feat: 支持阿里云百炼向量模型

apps/common/chunk/impl/mark_chunk_handle.py

@@ -11,27 +11,25 @@
 
 from common.chunk.i_chunk_handle import IChunkHandle
 
-split_chunk_pattern = "！|。|\n|；|;"
-min_chunk_len = 20
+max_chunk_len = 256
+split_chunk_pattern = r'.{1,%d}[。| |\\.|！|;|；|!|\n]' % max_chunk_len
+max_chunk_pattern = r'.{1,%d}' % max_chunk_len
 
 
 class MarkChunkHandle(IChunkHandle):
     def handle(self, chunk_list: List[str]):
         result = []
         for chunk in chunk_list:
-            base_chunk = re.split(split_chunk_pattern, chunk)
-            base_chunk = [chunk.strip() for chunk in base_chunk if len(chunk.strip()) > 0]
-            result_chunk = []
-            for c in base_chunk:
-                if len(result_chunk) == 0:
-                    result_chunk.append(c)
-                else:
-                    if len(result_chunk[-1]) < min_chunk_len:
-                        result_chunk[-1] = result_chunk[-1] + c
+            chunk_result = re.findall(split_chunk_pattern, chunk, flags=re.DOTALL)
+            for c_r in chunk_result:
+                result.append(c_r)
+            other_chunk_list = re.split(split_chunk_pattern, chunk, flags=re.DOTALL)
+            for other_chunk in other_chunk_list:
+                if len(other_chunk) > 0:
+                    if len(other_chunk) < max_chunk_len:
+                        result.append(other_chunk)
                     else:
-                        if len(c) < min_chunk_len:
-                            result_chunk[-1] = result_chunk[-1] + c
-                        else:
-                            result_chunk.append(c)
-            result = [*result, *result_chunk]
+                        max_chunk_list = re.findall(max_chunk_pattern, other_chunk, flags=re.DOTALL)
+                        for m_c in max_chunk_list:
+                            result.append(m_c)
         return result

apps/setting/models_provider/impl/aliyun_bai_lian_model_provider/aliyun_bai_lian_model_provider.py

@@ -11,10 +11,13 @@
 from common.util.file_util import get_file_content
 from setting.models_provider.base_model_provider import ModelProvideInfo, ModelTypeConst, ModelInfo, IModelProvider, \
     ModelInfoManage
+from setting.models_provider.impl.aliyun_bai_lian_model_provider.credential.embedding import \
+    AliyunBaiLianEmbeddingCredential
 from setting.models_provider.impl.aliyun_bai_lian_model_provider.credential.reranker import \
     AliyunBaiLianRerankerCredential
 from setting.models_provider.impl.aliyun_bai_lian_model_provider.credential.stt import AliyunBaiLianSTTModelCredential
 from setting.models_provider.impl.aliyun_bai_lian_model_provider.credential.tts import AliyunBaiLianTTSModelCredential
+from setting.models_provider.impl.aliyun_bai_lian_model_provider.model.embedding import AliyunBaiLianEmbedding
 from setting.models_provider.impl.aliyun_bai_lian_model_provider.model.reranker import AliyunBaiLianReranker
 from setting.models_provider.impl.aliyun_bai_lian_model_provider.model.stt import AliyunBaiLianSpeechToText
 from setting.models_provider.impl.aliyun_bai_lian_model_provider.model.tts import AliyunBaiLianTextToSpeech
@@ -23,6 +26,7 @@
 aliyun_bai_lian_model_credential = AliyunBaiLianRerankerCredential()
 aliyun_bai_lian_tts_model_credential = AliyunBaiLianTTSModelCredential()
 aliyun_bai_lian_stt_model_credential = AliyunBaiLianSTTModelCredential()
+aliyun_bai_lian_embedding_model_credential = AliyunBaiLianEmbeddingCredential()
 
 model_info_list = [ModelInfo('gte-rerank',
                              '阿里巴巴通义实验室开发的GTE-Rerank文本排序系列模型，开发者可以通过LlamaIndex框架进行集成高质量文本检索、排序。',
@@ -33,10 +37,15 @@
                    ModelInfo('cosyvoice-v1',
                              'CosyVoice基于新一代生成式语音大模型，能根据上下文预测情绪、语调、韵律等，具有更好的拟人效果',
                              ModelTypeConst.TTS, aliyun_bai_lian_tts_model_credential, AliyunBaiLianTextToSpeech),
+                   ModelInfo('text-embedding-v1',
+                             '通用文本向量，是通义实验室基于LLM底座的多语言文本统一向量模型，面向全球多个主流语种，提供高水准的向量服务，帮助开发者将文本数据快速转换为高质量的向量数据。',
+                             ModelTypeConst.EMBEDDING, aliyun_bai_lian_embedding_model_credential,
+                             AliyunBaiLianEmbedding),
                    ]
 
 model_info_manage = ModelInfoManage.builder().append_model_info_list(model_info_list).append_default_model_info(
-    model_info_list[1]).append_default_model_info(model_info_list[2]).build()
+    model_info_list[1]).append_default_model_info(model_info_list[2]).append_default_model_info(
+    model_info_list[3]).build()
 
 
 class AliyunBaiLianModelProvider(IModelProvider):

apps/setting/models_provider/impl/aliyun_bai_lian_model_provider/credential/embedding.py

@@ -0,0 +1,46 @@
+# coding=utf-8
+"""
+    @project: MaxKB
+    @Author：虎
+    @file： embedding.py
+    @date：2024/10/16 17:01
+    @desc:
+"""
+from typing import Dict
+
+from common import forms
+from common.exception.app_exception import AppApiException
+from common.forms import BaseForm
+from setting.models_provider.base_model_provider import ValidCode, BaseModelCredential
+from setting.models_provider.impl.aliyun_bai_lian_model_provider.model.embedding import AliyunBaiLianEmbedding
+
+
+class AliyunBaiLianEmbeddingCredential(BaseForm, BaseModelCredential):
+
+    def is_valid(self, model_type: str, model_name, model_credential: Dict[str, object], provider,
+                 raise_exception=False):
+        model_type_list = provider.get_model_type_list()
+        if not any(list(filter(lambda mt: mt.get('value') == model_type, model_type_list))):
+            raise AppApiException(ValidCode.valid_error.value, f'{model_type} 模型类型不支持')
+        for key in ['dashscope_api_key']:
+            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: AliyunBaiLianEmbedding = provider.get_model(model_type, model_name, model_credential)
+            model.embed_query('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: Dict[str, object]):
+        return model
+
+    dashscope_api_key = forms.PasswordInputField('API Key', required=True)

apps/setting/models_provider/impl/aliyun_bai_lian_model_provider/model/embedding.py

@@ -0,0 +1,54 @@
+# coding=utf-8
+"""
+    @project: MaxKB
+    @Author：虎
+    @file： embedding.py
+    @date：2024/10/16 16:34
+    @desc:
+"""
+from typing import Dict, List
+
+from langchain_community.embeddings import DashScopeEmbeddings
+from langchain_community.embeddings.dashscope import embed_with_retry
+
+from setting.models_provider.base_model_provider import MaxKBBaseModel
+
+
+class AliyunBaiLianEmbedding(MaxKBBaseModel, DashScopeEmbeddings):
+    @staticmethod
+    def new_instance(model_type, model_name, model_credential: Dict[str, object], **model_kwargs):
+        return AliyunBaiLianEmbedding(
+            model=model_name,
+            dashscope_api_key=model_credential.get('dashscope_api_key')
+        )
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        """Call out to DashScope's embedding endpoint for embedding search docs.
+
+        Args:
+            texts: The list of texts to embed.
+            chunk_size: The chunk size of embeddings. If None, will use the chunk size
+                specified by the class.
+
+        Returns:
+            List of embeddings, one for each text.
+        """
+        embeddings = embed_with_retry(
+            self, input=texts, text_type="document", model=self.model
+        )
+        embedding_list = [item["embedding"] for item in embeddings]
+        return embedding_list
+
+    def embed_query(self, text: str) -> List[float]:
+        """Call out to DashScope's embedding endpoint for embedding query text.
+
+        Args:
+            text: The text to embed.
+
+        Returns:
+            Embedding for the text.
+        """
+        embedding = embed_with_retry(
+            self, input=[text], text_type="document", model=self.model
+        )[0]["embedding"]
+        return embedding