feat: 增加对接模型

apps/setting/models_provider/constants/model_provider_constants.py

@@ -8,6 +8,7 @@
 """
 from enum import Enum
 
+from setting.models_provider.impl.aws_bedrock_model_provider.aws_bedrock_model_provider import BedrockModelProvider
 from setting.models_provider.impl.azure_model_provider.azure_model_provider import AzureModelProvider
 from setting.models_provider.impl.deepseek_model_provider.deepseek_model_provider import DeepSeekModelProvider
 from setting.models_provider.impl.gemini_model_provider.gemini_model_provider import GeminiModelProvider
@@ -15,6 +16,9 @@ from setting.models_provider.impl.kimi_model_provider.kimi_model_provider import
 from setting.models_provider.impl.ollama_model_provider.ollama_model_provider import OllamaModelProvider
 from setting.models_provider.impl.openai_model_provider.openai_model_provider import OpenAIModelProvider
 from setting.models_provider.impl.qwen_model_provider.qwen_model_provider import QwenModelProvider
+from setting.models_provider.impl.tencent_model_provider.tencent_model_provider import TencentModelProvider
+from setting.models_provider.impl.volcanic_engine_model_provider.volcanic_engine_model_provider import \
+    VolcanicEngineModelProvider
 from setting.models_provider.impl.wenxin_model_provider.wenxin_model_provider import WenxinModelProvider
 from setting.models_provider.impl.xf_model_provider.xf_model_provider import XunFeiModelProvider
 from setting.models_provider.impl.zhipu_model_provider.zhipu_model_provider import ZhiPuModelProvider
@@ -32,4 +36,7 @@ class ModelProvideConstants(Enum):
     model_xf_provider = XunFeiModelProvider()
     model_deepseek_provider = DeepSeekModelProvider()
     model_gemini_provider = GeminiModelProvider()
+    model_volcanic_engine_provider = VolcanicEngineModelProvider()
+    model_tencent_provider = TencentModelProvider()
+    model_aws_bedrock_provider = BedrockModelProvider()
     model_local_provider = LocalModelProvider()

apps/setting/models_provider/impl/aws_bedrock_model_provider/__init__.py

@@ -0,0 +1,2 @@
+#!/usr/bin/env python
+# -*- coding: UTF-8 -*-

apps/setting/models_provider/impl/aws_bedrock_model_provider/aws_bedrock_model_provider.py

@@ -0,0 +1,107 @@
+#!/usr/bin/env python
+# -*- coding: UTF-8 -*-
+
+import os
+from common.util.file_util import get_file_content
+from setting.models_provider.base_model_provider import (
+    IModelProvider, ModelProvideInfo, ModelInfo, ModelTypeConst, ModelInfoManage
+)
+from setting.models_provider.impl.aws_bedrock_model_provider.credential.llm import BedrockLLMModelCredential
+from setting.models_provider.impl.aws_bedrock_model_provider.model.llm import BedrockModel
+from setting.models_provider.impl.tencent_model_provider.credential.embedding import TencentEmbeddingCredential
+from setting.models_provider.impl.tencent_model_provider.credential.llm import TencentLLMModelCredential
+from setting.models_provider.impl.tencent_model_provider.model.embedding import TencentEmbeddingModel
+from setting.models_provider.impl.tencent_model_provider.model.llm import TencentModel
+from smartdoc.conf import PROJECT_DIR
+
+
+def _create_model_info(model_name, description, model_type, credential_class, model_class):
+    return ModelInfo(
+        name=model_name,
+        desc=description,
+        model_type=model_type,
+        model_credential=credential_class(),
+        model_class=model_class
+    )
+
+
+def _get_aws_bedrock_icon_path():
+    return os.path.join(PROJECT_DIR, "apps", "setting", 'models_provider', 'impl', 'aws_bedrock_model_provider',
+                        'icon', 'bedrock_icon_svg')
+
+
+def _initialize_model_info():
+    model_info_list = [_create_model_info(
+        'amazon.titan-text-premier-v1:0',
+        'Titan Text Premier 是 Titan Text 系列中功能强大且先进的型号，旨在为各种企业应用程序提供卓越的性能。凭借其尖端功能，它提供了更高的准确性和出色的结果，使其成为寻求一流文本处理解决方案的组织的绝佳选择。',
+        ModelTypeConst.LLM,
+        BedrockLLMModelCredential,
+        BedrockModel
+    ),
+        _create_model_info(
+            'amazon.titan-text-lite-v1',
+            'Amazon Titan Text Lite 是一种轻量级的高效模型，非常适合英语任务的微调，包括摘要和文案写作等，在这种场景下，客户需要更小、更经济高效且高度可定制的模型',
+            ModelTypeConst.LLM,
+            BedrockLLMModelCredential,
+            BedrockModel),
+        _create_model_info(
+            'amazon.titan-text-express-v1',
+            'Amazon Titan Text Express 的上下文长度长达 8000 个令牌，因而非常适合各种高级常规语言任务，例如开放式文本生成和对话式聊天，以及检索增强生成（RAG）中的支持。在发布时，该模型针对英语进行了优化，但也支持其他语言。',
+            ModelTypeConst.LLM,
+            BedrockLLMModelCredential,
+            BedrockModel),
+        _create_model_info(
+            'amazon.titan-embed-text-v2:0',
+            'Amazon Titan Text Embeddings V2 是一种轻量级、高效的模型，非常适合在不同维度上执行高精度检索任务。该模型支持灵活的嵌入大小（1024、512 和 256），并优先考虑在较小维度上保持准确性，从而可以在不影响准确性的情况下降低存储成本。Titan Text Embeddings V2 适用于各种任务，包括文档检索、推荐系统、搜索引擎和对话式系统。',
+            ModelTypeConst.LLM,
+            BedrockLLMModelCredential,
+            BedrockModel),
+        _create_model_info(
+            'mistral.mistral-7b-instruct-v0:2',
+            '7B 密集型转换器，可快速部署，易于定制。体积虽小，但功能强大，适用于各种用例。支持英语和代码，以及 32k 的上下文窗口。',
+            ModelTypeConst.EMBEDDING,
+            BedrockLLMModelCredential,
+            BedrockModel),
+        _create_model_info(
+            'mistral.mistral-large-2402-v1:0',
+            '先进的 Mistral AI 大型语言模型，能够处理任何语言任务，包括复杂的多语言推理、文本理解、转换和代码生成。',
+            ModelTypeConst.LLM,
+            BedrockLLMModelCredential,
+            BedrockModel),
+        _create_model_info(
+            'meta.llama3-70b-instruct-v1:0',
+            '非常适合内容创作、会话式人工智能、语言理解、研发和企业应用',
+            ModelTypeConst.LLM,
+            BedrockLLMModelCredential,
+            BedrockModel),
+        _create_model_info(
+            'meta.llama3-8b-instruct-v1:0',
+            '非常适合有限的计算能力和资源、边缘设备和更快的训练时间。',
+            ModelTypeConst.LLM,
+            BedrockLLMModelCredential,
+            BedrockModel),
+    ]
+
+    model_info_manage = ModelInfoManage.builder() \
+        .append_model_info_list(model_info_list) \
+        .append_default_model_info(model_info_list[0]) \
+        .build()
+
+    return model_info_manage
+
+
+class BedrockModelProvider(IModelProvider):
+    def __init__(self):
+        self._model_info_manage = _initialize_model_info()
+
+    def get_model_info_manage(self):
+        return self._model_info_manage
+
+    def get_model_provide_info(self):
+        icon_path = _get_aws_bedrock_icon_path()
+        icon_data = get_file_content(icon_path)
+        return ModelProvideInfo(
+            provider='model_aws_bedrock_provider',
+            name='Amazon Bedrock',
+            icon=icon_data
+        )

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

@@ -0,0 +1,64 @@
+import json
+from typing import Dict
+
+from tencentcloud.common import credential
+from tencentcloud.common.profile.client_profile import ClientProfile
+from tencentcloud.common.profile.http_profile import HttpProfile
+from tencentcloud.hunyuan.v20230901 import hunyuan_client, models
+
+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 TencentEmbeddingCredential(BaseForm, BaseModelCredential):
+    @classmethod
+    def _validate_model_type(cls, model_type: str, provider) -> bool:
+        model_type_list = provider.get_model_type_list()
+        if not any(mt.get('value') == model_type for mt in model_type_list):
+            raise AppApiException(ValidCode.valid_error.value, f'{model_type} 模型类型不支持')
+        return True
+
+    @classmethod
+    def _validate_credential(cls, model_credential: Dict[str, object]) -> credential.Credential:
+        for key in ['SecretId', 'SecretKey']:
+            if key not in model_credential:
+                raise AppApiException(ValidCode.valid_error.value, f'{key} 字段为必填字段')
+        return credential.Credential(model_credential['SecretId'], model_credential['SecretKey'])
+
+    @classmethod
+    def _test_credentials(cls, client, model_name: str):
+        req = models.GetEmbeddingRequest()
+        params = {
+            "Model": model_name,
+            "Input": "测试"
+        }
+        req.from_json_string(json.dumps(params))
+        try:
+            res = client.GetEmbedding(req)
+            print(res.to_json_string())
+        except Exception as e:
+            raise AppApiException(ValidCode.valid_error.value, f'校验失败,请检查参数是否正确: {str(e)}')
+
+    def is_valid(self, model_type: str, model_name, model_credential: Dict[str, object], provider,
+                 raise_exception=True) -> bool:
+        try:
+            self._validate_model_type(model_type, provider)
+            cred = self._validate_credential(model_credential)
+            httpProfile = HttpProfile(endpoint="hunyuan.tencentcloudapi.com")
+            clientProfile = ClientProfile(httpProfile=httpProfile)
+            client = hunyuan_client.HunyuanClient(cred, "", clientProfile)
+            self._test_credentials(client, model_name)
+            return True
+        except AppApiException as e:
+            if raise_exception:
+                raise e
+            return False
+
+    def encryption_dict(self, model: Dict[str, object]) -> Dict[str, object]:
+        encrypted_secret_key = super().encryption(model.get('SecretKey', ''))
+        return {**model, 'SecretKey': encrypted_secret_key}
+
+    SecretId = forms.PasswordInputField('SecretId', required=True)
+    SecretKey = forms.PasswordInputField('SecretKey', required=True)

apps/setting/models_provider/impl/aws_bedrock_model_provider/credential/llm.py

@@ -0,0 +1,62 @@
+import os
+import re
+from typing import Dict
+from common.exception.app_exception import AppApiException
+from common.forms import BaseForm
+from setting.models_provider.base_model_provider import ValidCode, BaseModelCredential
+from langchain_core.messages import HumanMessage
+from common import forms
+
+
+class BedrockLLMModelCredential(BaseForm, BaseModelCredential):
+
+    @staticmethod
+    def _update_aws_credentials(profile_name, access_key_id, secret_access_key):
+        credentials_path = os.path.join(os.path.expanduser("~"), ".aws", "credentials")
+        os.makedirs(os.path.dirname(credentials_path), exist_ok=True)
+
+        content = open(credentials_path, 'r').read() if os.path.exists(credentials_path) else ''
+        pattern = rf'\n*\[{profile_name}\]\n*(aws_access_key_id = .*)\n*(aws_secret_access_key = .*)\n*'
+        content = re.sub(pattern, '', content, flags=re.DOTALL)
+
+        if not re.search(rf'\[{profile_name}\]', content):
+            content += f"\n[{profile_name}]\naws_access_key_id = {access_key_id}\naws_secret_access_key = {secret_access_key}\n"
+
+        with open(credentials_path, 'w') as file:
+            file.write(content)
+
+    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(mt.get('value') == model_type for mt in model_type_list):
+            if raise_exception:
+                raise AppApiException(ValidCode.valid_error.value, f'{model_type} 模型类型不支持')
+            return False
+
+        required_keys = ['region_name', 'access_key_id', 'secret_access_key']
+        if not all(key in model_credential for key in required_keys):
+            if raise_exception:
+                raise AppApiException(ValidCode.valid_error.value, f'以下字段为必填字段: {", ".join(required_keys)}')
+            return False
+
+        try:
+            self._update_aws_credentials('aws-profile', model_credential['access_key_id'],
+                                         model_credential['secret_access_key'])
+            model_credential['credentials_profile_name'] = 'aws-profile'
+            model = provider.get_model(model_type, model_name, model_credential)
+            model.invoke([HumanMessage(content='你好')])
+        except AppApiException:
+            raise
+        except Exception as 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)}')
+            return False
+
+        return True
+
+    def encryption_dict(self, model: Dict[str, object]):
+        return {**model, 'secret_access_key': super().encryption(model.get('secret_access_key', ''))}
+
+    region_name = forms.TextInputField('Region Name', required=True)
+    access_key_id = forms.TextInputField('Access Key ID', required=True)
+    secret_access_key = forms.PasswordInputField('Secret Access Key', required=True)

apps/setting/models_provider/impl/aws_bedrock_model_provider/icon/bedrock_icon_svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" version="2.0" focusable="false" aria-hidden="true" class="globalNav-1216 globalNav-1213" data-testid="awsc-logo" viewBox="0 0 29 17"><path class="globalNav-1214" d="M8.38 6.17a2.6 2.6 0 00.11.83c.08.232.18.456.3.67a.4.4 0 01.07.21.36.36 0 01-.18.28l-.59.39a.43.43 0 01-.24.08.38.38 0 01-.28-.13 2.38 2.38 0 01-.34-.43c-.09-.16-.18-.34-.28-.55a3.44 3.44 0 01-2.74 1.29 2.54 2.54 0 01-1.86-.67 2.36 2.36 0 01-.68-1.79 2.43 2.43 0 01.84-1.92 3.43 3.43 0 012.29-.72 6.75 6.75 0 011 .07c.35.05.7.12 1.07.2V3.3a2.06 2.06 0 00-.44-1.49 2.12 2.12 0 00-1.52-.43 4.4 4.4 0 00-1 .12 6.85 6.85 0 00-1 .32l-.33.12h-.14c-.14 0-.2-.1-.2-.29v-.46A.62.62 0 012.3.87a.78.78 0 01.27-.2A6 6 0 013.74.25 5.7 5.7 0 015.19.07a3.37 3.37 0 012.44.76 3 3 0 01.77 2.29l-.02 3.05zM4.6 7.59a3 3 0 001-.17 2 2 0 00.88-.6 1.36 1.36 0 00.32-.59 3.18 3.18 0 00.09-.81V5A7.52 7.52 0 006 4.87h-.88a2.13 2.13 0 00-1.38.37 1.3 1.3 0 00-.46 1.08 1.3 1.3 0 00.34 1c.278.216.63.313.98.27zm7.49 1a.56.56 0 01-.36-.09.73.73 0 01-.2-.37L9.35.93a1.39 1.39 0 01-.08-.38c0-.15.07-.23.22-.23h.92a.56.56 0 01.36.09.74.74 0 01.19.37L12.53 7 14 .79a.61.61 0 01.18-.37.59.59 0 01.37-.09h.75a.62.62 0 01.38.09.74.74 0 01.18.37L17.31 7 18.92.76a.74.74 0 01.19-.37.56.56 0 01.36-.09h.87a.21.21 0 01.23.23 1 1 0 010 .15s0 .13-.06.23l-2.26 7.2a.74.74 0 01-.19.37.6.6 0 01-.36.09h-.8a.53.53 0 01-.37-.1.64.64 0 01-.18-.37l-1.45-6-1.44 6a.64.64 0 01-.18.37.55.55 0 01-.37.1l-.82.02zm12 .24a6.29 6.29 0 01-1.44-.16 4.21 4.21 0 01-1.07-.37.69.69 0 01-.29-.26.66.66 0 01-.06-.27V7.3c0-.19.07-.29.21-.29a.57.57 0 01.18 0l.23.1c.32.143.656.25 1 .32.365.08.737.12 1.11.12a2.47 2.47 0 001.36-.31 1 1 0 00.48-.88.88.88 0 00-.25-.65 2.29 2.29 0 00-.94-.49l-1.35-.43a2.83 2.83 0 01-1.49-.94 2.24 2.24 0 01-.47-1.36 2 2 0 01.25-1c.167-.3.395-.563.67-.77a3 3 0 011-.48A4.1 4.1 0 0124.4.08a4.4 4.4 0 01.62 0l.61.1.53.15.39.16c.105.062.2.14.28.23a.57.57 0 01.08.31v.44c0 .2-.07.3-.21.3a.92.92 0 01-.36-.12 4.35 4.35 0 00-1.8-.36 2.51 2.51 0 00-1.24.26.92.92 0 00-.44.84c0 .249.1.488.28.66.295.236.635.41 1 .51l1.32.42a2.88 2.88 0 011.44.9 2.1 2.1 0 01.43 1.31 2.38 2.38 0 01-.24 1.08 2.34 2.34 0 01-.68.82 3 3 0 01-1 .53 4.59 4.59 0 01-1.35.22l.03-.01z"></path><path class="globalNav-1215" d="M25.82 13.43a20.07 20.07 0 01-11.35 3.47A20.54 20.54 0 01.61 11.62c-.29-.26 0-.62.32-.42a27.81 27.81 0 0013.86 3.68 27.54 27.54 0 0010.58-2.16c.52-.22.96.34.45.71z"></path><path class="globalNav-1215" d="M27.1 12c-.4-.51-2.6-.24-3.59-.12-.3 0-.34-.23-.07-.42 1.75-1.23 4.63-.88 5-.46.37.42-.09 3.3-1.74 4.68-.25.21-.49.09-.38-.18.34-.95 1.17-3.02.78-3.5z"></path></svg>

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

@@ -0,0 +1,25 @@
+from setting.models_provider.base_model_provider import MaxKBBaseModel
+from typing import Dict
+import requests
+
+
+class TencentEmbeddingModel(MaxKBBaseModel):
+    def __init__(self, secret_id: str, secret_key: str, api_base: str, model_name: str):
+        self.secret_id = secret_id
+        self.secret_key = secret_key
+        self.api_base = api_base
+        self.model_name = model_name
+
+    @staticmethod
+    def new_instance(model_type: str, model_name: str, model_credential: Dict[str, str], **model_kwargs):
+        return TencentEmbeddingModel(
+            secret_id=model_credential.get('SecretId'),
+            secret_key=model_credential.get('SecretKey'),
+            api_base=model_credential.get('api_base'),
+            model_name=model_name,
+        )
+
+
+    def _generate_auth_token(self):
+        # Example method to generate an authentication token for the model API
+        return f"{self.secret_id}:{self.secret_key}"

apps/setting/models_provider/impl/aws_bedrock_model_provider/model/llm.py

@@ -0,0 +1,35 @@
+from typing import List, Dict, Any
+from langchain_community.chat_models import BedrockChat
+from langchain_core.messages import BaseMessage, get_buffer_string
+from common.config.tokenizer_manage_config import TokenizerManage
+from setting.models_provider.base_model_provider import MaxKBBaseModel
+
+
+class BedrockModel(MaxKBBaseModel, BedrockChat):
+
+    def __init__(self, model_id: str, region_name: str, credentials_profile_name: str,
+                 streaming: bool = False, **kwargs):
+        super().__init__(model_id=model_id, region_name=region_name,
+                         credentials_profile_name=credentials_profile_name, streaming=streaming, **kwargs)
+
+    @classmethod
+    def new_instance(cls, model_type: str, model_name: str, model_credential: Dict[str, str],
+                     **model_kwargs) -> 'BedrockModel':
+        return cls(
+            model_id=model_name,
+            region_name=model_credential['region_name'],
+            credentials_profile_name=model_credential['credentials_profile_name'],
+            streaming=model_kwargs.pop('streaming', False),
+            **model_kwargs
+        )
+
+    def _get_num_tokens(self, content: str) -> int:
+        """Helper method to count tokens in a string."""
+        tokenizer = TokenizerManage.get_tokenizer()
+        return len(tokenizer.encode(content))
+
+    def get_num_tokens_from_messages(self, messages: List[BaseMessage]) -> int:
+        return sum(self._get_num_tokens(get_buffer_string([message])) for message in messages)
+
+    def get_num_tokens(self, text: str) -> int:
+        return self._get_num_tokens(text)

apps/setting/models_provider/impl/tencent_model_provider/__init__.py

@@ -0,0 +1,2 @@
+#!/usr/bin/env python
+# -*- coding: UTF-8 -*-

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

@@ -0,0 +1,64 @@
+import json
+from typing import Dict
+
+from tencentcloud.common import credential
+from tencentcloud.common.profile.client_profile import ClientProfile
+from tencentcloud.common.profile.http_profile import HttpProfile
+from tencentcloud.hunyuan.v20230901 import hunyuan_client, models
+
+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 TencentEmbeddingCredential(BaseForm, BaseModelCredential):
+    @classmethod
+    def _validate_model_type(cls, model_type: str, provider) -> bool:
+        model_type_list = provider.get_model_type_list()
+        if not any(mt.get('value') == model_type for mt in model_type_list):
+            raise AppApiException(ValidCode.valid_error.value, f'{model_type} 模型类型不支持')
+        return True
+
+    @classmethod
+    def _validate_credential(cls, model_credential: Dict[str, object]) -> credential.Credential:
+        for key in ['SecretId', 'SecretKey']:
+            if key not in model_credential:
+                raise AppApiException(ValidCode.valid_error.value, f'{key} 字段为必填字段')
+        return credential.Credential(model_credential['SecretId'], model_credential['SecretKey'])
+
+    @classmethod
+    def _test_credentials(cls, client, model_name: str):
+        req = models.GetEmbeddingRequest()
+        params = {
+            "Model": model_name,
+            "Input": "测试"
+        }
+        req.from_json_string(json.dumps(params))
+        try:
+            res = client.GetEmbedding(req)
+            print(res.to_json_string())
+        except Exception as e:
+            raise AppApiException(ValidCode.valid_error.value, f'校验失败,请检查参数是否正确: {str(e)}')
+
+    def is_valid(self, model_type: str, model_name, model_credential: Dict[str, object], provider,
+                 raise_exception=True) -> bool:
+        try:
+            self._validate_model_type(model_type, provider)
+            cred = self._validate_credential(model_credential)
+            httpProfile = HttpProfile(endpoint="hunyuan.tencentcloudapi.com")
+            clientProfile = ClientProfile(httpProfile=httpProfile)
+            client = hunyuan_client.HunyuanClient(cred, "", clientProfile)
+            self._test_credentials(client, model_name)
+            return True
+        except AppApiException as e:
+            if raise_exception:
+                raise e
+            return False
+
+    def encryption_dict(self, model: Dict[str, object]) -> Dict[str, object]:
+        encrypted_secret_key = super().encryption(model.get('SecretKey', ''))
+        return {**model, 'SecretKey': encrypted_secret_key}
+
+    SecretId = forms.PasswordInputField('SecretId', required=True)
+    SecretKey = forms.PasswordInputField('SecretKey', required=True)

apps/setting/models_provider/impl/tencent_model_provider/credential/llm.py

@@ -0,0 +1,47 @@
+# coding=utf-8
+from langchain_core.messages import HumanMessage
+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 TencentLLMModelCredential(BaseForm, BaseModelCredential):
+    REQUIRED_FIELDS = ['hunyuan_app_id', 'hunyuan_secret_id', 'hunyuan_secret_key']
+
+    @classmethod
+    def _validate_model_type(cls, model_type, provider, raise_exception=False):
+        if not any(mt['value'] == model_type for mt in provider.get_model_type_list()):
+            if raise_exception:
+                raise AppApiException(ValidCode.valid_error.value, f'{model_type} 模型类型不支持')
+            return False
+        return True
+
+    @classmethod
+    def _validate_credential_fields(cls, model_credential, raise_exception=False):
+        missing_keys = [key for key in cls.REQUIRED_FIELDS if key not in model_credential]
+        if missing_keys:
+            if raise_exception:
+                raise AppApiException(ValidCode.valid_error.value, f'{", ".join(missing_keys)} 字段为必填字段')
+            return False
+        return True
+
+    def is_valid(self, model_type, model_name, model_credential, provider, raise_exception=False):
+        if not (self._validate_model_type(model_type, provider, raise_exception) and
+                self._validate_credential_fields(model_credential, raise_exception)):
+            return False
+        try:
+            model = provider.get_model(model_type, model_name, model_credential)
+            model.invoke([HumanMessage(content='你好')])
+        except Exception as 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)}')
+            return False
+        return True
+
+    def encryption_dict(self, model):
+        return {**model, 'hunyuan_secret_key': super().encryption(model.get('hunyuan_secret_key', ''))}
+
+    hunyuan_app_id = forms.TextInputField('APP ID', required=True)
+    hunyuan_secret_id = forms.PasswordInputField('SecretId', required=True)
+    hunyuan_secret_key = forms.PasswordInputField('SecretKey', required=True)

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

@@ -0,0 +1,25 @@
+from setting.models_provider.base_model_provider import MaxKBBaseModel
+from typing import Dict
+import requests
+
+
+class TencentEmbeddingModel(MaxKBBaseModel):
+    def __init__(self, secret_id: str, secret_key: str, api_base: str, model_name: str):
+        self.secret_id = secret_id
+        self.secret_key = secret_key
+        self.api_base = api_base
+        self.model_name = model_name
+
+    @staticmethod
+    def new_instance(model_type: str, model_name: str, model_credential: Dict[str, str], **model_kwargs):
+        return TencentEmbeddingModel(
+            secret_id=model_credential.get('SecretId'),
+            secret_key=model_credential.get('SecretKey'),
+            api_base=model_credential.get('api_base'),
+            model_name=model_name,
+        )
+
+
+    def _generate_auth_token(self):
+        # Example method to generate an authentication token for the model API
+        return f"{self.secret_id}:{self.secret_key}"

apps/setting/models_provider/impl/tencent_model_provider/model/hunyuan.py

@@ -0,0 +1,273 @@
+import json
+import logging
+from typing import Any, Dict, Iterator, List, Mapping, Optional, Type
+
+from langchain_core.callbacks import CallbackManagerForLLMRun
+from langchain_core.language_models.chat_models import (
+    BaseChatModel,
+    generate_from_stream,
+)
+from langchain_core.messages import (
+    AIMessage,
+    AIMessageChunk,
+    BaseMessage,
+    BaseMessageChunk,
+    ChatMessage,
+    ChatMessageChunk,
+    HumanMessage,
+    HumanMessageChunk,
+)
+from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
+from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
+from langchain_core.utils import (
+    convert_to_secret_str,
+    get_from_dict_or_env,
+    get_pydantic_field_names,
+    pre_init,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def _convert_message_to_dict(message: BaseMessage) -> dict:
+    message_dict: Dict[str, Any]
+    if isinstance(message, ChatMessage):
+        message_dict = {"Role": message.role, "Content": message.content}
+    elif isinstance(message, HumanMessage):
+        message_dict = {"Role": "user", "Content": message.content}
+    elif isinstance(message, AIMessage):
+        message_dict = {"Role": "assistant", "Content": message.content}
+    else:
+        raise TypeError(f"Got unknown type {message}")
+
+    return message_dict
+
+
+def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
+    role = _dict["Role"]
+    if role == "user":
+        return HumanMessage(content=_dict["Content"])
+    elif role == "assistant":
+        return AIMessage(content=_dict.get("Content", "") or "")
+    else:
+        return ChatMessage(content=_dict["Content"], role=role)
+
+
+def _convert_delta_to_message_chunk(
+    _dict: Mapping[str, Any], default_class: Type[BaseMessageChunk]
+) -> BaseMessageChunk:
+    role = _dict.get("Role")
+    content = _dict.get("Content") or ""
+
+    if role == "user" or default_class == HumanMessageChunk:
+        return HumanMessageChunk(content=content)
+    elif role == "assistant" or default_class == AIMessageChunk:
+        return AIMessageChunk(content=content)
+    elif role or default_class == ChatMessageChunk:
+        return ChatMessageChunk(content=content, role=role)  # type: ignore[arg-type]
+    else:
+        return default_class(content=content)  # type: ignore[call-arg]
+
+
+def _create_chat_result(response: Mapping[str, Any]) -> ChatResult:
+    generations = []
+    for choice in response["Choices"]:
+        message = _convert_dict_to_message(choice["Message"])
+        generations.append(ChatGeneration(message=message))
+
+    token_usage = response["Usage"]
+    llm_output = {"token_usage": token_usage}
+    return ChatResult(generations=generations, llm_output=llm_output)
+
+
+class ChatHunyuan(BaseChatModel):
+    """Tencent Hunyuan chat models API by Tencent.
+
+    For more information, see https://cloud.tencent.com/document/product/1729
+    """
+
+    @property
+    def lc_secrets(self) -> Dict[str, str]:
+        return {
+            "hunyuan_app_id": "HUNYUAN_APP_ID",
+            "hunyuan_secret_id": "HUNYUAN_SECRET_ID",
+            "hunyuan_secret_key": "HUNYUAN_SECRET_KEY",
+        }
+
+    @property
+    def lc_serializable(self) -> bool:
+        return True
+
+    hunyuan_app_id: Optional[int] = None
+    """Hunyuan App ID"""
+    hunyuan_secret_id: Optional[str] = None
+    """Hunyuan Secret ID"""
+    hunyuan_secret_key: Optional[SecretStr] = None
+    """Hunyuan Secret Key"""
+    streaming: bool = False
+    """Whether to stream the results or not."""
+    request_timeout: int = 60
+    """Timeout for requests to Hunyuan API. Default is 60 seconds."""
+    temperature: float = 1.0
+    """What sampling temperature to use."""
+    top_p: float = 1.0
+    """What probability mass to use."""
+    model: str = "hunyuan-lite"
+    """What Model to use.
+     Optional model:
+    - hunyuan-lite、
+    - hunyuan-standard
+    - hunyuan-standard-256K
+    - hunyuan-pro
+    - hunyuan-code
+    - hunyuan-role
+    - hunyuan-functioncall
+    - hunyuan-vision
+    """
+    stream_moderation: bool = False
+    """Whether to review the results or not when streaming is true."""
+    enable_enhancement: bool = True
+    """Whether to enhancement the results or not."""
+
+    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
+    """Holds any model parameters valid for API call not explicitly specified."""
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        allow_population_by_field_name = True
+
+    @root_validator(pre=True)
+    def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
+        """Build extra kwargs from additional params that were passed in."""
+        all_required_field_names = get_pydantic_field_names(cls)
+        extra = values.get("model_kwargs", {})
+        for field_name in list(values):
+            if field_name in extra:
+                raise ValueError(f"Found {field_name} supplied twice.")
+            if field_name not in all_required_field_names:
+                logger.warning(
+                    f"""WARNING! {field_name} is not default parameter.
+                    {field_name} was transferred to model_kwargs.
+                    Please confirm that {field_name} is what you intended."""
+                )
+                extra[field_name] = values.pop(field_name)
+
+        invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
+        if invalid_model_kwargs:
+            raise ValueError(
+                f"Parameters {invalid_model_kwargs} should be specified explicitly. "
+                f"Instead they were passed in as part of `model_kwargs` parameter."
+            )
+
+        values["model_kwargs"] = extra
+        return values
+
+    @pre_init
+    def validate_environment(cls, values: Dict) -> Dict:
+        values["hunyuan_app_id"] = get_from_dict_or_env(
+            values,
+            "hunyuan_app_id",
+            "HUNYUAN_APP_ID",
+        )
+        values["hunyuan_secret_id"] = get_from_dict_or_env(
+            values,
+            "hunyuan_secret_id",
+            "HUNYUAN_SECRET_ID",
+        )
+        values["hunyuan_secret_key"] = convert_to_secret_str(
+            get_from_dict_or_env(
+                values,
+                "hunyuan_secret_key",
+                "HUNYUAN_SECRET_KEY",
+            )
+        )
+        return values
+
+    @property
+    def _default_params(self) -> Dict[str, Any]:
+        """Get the default parameters for calling Hunyuan API."""
+        normal_params = {
+            "Temperature": self.temperature,
+            "TopP": self.top_p,
+            "Model": self.model,
+            "Stream": self.streaming,
+            "StreamModeration": self.stream_moderation,
+            "EnableEnhancement": self.enable_enhancement,
+        }
+        return {**normal_params, **self.model_kwargs}
+
+    def _generate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> ChatResult:
+        if self.streaming:
+            stream_iter = self._stream(
+                messages=messages, stop=stop, run_manager=run_manager, **kwargs
+            )
+            return generate_from_stream(stream_iter)
+
+        res = self._chat(messages, **kwargs)
+        return _create_chat_result(json.loads(res.to_json_string()))
+
+    def _stream(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> Iterator[ChatGenerationChunk]:
+        res = self._chat(messages, **kwargs)
+
+        default_chunk_class = AIMessageChunk
+        for chunk in res:
+            chunk = chunk.get("data", "")
+            if len(chunk) == 0:
+                continue
+            response = json.loads(chunk)
+            if "error" in response:
+                raise ValueError(f"Error from Hunyuan api response: {response}")
+
+            for choice in response["Choices"]:
+                chunk = _convert_delta_to_message_chunk(
+                    choice["Delta"], default_chunk_class
+                )
+                default_chunk_class = chunk.__class__
+                cg_chunk = ChatGenerationChunk(message=chunk)
+                if run_manager:
+                    run_manager.on_llm_new_token(chunk.content, chunk=cg_chunk)
+                yield cg_chunk
+
+    def _chat(self, messages: List[BaseMessage], **kwargs: Any) -> Any:
+        if self.hunyuan_secret_key is None:
+            raise ValueError("Hunyuan secret key is not set.")
+
+        try:
+            from tencentcloud.common import credential
+            from tencentcloud.hunyuan.v20230901 import hunyuan_client, models
+        except ImportError:
+            raise ImportError(
+                "Could not import tencentcloud python package. "
+                "Please install it with `pip install tencentcloud-sdk-python`."
+            )
+
+        parameters = {**self._default_params, **kwargs}
+        cred = credential.Credential(
+            self.hunyuan_secret_id, str(self.hunyuan_secret_key.get_secret_value())
+        )
+        client = hunyuan_client.HunyuanClient(cred, "")
+        req = models.ChatCompletionsRequest()
+        params = {
+            "Messages": [_convert_message_to_dict(m) for m in messages],
+            **parameters,
+        }
+        req.from_json_string(json.dumps(params))
+        resp = client.ChatCompletions(req)
+        return resp
+
+    @property
+    def _llm_type(self) -> str:
+        return "hunyuan-chat"

apps/setting/models_provider/impl/tencent_model_provider/model/llm.py

@@ -0,0 +1,37 @@
+# coding=utf-8
+
+from typing import List, Dict
+
+from langchain_core.messages import BaseMessage, get_buffer_string
+from common.config.tokenizer_manage_config import TokenizerManage
+from setting.models_provider.base_model_provider import MaxKBBaseModel
+from setting.models_provider.impl.tencent_model_provider.model.hunyuan import ChatHunyuan
+
+
+class TencentModel(MaxKBBaseModel, ChatHunyuan):
+
+    def __init__(self, model_name: str, credentials: Dict[str, str], streaming: bool = False, **kwargs):
+        hunyuan_app_id = credentials.get('hunyuan_app_id')
+        hunyuan_secret_id = credentials.get('hunyuan_secret_id')
+        hunyuan_secret_key = credentials.get('hunyuan_secret_key')
+
+        if not all([hunyuan_app_id, hunyuan_secret_id, hunyuan_secret_key]):
+            raise ValueError(
+                "All of 'hunyuan_app_id', 'hunyuan_secret_id', and 'hunyuan_secret_key' must be provided in credentials.")
+
+        super().__init__(model=model_name, hunyuan_app_id=hunyuan_app_id, hunyuan_secret_id=hunyuan_secret_id,
+                         hunyuan_secret_key=hunyuan_secret_key, streaming=streaming, **kwargs)
+
+    @staticmethod
+    def new_instance(model_type: str, model_name: str, model_credential: Dict[str, object],
+                     **model_kwargs) -> 'TencentModel':
+        streaming = model_kwargs.pop('streaming', False)
+        return TencentModel(model_name=model_name, credentials=model_credential, streaming=streaming, **model_kwargs)
+
+    def get_num_tokens_from_messages(self, messages: List[BaseMessage]) -> int:
+        tokenizer = TokenizerManage.get_tokenizer()
+        return sum(len(tokenizer.encode(get_buffer_string([m]))) for m in messages)
+
+    def get_num_tokens(self, text: str) -> int:
+        tokenizer = TokenizerManage.get_tokenizer()
+        return len(tokenizer.encode(text))

apps/setting/models_provider/impl/tencent_model_provider/tencent_model_provider.py

@@ -0,0 +1,103 @@
+#!/usr/bin/env python
+# -*- coding: UTF-8 -*-
+
+import os
+from common.util.file_util import get_file_content
+from setting.models_provider.base_model_provider import (
+    IModelProvider, ModelProvideInfo, ModelInfo, ModelTypeConst, ModelInfoManage
+)
+from setting.models_provider.impl.tencent_model_provider.credential.embedding import TencentEmbeddingCredential
+from setting.models_provider.impl.tencent_model_provider.credential.llm import TencentLLMModelCredential
+from setting.models_provider.impl.tencent_model_provider.model.embedding import TencentEmbeddingModel
+from setting.models_provider.impl.tencent_model_provider.model.llm import TencentModel
+from smartdoc.conf import PROJECT_DIR
+
+
+def _create_model_info(model_name, description, model_type, credential_class, model_class):
+    return ModelInfo(
+        name=model_name,
+        desc=description,
+        model_type=model_type,
+        model_credential=credential_class(),
+        model_class=model_class
+    )
+
+
+def _get_tencent_icon_path():
+    return os.path.join(PROJECT_DIR, "apps", "setting", 'models_provider', 'impl', 'tencent_model_provider',
+                        'icon', 'tencent_icon_svg')
+
+
+def _initialize_model_info():
+    model_info_list = [_create_model_info(
+        'hunyuan-pro',
+        '当前混元模型中效果最优版本，万亿级参数规模 MOE-32K 长文模型。在各种 benchmark 上达到绝对领先的水平，复杂指令和推理，具备复杂数学能力，支持 functioncall，在多语言翻译、金融法律医疗等领域应用重点优化',
+        ModelTypeConst.LLM,
+        TencentLLMModelCredential,
+        TencentModel
+    ),
+        _create_model_info(
+            'hunyuan-standard',
+            '采用更优的路由策略，同时缓解了负载均衡和专家趋同的问题。长文方面，大海捞针指标达到99.9%',
+            ModelTypeConst.LLM,
+            TencentLLMModelCredential,
+            TencentModel),
+        _create_model_info(
+            'hunyuan-lite',
+            '升级为 MOE 结构，上下文窗口为 256k ，在 NLP，代码，数学，行业等多项评测集上领先众多开源模型',
+            ModelTypeConst.LLM,
+            TencentLLMModelCredential,
+            TencentModel),
+        _create_model_info(
+            'hunyuan-role',
+            '混元最新版角色扮演模型，混元官方精调训练推出的角色扮演模型，基于混元模型结合角色扮演场景数据集进行增训，在角色扮演场景具有更好的基础效果',
+            ModelTypeConst.LLM,
+            TencentLLMModelCredential,
+            TencentModel),
+        _create_model_info(
+            'hunyuan-functioncall ',
+            '混元最新 MOE 架构 FunctionCall 模型，经过高质量的 FunctionCall 数据训练，上下文窗口达 32K，在多个维度的评测指标上处于领先。',
+            ModelTypeConst.LLM,
+            TencentLLMModelCredential,
+            TencentModel),
+        _create_model_info(
+            'hunyuan-code',
+            '混元最新代码生成模型，经过 200B 高质量代码数据增训基座模型，迭代半年高质量 SFT 数据训练，上下文长窗口长度增大到 8K，五大语言代码生成自动评测指标上位居前列；五大语言10项考量各方面综合代码任务人工高质量评测上，性能处于第一梯队',
+            ModelTypeConst.LLM,
+            TencentLLMModelCredential,
+            TencentModel),
+    ]
+
+    tencent_embedding_model_info = _create_model_info(
+        'hunyuan-embedding',
+        '',
+        ModelTypeConst.EMBEDDING,
+        TencentEmbeddingCredential,
+        TencentEmbeddingModel
+    )
+
+    model_info_embedding_list = [tencent_embedding_model_info]
+
+    model_info_manage = ModelInfoManage.builder() \
+        .append_model_info_list(model_info_list) \
+        .append_default_model_info(model_info_list[0]) \
+        .build()
+
+    return model_info_manage
+
+
+class TencentModelProvider(IModelProvider):
+    def __init__(self):
+        self._model_info_manage = _initialize_model_info()
+
+    def get_model_info_manage(self):
+        return self._model_info_manage
+
+    def get_model_provide_info(self):
+        icon_path = _get_tencent_icon_path()
+        icon_data = get_file_content(icon_path)
+        return ModelProvideInfo(
+            provider='model_tencent_provider',
+            name='腾讯混元',
+            icon=icon_data
+        )

apps/setting/models_provider/impl/volcanic_engine_model_provider/__init__.py

@@ -0,0 +1,2 @@
+#!/usr/bin/env python
+# -*- coding: UTF-8 -*-

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

@@ -0,0 +1,46 @@
+# coding=utf-8
+"""
+    @project: MaxKB
+    @Author：虎
+    @file： embedding.py
+    @date：2024/7/12 16:45
+    @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 OpenAIEmbeddingCredential(BaseForm, BaseModelCredential):
+    def is_valid(self, model_type: str, model_name, model_credential: Dict[str, object], provider,
+                 raise_exception=True):
+        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 ['api_base', '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 = 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, 'api_key': super().encryption(model.get('api_key', ''))}
+
+    api_base = forms.TextInputField('API 域名', required=True)
+    api_key = forms.PasswordInputField('API Key', required=True)

apps/setting/models_provider/impl/volcanic_engine_model_provider/credential/llm.py

@@ -0,0 +1,50 @@
+# coding=utf-8
+"""
+    @project: MaxKB
+    @Author：虎
+    @file： llm.py
+    @date：2024/7/11 17:57
+    @desc:
+"""
+from typing import Dict
+
+from langchain_core.messages import HumanMessage
+
+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 VolcanicEngineLLMModelCredential(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 ['access_key_id', 'secret_access_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 = provider.get_model(model_type, model_name, model_credential)
+            res = model.invoke([HumanMessage(content='你好')])
+            print(res)
+        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, 'access_key_id': super().encryption(model.get('access_key_id', ''))}
+
+    access_key_id = forms.PasswordInputField('Access Key ID', required=True)
+    secret_access_key = forms.PasswordInputField('Secret Access Key', required=True)

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

@@ -0,0 +1,15 @@
+from typing import Dict
+
+from langchain_community.embeddings import VolcanoEmbeddings
+
+from setting.models_provider.base_model_provider import MaxKBBaseModel
+
+
+class VolcanicEngineEmbeddingModel(MaxKBBaseModel, VolcanoEmbeddings):
+    @staticmethod
+    def new_instance(model_type, model_name, model_credential: Dict[str, object], **model_kwargs):
+        return VolcanicEngineEmbeddingModel(
+            api_key=model_credential.get('api_key'),
+            model=model_name,
+            openai_api_base=model_credential.get('api_base'),
+        )

apps/setting/models_provider/impl/volcanic_engine_model_provider/model/llm.py

@@ -0,0 +1,27 @@
+from typing import List, Dict
+
+from langchain_community.chat_models import VolcEngineMaasChat
+from langchain_core.messages import BaseMessage, get_buffer_string
+
+from common.config.tokenizer_manage_config import TokenizerManage
+from setting.models_provider.base_model_provider import MaxKBBaseModel
+from langchain_openai import ChatOpenAI
+
+
+class VolcanicEngineChatModel(MaxKBBaseModel, ChatOpenAI):
+    @staticmethod
+    def new_instance(model_type, model_name, model_credential: Dict[str, object], **model_kwargs):
+        volcanic_engine_chat = VolcanicEngineChatModel(
+            model=model_name,
+            volc_engine_maas_ak=model_credential.get("access_key_id"),
+            volc_engine_maas_sk=model_credential.get("secret_access_key"),
+        )
+        return volcanic_engine_chat
+
+    def get_num_tokens_from_messages(self, messages: List[BaseMessage]) -> int:
+        tokenizer = TokenizerManage.get_tokenizer()
+        return sum([len(tokenizer.encode(get_buffer_string([m]))) for m in messages])
+
+    def get_num_tokens(self, text: str) -> int:
+        tokenizer = TokenizerManage.get_tokenizer()
+        return len(tokenizer.encode(text))

apps/setting/models_provider/impl/volcanic_engine_model_provider/volcanic_engine_model_provider.py

@@ -0,0 +1,51 @@
+#!/usr/bin/env python
+# -*- coding: UTF-8 -*-
+"""
+@Project ：MaxKB 
+@File    ：gemini_model_provider.py
+@Author  ：Brian Yang
+@Date    ：5/13/24 7:47 AM 
+"""
+import os
+
+from common.util.file_util import get_file_content
+from setting.models_provider.base_model_provider import IModelProvider, ModelProvideInfo, ModelInfo, ModelTypeConst, \
+    ModelInfoManage
+from setting.models_provider.impl.openai_model_provider.credential.embedding import OpenAIEmbeddingCredential
+from setting.models_provider.impl.openai_model_provider.credential.llm import OpenAILLMModelCredential
+from setting.models_provider.impl.openai_model_provider.model.embedding import OpenAIEmbeddingModel
+from setting.models_provider.impl.openai_model_provider.model.llm import OpenAIChatModel
+
+from smartdoc.conf import PROJECT_DIR
+
+volcanic_engine_llm_model_credential = OpenAILLMModelCredential()
+
+model_info_list = [
+    ModelInfo('ep-xxxxxxxxxx-yyyy',
+              '用户前往火山方舟的模型推理页面创建推理接入点，这里需要输入ep-xxxxxxxxxx-yyyy进行调用',
+              ModelTypeConst.LLM,
+              volcanic_engine_llm_model_credential, OpenAIChatModel
+              )
+]
+
+open_ai_embedding_credential = OpenAIEmbeddingCredential()
+model_info_embedding_list = [
+    ModelInfo('ep-xxxxxxxxxx-yyyy',
+              '用户前往火山方舟的模型推理页面创建推理接入点，这里需要输入ep-xxxxxxxxxx-yyyy进行调用',
+              ModelTypeConst.EMBEDDING, open_ai_embedding_credential,
+              OpenAIEmbeddingModel)]
+
+model_info_manage = ModelInfoManage.builder().append_model_info_list(model_info_list).append_default_model_info(
+    model_info_list[0]).build()
+
+
+class VolcanicEngineModelProvider(IModelProvider):
+
+    def get_model_info_manage(self):
+        return model_info_manage
+
+    def get_model_provide_info(self):
+        return ModelProvideInfo(provider='model_volcanic_engine_provider', name='火山引擎', icon=get_file_content(
+            os.path.join(PROJECT_DIR, "apps", "setting", 'models_provider', 'impl', 'volcanic_engine_model_provider',
+                         'icon',
+                         'volcanic_engine_icon_svg')))

pyproject.toml

@@ -46,6 +46,8 @@ gunicorn = "^22.0.0"
 python-daemon = "3.0.1"
 gevent = "^24.2.1"
 
+boto3 = "^1.34.151"
+langchain-aws = "^0.1.13"
 [build-system]
 requires = ["poetry-core"]
 build-backend = "poetry.core.masonry.api"