feat: 支持讯飞向量模型

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

@@ -0,0 +1,43 @@
+# coding=utf-8
+"""
+    @project: MaxKB
+    @Author：虎
+    @file： embedding.py
+    @date：2024/10/17 15:40
+    @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 BaseModelCredential, ValidCode
+
+
+class XFEmbeddingCredential(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')
+        self.valid_form(model_credential)
+        try:
+            model = 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
+
+    base_url = forms.TextInputField('API 域名', required=True, default_value="https://emb-cn-huabei-1.xf-yun.com/")
+    spark_app_id = forms.TextInputField('APP ID', required=True)
+    spark_api_key = forms.PasswordInputField("API Key", required=True)
+    spark_api_secret = forms.PasswordInputField('API Secret', required=True)

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

@@ -0,0 +1,49 @@
+# coding=utf-8
+"""
+    @project: MaxKB
+    @Author：虎
+    @file： embedding.py
+    @date：2024/10/17 15:29
+    @desc:
+"""
+
+import base64
+import json
+from typing import Dict, Optional
+
+import numpy as np
+from langchain_community.embeddings import SparkLLMTextEmbeddings
+from numpy import ndarray
+
+from setting.models_provider.base_model_provider import MaxKBBaseModel
+
+
+class XFEmbedding(MaxKBBaseModel, SparkLLMTextEmbeddings):
+    @staticmethod
+    def new_instance(model_type, model_name, model_credential: Dict[str, object], **model_kwargs):
+        return XFEmbedding(
+            spark_app_id=model_credential.get('spark_app_id'),
+            spark_api_key=model_credential.get('spark_api_key'),
+            spark_api_secret=model_credential.get('spark_api_secret')
+        )
+
+    @staticmethod
+    def _parser_message(
+            message: str,
+    ) -> Optional[ndarray]:
+        data = json.loads(message)
+        code = data["header"]["code"]
+        if code != 0:
+            # 这里是讯飞的QPS限制会报错,所以不建议用讯飞的向量模型
+            raise Exception(f"Request error: {code}, {data}")
+        else:
+            text_base = data["payload"]["feature"]["text"]
+            text_data = base64.b64decode(text_base)
+            dt = np.dtype(np.float32)
+            dt = dt.newbyteorder("<")
+            text = np.frombuffer(text_data, dtype=dt)
+            if len(text) > 2560:
+                array = text[:2560]
+            else:
+                array = text
+            return array

apps/setting/models_provider/impl/xf_model_provider/xf_model_provider.py

@@ -12,9 +12,11 @@
 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.xf_model_provider.credential.embedding import XFEmbeddingCredential
 from setting.models_provider.impl.xf_model_provider.credential.llm import XunFeiLLMModelCredential
 from setting.models_provider.impl.xf_model_provider.credential.stt import XunFeiSTTModelCredential
 from setting.models_provider.impl.xf_model_provider.credential.tts import XunFeiTTSModelCredential
+from setting.models_provider.impl.xf_model_provider.model.embedding import XFEmbedding
 from setting.models_provider.impl.xf_model_provider.model.llm import XFChatSparkLLM
 from setting.models_provider.impl.xf_model_provider.model.stt import XFSparkSpeechToText
 from setting.models_provider.impl.xf_model_provider.model.tts import XFSparkTextToSpeech
@@ -25,12 +27,14 @@
 qwen_model_credential = XunFeiLLMModelCredential()
 stt_model_credential = XunFeiSTTModelCredential()
 tts_model_credential = XunFeiTTSModelCredential()
+embedding_model_credential = XFEmbeddingCredential()
 model_info_list = [
     ModelInfo('generalv3.5', '', ModelTypeConst.LLM, qwen_model_credential, XFChatSparkLLM),
     ModelInfo('generalv3', '', ModelTypeConst.LLM, qwen_model_credential, XFChatSparkLLM),
     ModelInfo('generalv2', '', ModelTypeConst.LLM, qwen_model_credential, XFChatSparkLLM),
     ModelInfo('iat', '中英文识别', ModelTypeConst.STT, stt_model_credential, XFSparkSpeechToText),
     ModelInfo('tts', '', ModelTypeConst.TTS, tts_model_credential, XFSparkTextToSpeech),
+    ModelInfo('embedding', '', ModelTypeConst.EMBEDDING, embedding_model_credential, XFEmbedding)
 ]
 
 model_info_manage = ModelInfoManage.builder().append_model_info_list(model_info_list).append_default_model_info(