第一次提交：阿龙电竞 Django 后端最新版本

venv_prod/lib/python3.12/site-packages/tencentcloud/es/v20250101/errorcodes.py

@@ -0,0 +1,45 @@
+# -*- coding: utf8 -*-
+# Copyright (c) 2017-2025 Tencent. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+# 内部错误。
+INTERNALERROR = 'InternalError'
+
+# 参数错误。
+INVALIDPARAMETER = 'InvalidParameter'
+
+# 参数取值错误。
+INVALIDPARAMETERVALUE = 'InvalidParameterValue'
+
+# 超过配额限制。
+LIMITEXCEEDED = 'LimitExceeded'
+
+# 缺少参数错误。
+MISSINGPARAMETER = 'MissingParameter'
+
+# 请求的次数超过了频率限制。
+REQUESTLIMITEXCEEDED = 'RequestLimitExceeded'
+
+# 资源不存在。
+RESOURCENOTFOUND = 'ResourceNotFound'
+
+# 未授权操作。
+UNAUTHORIZEDOPERATION = 'UnauthorizedOperation'
+
+# 未知参数错误。
+UNKNOWNPARAMETER = 'UnknownParameter'
+
+# 操作不支持。
+UNSUPPORTEDOPERATION = 'UnsupportedOperation'

venv_prod/lib/python3.12/site-packages/tencentcloud/es/v20250101/es_client.py

@@ -0,0 +1,256 @@
+# -*- coding: utf8 -*-
+# Copyright (c) 2017-2025 Tencent. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+
+from tencentcloud.common.exception.tencent_cloud_sdk_exception import TencentCloudSDKException
+from tencentcloud.common.abstract_client import AbstractClient
+from tencentcloud.es.v20250101 import models
+
+
+class EsClient(AbstractClient):
+    _apiVersion = '2025-01-01'
+    _endpoint = 'es.tencentcloudapi.com'
+    _service = 'es'
+
+
+    def ChatCompletions(self, request):
+        r"""本服务支持一系列高性能的大语言模型，包括DeepSeek以及腾讯自主研发的混元大模型，结合混合搜索等先进搜索技术，快速高效实现RAG，有效解决幻觉和知识更新问题。
+        本接口有模型维度调用上限控制，单个模型qps限制5，如您有提高并发限制的需求请[联系我们](https://cloud.tencent.com/act/event/Online_service)  。
+
+        :param request: Request instance for ChatCompletions.
+        :type request: :class:`tencentcloud.es.v20250101.models.ChatCompletionsRequest`
+        :rtype: :class:`tencentcloud.es.v20250101.models.ChatCompletionsResponse`
+
+        """
+        try:
+            params = request._serialize()
+            options = {"Endpoint": "%s://es.ai.tencentcloudapi.com" % self.profile.httpProfile.scheme}
+            return self._call_and_deserialize("ChatCompletions", params, models.ChatCompletionsResponse, headers=request.headers, options=options)
+        except Exception as e:
+            if isinstance(e, TencentCloudSDKException):
+                raise
+            else:
+                raise TencentCloudSDKException(type(e).__name__, str(e))
+
+
+    def ChunkDocument(self, request):
+        r"""文本切片是将长文本分割为短片段的技术，用于适配模型输入、提升处理效率或信息检索，平衡片段长度与语义连贯性，适用于NLP、数据分析等场景。
+        本接口为分隔符规则切片接口，有单账号调用上限控制，如您有提高并发限制的需求请 [联系我们](https://cloud.tencent.com/act/event/Online_service)  。
+
+        :param request: Request instance for ChunkDocument.
+        :type request: :class:`tencentcloud.es.v20250101.models.ChunkDocumentRequest`
+        :rtype: :class:`tencentcloud.es.v20250101.models.ChunkDocumentResponse`
+
+        """
+        try:
+            params = request._serialize()
+            headers = request.headers
+            body = self.call("ChunkDocument", params, headers=headers)
+            response = json.loads(body)
+            model = models.ChunkDocumentResponse()
+            model._deserialize(response["Response"])
+            return model
+        except Exception as e:
+            if isinstance(e, TencentCloudSDKException):
+                raise
+            else:
+                raise TencentCloudSDKException(type(e).__name__, str(e))
+
+
+    def ChunkDocumentAsync(self, request):
+        r"""文本切片是将长文本分割为短片段的技术，用于适配模型输入、提升处理效率或信息检索，平衡片段长度与语义连贯性，适用于NLP、数据分析等场景。
+        本接口为异步接口，有模型维度调用上限控制，单个模型qps限制5，如您有提高并发限制的需求请[联系我们](https://cloud.tencent.com/act/event/Online_service) 。
+
+        :param request: Request instance for ChunkDocumentAsync.
+        :type request: :class:`tencentcloud.es.v20250101.models.ChunkDocumentAsyncRequest`
+        :rtype: :class:`tencentcloud.es.v20250101.models.ChunkDocumentAsyncResponse`
+
+        """
+        try:
+            params = request._serialize()
+            headers = request.headers
+            body = self.call("ChunkDocumentAsync", params, headers=headers)
+            response = json.loads(body)
+            model = models.ChunkDocumentAsyncResponse()
+            model._deserialize(response["Response"])
+            return model
+        except Exception as e:
+            if isinstance(e, TencentCloudSDKException):
+                raise
+            else:
+                raise TencentCloudSDKException(type(e).__name__, str(e))
+
+
+    def GetDocumentChunkResult(self, request):
+        r"""获取文档切片结果
+
+        :param request: Request instance for GetDocumentChunkResult.
+        :type request: :class:`tencentcloud.es.v20250101.models.GetDocumentChunkResultRequest`
+        :rtype: :class:`tencentcloud.es.v20250101.models.GetDocumentChunkResultResponse`
+
+        """
+        try:
+            params = request._serialize()
+            headers = request.headers
+            body = self.call("GetDocumentChunkResult", params, headers=headers)
+            response = json.loads(body)
+            model = models.GetDocumentChunkResultResponse()
+            model._deserialize(response["Response"])
+            return model
+        except Exception as e:
+            if isinstance(e, TencentCloudSDKException):
+                raise
+            else:
+                raise TencentCloudSDKException(type(e).__name__, str(e))
+
+
+    def GetDocumentParseResult(self, request):
+        r"""本接口用于获取文档解析异步处理结果。
+
+        :param request: Request instance for GetDocumentParseResult.
+        :type request: :class:`tencentcloud.es.v20250101.models.GetDocumentParseResultRequest`
+        :rtype: :class:`tencentcloud.es.v20250101.models.GetDocumentParseResultResponse`
+
+        """
+        try:
+            params = request._serialize()
+            headers = request.headers
+            body = self.call("GetDocumentParseResult", params, headers=headers)
+            response = json.loads(body)
+            model = models.GetDocumentParseResultResponse()
+            model._deserialize(response["Response"])
+            return model
+        except Exception as e:
+            if isinstance(e, TencentCloudSDKException):
+                raise
+            else:
+                raise TencentCloudSDKException(type(e).__name__, str(e))
+
+
+    def GetMultiModalEmbedding(self, request):
+        r"""Embedding是一种将高维数据映射到低维空间的技术，通常用于将非结构化数据，如文本、图像或音频转化为向量表示，使其更容易输入机器模型进行处理，并且向量之间的距离可以反映对象之间的相似性。
+        本接口有模型维度调用上限控制，单个模型qps限制10，如您有提高并发限制的需求请[联系我们](https://cloud.tencent.com/act/event/Online_service)  。
+
+        :param request: Request instance for GetMultiModalEmbedding.
+        :type request: :class:`tencentcloud.es.v20250101.models.GetMultiModalEmbeddingRequest`
+        :rtype: :class:`tencentcloud.es.v20250101.models.GetMultiModalEmbeddingResponse`
+
+        """
+        try:
+            params = request._serialize()
+            headers = request.headers
+            body = self.call("GetMultiModalEmbedding", params, headers=headers)
+            response = json.loads(body)
+            model = models.GetMultiModalEmbeddingResponse()
+            model._deserialize(response["Response"])
+            return model
+        except Exception as e:
+            if isinstance(e, TencentCloudSDKException):
+                raise
+            else:
+                raise TencentCloudSDKException(type(e).__name__, str(e))
+
+
+    def GetTextEmbedding(self, request):
+        r"""Embedding是一种将高维数据映射到低维空间的技术，通常用于将非结构化数据，如文本、图像或音频转化为向量表示，使其更容易输入机器模型进行处理，并且向量之间的距离可以反映对象之间的相似性。
+        本接口有模型维度调用上限控制，单个模型qps限制20，如您有提高并发限制的需求请[联系我们](https://cloud.tencent.com/act/event/Online_service)  。
+
+        :param request: Request instance for GetTextEmbedding.
+        :type request: :class:`tencentcloud.es.v20250101.models.GetTextEmbeddingRequest`
+        :rtype: :class:`tencentcloud.es.v20250101.models.GetTextEmbeddingResponse`
+
+        """
+        try:
+            params = request._serialize()
+            headers = request.headers
+            body = self.call("GetTextEmbedding", params, headers=headers)
+            response = json.loads(body)
+            model = models.GetTextEmbeddingResponse()
+            model._deserialize(response["Response"])
+            return model
+        except Exception as e:
+            if isinstance(e, TencentCloudSDKException):
+                raise
+            else:
+                raise TencentCloudSDKException(type(e).__name__, str(e))
+
+
+    def ParseDocument(self, request):
+        r"""本服务可将各类格式文档精准转换为标准格式，满足企业知识库建设、技术文档迁移、内容平台结构化存储等需求。
+        本接口有模型维度调用上限控制，单个模型qps限制5，如您有提高并发限制的需求请[联系我们](https://cloud.tencent.com/act/event/Online_service) 。
+
+        :param request: Request instance for ParseDocument.
+        :type request: :class:`tencentcloud.es.v20250101.models.ParseDocumentRequest`
+        :rtype: :class:`tencentcloud.es.v20250101.models.ParseDocumentResponse`
+
+        """
+        try:
+            params = request._serialize()
+            options = {"Endpoint": "%s://es.ai.tencentcloudapi.com" % self.profile.httpProfile.scheme}
+            return self._call_and_deserialize("ParseDocument", params, models.ParseDocumentResponse, headers=request.headers, options=options)
+        except Exception as e:
+            if isinstance(e, TencentCloudSDKException):
+                raise
+            else:
+                raise TencentCloudSDKException(type(e).__name__, str(e))
+
+
+    def ParseDocumentAsync(self, request):
+        r"""本服务可将各类格式文档精准转换为标准格式，满足企业知识库建设、技术文档迁移、内容平台结构化存储等需求。
+        本接口为异步接口，有模型维度调用上限控制，单个模型qps限制5，如您有提高并发限制的需求请[联系我们](https://cloud.tencent.com/act/event/Online_service) 。
+
+        :param request: Request instance for ParseDocumentAsync.
+        :type request: :class:`tencentcloud.es.v20250101.models.ParseDocumentAsyncRequest`
+        :rtype: :class:`tencentcloud.es.v20250101.models.ParseDocumentAsyncResponse`
+
+        """
+        try:
+            params = request._serialize()
+            headers = request.headers
+            body = self.call("ParseDocumentAsync", params, headers=headers)
+            response = json.loads(body)
+            model = models.ParseDocumentAsyncResponse()
+            model._deserialize(response["Response"])
+            return model
+        except Exception as e:
+            if isinstance(e, TencentCloudSDKException):
+                raise
+            else:
+                raise TencentCloudSDKException(type(e).__name__, str(e))
+
+
+    def RunRerank(self, request):
+        r"""重排是指在 RAG 过程中，通过评估文档与查询之间的相关性，将最相关的文档放在前面，确保语言模型在生成回答时优先考虑排名靠前的上下文，提高生成结果的准确性和可信度，也可以通过这种方式进行过滤，减少大模型成本。
+        本接口有单账号调用上限控制，如您有提高并发限制的需求请[联系我们](https://cloud.tencent.com/act/event/Online_service)  。
+
+        :param request: Request instance for RunRerank.
+        :type request: :class:`tencentcloud.es.v20250101.models.RunRerankRequest`
+        :rtype: :class:`tencentcloud.es.v20250101.models.RunRerankResponse`
+
+        """
+        try:
+            params = request._serialize()
+            headers = request.headers
+            body = self.call("RunRerank", params, headers=headers)
+            response = json.loads(body)
+            model = models.RunRerankResponse()
+            model._deserialize(response["Response"])
+            return model
+        except Exception as e:
+            if isinstance(e, TencentCloudSDKException):
+                raise
+            else:
+                raise TencentCloudSDKException(type(e).__name__, str(e))

venv_prod/lib/python3.12/site-packages/tencentcloud/es/v20250101/es_client_async.py

@@ -0,0 +1,216 @@
+# -*- coding: utf8 -*-
+# Copyright (c) 2017-2025 Tencent. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+
+from tencentcloud.common.abstract_client_async import AbstractClient
+from tencentcloud.es.v20250101 import models
+from typing import Dict
+
+
+class EsClient(AbstractClient):
+    _apiVersion = '2025-01-01'
+    _endpoint = 'es.tencentcloudapi.com'
+    _service = 'es'
+
+    async def ChatCompletions(
+            self,
+            request: models.ChatCompletionsRequest,
+            opts: Dict = None,
+    ) -> models.ChatCompletionsResponse:
+        """
+        本服务支持一系列高性能的大语言模型，包括DeepSeek以及腾讯自主研发的混元大模型，结合混合搜索等先进搜索技术，快速高效实现RAG，有效解决幻觉和知识更新问题。
+        本接口有模型维度调用上限控制，单个模型qps限制5，如您有提高并发限制的需求请[联系我们](https://cloud.tencent.com/act/event/Online_service)  。
+        """
+        
+        kwargs = {}
+        kwargs["action"] = "ChatCompletions"
+        kwargs["params"] = request._serialize()
+        kwargs["resp_cls"] = models.ChatCompletionsResponse
+        kwargs["headers"] = request.headers
+        kwargs["opts"] = opts or {}
+        kwargs["opts"]["Endpoint"] = "%s://es.ai.tencentcloudapi.com" % self.profile.httpProfile.scheme
+        
+        return await self.call_and_deserialize(**kwargs)
+        
+    async def ChunkDocument(
+            self,
+            request: models.ChunkDocumentRequest,
+            opts: Dict = None,
+    ) -> models.ChunkDocumentResponse:
+        """
+        文本切片是将长文本分割为短片段的技术，用于适配模型输入、提升处理效率或信息检索，平衡片段长度与语义连贯性，适用于NLP、数据分析等场景。
+        本接口为分隔符规则切片接口，有单账号调用上限控制，如您有提高并发限制的需求请 [联系我们](https://cloud.tencent.com/act/event/Online_service)  。
+        """
+        
+        kwargs = {}
+        kwargs["action"] = "ChunkDocument"
+        kwargs["params"] = request._serialize()
+        kwargs["resp_cls"] = models.ChunkDocumentResponse
+        kwargs["headers"] = request.headers
+        kwargs["opts"] = opts or {}
+        
+        return await self.call_and_deserialize(**kwargs)
+        
+    async def ChunkDocumentAsync(
+            self,
+            request: models.ChunkDocumentAsyncRequest,
+            opts: Dict = None,
+    ) -> models.ChunkDocumentAsyncResponse:
+        """
+        文本切片是将长文本分割为短片段的技术，用于适配模型输入、提升处理效率或信息检索，平衡片段长度与语义连贯性，适用于NLP、数据分析等场景。
+        本接口为异步接口，有模型维度调用上限控制，单个模型qps限制5，如您有提高并发限制的需求请[联系我们](https://cloud.tencent.com/act/event/Online_service) 。
+        """
+        
+        kwargs = {}
+        kwargs["action"] = "ChunkDocumentAsync"
+        kwargs["params"] = request._serialize()
+        kwargs["resp_cls"] = models.ChunkDocumentAsyncResponse
+        kwargs["headers"] = request.headers
+        kwargs["opts"] = opts or {}
+        
+        return await self.call_and_deserialize(**kwargs)
+        
+    async def GetDocumentChunkResult(
+            self,
+            request: models.GetDocumentChunkResultRequest,
+            opts: Dict = None,
+    ) -> models.GetDocumentChunkResultResponse:
+        """
+        获取文档切片结果
+        """
+        
+        kwargs = {}
+        kwargs["action"] = "GetDocumentChunkResult"
+        kwargs["params"] = request._serialize()
+        kwargs["resp_cls"] = models.GetDocumentChunkResultResponse
+        kwargs["headers"] = request.headers
+        kwargs["opts"] = opts or {}
+        
+        return await self.call_and_deserialize(**kwargs)
+        
+    async def GetDocumentParseResult(
+            self,
+            request: models.GetDocumentParseResultRequest,
+            opts: Dict = None,
+    ) -> models.GetDocumentParseResultResponse:
+        """
+        本接口用于获取文档解析异步处理结果。
+        """
+        
+        kwargs = {}
+        kwargs["action"] = "GetDocumentParseResult"
+        kwargs["params"] = request._serialize()
+        kwargs["resp_cls"] = models.GetDocumentParseResultResponse
+        kwargs["headers"] = request.headers
+        kwargs["opts"] = opts or {}
+        
+        return await self.call_and_deserialize(**kwargs)
+        
+    async def GetMultiModalEmbedding(
+            self,
+            request: models.GetMultiModalEmbeddingRequest,
+            opts: Dict = None,
+    ) -> models.GetMultiModalEmbeddingResponse:
+        """
+        Embedding是一种将高维数据映射到低维空间的技术，通常用于将非结构化数据，如文本、图像或音频转化为向量表示，使其更容易输入机器模型进行处理，并且向量之间的距离可以反映对象之间的相似性。
+        本接口有模型维度调用上限控制，单个模型qps限制10，如您有提高并发限制的需求请[联系我们](https://cloud.tencent.com/act/event/Online_service)  。
+        """
+        
+        kwargs = {}
+        kwargs["action"] = "GetMultiModalEmbedding"
+        kwargs["params"] = request._serialize()
+        kwargs["resp_cls"] = models.GetMultiModalEmbeddingResponse
+        kwargs["headers"] = request.headers
+        kwargs["opts"] = opts or {}
+        
+        return await self.call_and_deserialize(**kwargs)
+        
+    async def GetTextEmbedding(
+            self,
+            request: models.GetTextEmbeddingRequest,
+            opts: Dict = None,
+    ) -> models.GetTextEmbeddingResponse:
+        """
+        Embedding是一种将高维数据映射到低维空间的技术，通常用于将非结构化数据，如文本、图像或音频转化为向量表示，使其更容易输入机器模型进行处理，并且向量之间的距离可以反映对象之间的相似性。
+        本接口有模型维度调用上限控制，单个模型qps限制20，如您有提高并发限制的需求请[联系我们](https://cloud.tencent.com/act/event/Online_service)  。
+        """
+        
+        kwargs = {}
+        kwargs["action"] = "GetTextEmbedding"
+        kwargs["params"] = request._serialize()
+        kwargs["resp_cls"] = models.GetTextEmbeddingResponse
+        kwargs["headers"] = request.headers
+        kwargs["opts"] = opts or {}
+        
+        return await self.call_and_deserialize(**kwargs)
+        
+    async def ParseDocument(
+            self,
+            request: models.ParseDocumentRequest,
+            opts: Dict = None,
+    ) -> models.ParseDocumentResponse:
+        """
+        本服务可将各类格式文档精准转换为标准格式，满足企业知识库建设、技术文档迁移、内容平台结构化存储等需求。
+        本接口有模型维度调用上限控制，单个模型qps限制5，如您有提高并发限制的需求请[联系我们](https://cloud.tencent.com/act/event/Online_service) 。
+        """
+        
+        kwargs = {}
+        kwargs["action"] = "ParseDocument"
+        kwargs["params"] = request._serialize()
+        kwargs["resp_cls"] = models.ParseDocumentResponse
+        kwargs["headers"] = request.headers
+        kwargs["opts"] = opts or {}
+        kwargs["opts"]["Endpoint"] = "%s://es.ai.tencentcloudapi.com" % self.profile.httpProfile.scheme
+        
+        return await self.call_and_deserialize(**kwargs)
+        
+    async def ParseDocumentAsync(
+            self,
+            request: models.ParseDocumentAsyncRequest,
+            opts: Dict = None,
+    ) -> models.ParseDocumentAsyncResponse:
+        """
+        本服务可将各类格式文档精准转换为标准格式，满足企业知识库建设、技术文档迁移、内容平台结构化存储等需求。
+        本接口为异步接口，有模型维度调用上限控制，单个模型qps限制5，如您有提高并发限制的需求请[联系我们](https://cloud.tencent.com/act/event/Online_service) 。
+        """
+        
+        kwargs = {}
+        kwargs["action"] = "ParseDocumentAsync"
+        kwargs["params"] = request._serialize()
+        kwargs["resp_cls"] = models.ParseDocumentAsyncResponse
+        kwargs["headers"] = request.headers
+        kwargs["opts"] = opts or {}
+        
+        return await self.call_and_deserialize(**kwargs)
+        
+    async def RunRerank(
+            self,
+            request: models.RunRerankRequest,
+            opts: Dict = None,
+    ) -> models.RunRerankResponse:
+        """
+        重排是指在 RAG 过程中，通过评估文档与查询之间的相关性，将最相关的文档放在前面，确保语言模型在生成回答时优先考虑排名靠前的上下文，提高生成结果的准确性和可信度，也可以通过这种方式进行过滤，减少大模型成本。
+        本接口有单账号调用上限控制，如您有提高并发限制的需求请[联系我们](https://cloud.tencent.com/act/event/Online_service)  。
+        """
+        
+        kwargs = {}
+        kwargs["action"] = "RunRerank"
+        kwargs["params"] = request._serialize()
+        kwargs["resp_cls"] = models.RunRerankResponse
+        kwargs["headers"] = request.headers
+        kwargs["opts"] = opts or {}
+        
+        return await self.call_and_deserialize(**kwargs)