocumentCompressor):
+    client: Any
+    server_url: Optional[str]
+    """URL of the xinference server"""
+    model_uid: Optional[str]
+    """UID of the launched model"""
+    api_key: Optional[str]
+
+    @staticmethod
+    def new_instance(model_type, model_name, model_credential: Dict[str, object], **model_kwargs):
+        return XInferenceReranker(server_url=model_credential.get('server_url'), model_uid=model_name,
+                                  api_key=model_credential.get('api_key'))
+
+    top_n: Optional[int] = 3
+
+    def __init__(
+            self, server_url: Optional[str] = None, model_uid: Optional[str] = None, top_n=3,
+            api_key: Optional[str] = None
+    ):
+        try:
+            from xinference.client import RESTfulClient
+        except ImportError:
+            try:
+                from xinference_client import RESTfulClient
+            except ImportError as e:
+                raise ImportError(
+                    "Could not import RESTfulClient from xinference. Please install it"
+                    " with `pip install xinference` or `pip install xinference_client`."
+                ) from e
+
+        super().__init__()
+
+        if server_url is None:
+            raise ValueError("Please provide server URL")
+
+        if model_uid is None:
+            raise ValueError("Please provide the model UID")
+
+        self.server_url = server_url
+
+        self.model_uid = model_uid
+
+        self.api_key = api_key
+
+        self.client = RESTfulClient(server_url, api_key)
+
+        self.top_n = top_n
+
+    def compress_documents(self, documents: Sequence[Document], query: str, callbacks: Optional[Callbacks] = None) -> \
+            Sequence[Document]:
+        if documents is None or len(documents) == 0:
+            return []
+        model: RESTfulRerankModelHandle = self.client.get_model(self.model_uid)
+        res = model.rerank([document.page_content for document in documents], query, self.top_n, return_documents=True)
+        return [Document(page_content=d.get('document', {}).get('text'),
+                         metadata={'relevance_score': d.get('relevance_score')}) for d in res.get('results', [])]

apps/setting/models_provider/impl/xinference_model_provider/xinference_model_provider.py

@@ -10,8 +10,10 @@
 from setting.models_provider.impl.xinference_model_provider.credential.embedding import \
     XinferenceEmbeddingModelCredential
 from setting.models_provider.impl.xinference_model_provider.credential.llm import XinferenceLLMModelCredential
+from setting.models_provider.impl.xinference_model_provider.credential.reranker import XInferenceRerankerModelCredential
 from setting.models_provider.impl.xinference_model_provider.model.embedding import XinferenceEmbedding
 from setting.models_provider.impl.xinference_model_provider.model.llm import XinferenceChatModel
+from setting.models_provider.impl.xinference_model_provider.model.reranker import XInferenceReranker
 from smartdoc.conf import PROJECT_DIR
 
 xinference_llm_model_credential = XinferenceLLMModelCredential()
@@ -480,7 +482,9 @@
     ModelInfo('text2vec-large-chinese', 'Text2Vec 的中文大型版本嵌入模型。', ModelTypeConst.EMBEDDING,
               xinference_embedding_model_credential, XinferenceEmbedding),
 ]
-
+rerank_list = [ModelInfo('bce-reranker-base_v1',
+                         '发布新的重新排名器，建立在强大的 M3 和LLM （GEMMA 和 MiniCPM，实际上没那么大）骨干上，支持多语言处理和更大的输入，大幅提高 BEIR、C-MTEB/Retrieval 的排名性能、MIRACL、LlamaIndex 评估',
+                         ModelTypeConst.RERANKER, XInferenceRerankerModelCredential(), XInferenceReranker)]
 model_info_manage = (ModelInfoManage.builder().append_model_info_list(model_info_list).append_default_model_info(
     ModelInfo(
         'phi3',
@@ -492,6 +496,7 @@
         '',
         '',
         ModelTypeConst.EMBEDDING, xinference_embedding_model_credential, XinferenceEmbedding))
+                     .append_model_info_list(rerank_list).append_default_model_info(rerank_list[0])
                      .build())