feat: Support Anthropic

apps/setting/models_provider/constants/model_provider_constants.py

@@ -10,6 +10,7 @@
 
 from setting.models_provider.impl.aliyun_bai_lian_model_provider.aliyun_bai_lian_model_provider import \
     AliyunBaiLianModelProvider
+from setting.models_provider.impl.anthropic_model_provider.anthropic_model_provider import AnthropicModelProvider
 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
@@ -47,3 +48,4 @@ class ModelProvideConstants(Enum):
     model_xinference_provider = XinferenceModelProvider()
     model_vllm_provider = VllmModelProvider()
     aliyun_bai_lian_model_provider = AliyunBaiLianModelProvider()
+    model_anthropic_provider = AnthropicModelProvider()

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

@@ -0,0 +1,8 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： __init__.py.py
+    @date：2024/3/28 16:25
+    @desc:
+"""

apps/setting/models_provider/impl/anthropic_model_provider/anthropic_model_provider.py

@@ -0,0 +1,62 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： openai_model_provider.py
+    @date：2024/3/28 16:26
+    @desc:
+"""
+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.anthropic_model_provider.credential.image import AnthropicImageModelCredential
+from setting.models_provider.impl.anthropic_model_provider.credential.llm import AnthropicLLMModelCredential
+from setting.models_provider.impl.anthropic_model_provider.model.image import AnthropicImage
+from setting.models_provider.impl.anthropic_model_provider.model.llm import AnthropicChatModel
+from smartdoc.conf import PROJECT_DIR
+
+openai_llm_model_credential = AnthropicLLMModelCredential()
+openai_image_model_credential = AnthropicImageModelCredential()
+
+model_info_list = [
+    ModelInfo('claude-3-opus-20240229', '', ModelTypeConst.LLM,
+              openai_llm_model_credential, AnthropicChatModel
+              ),
+    ModelInfo('claude-3-sonnet-20240229', '', ModelTypeConst.LLM, openai_llm_model_credential,
+              AnthropicChatModel),
+    ModelInfo('claude-3-haiku-20240307', '', ModelTypeConst.LLM, openai_llm_model_credential,
+              AnthropicChatModel),
+    ModelInfo('claude-3-5-sonnet-20240620', '', ModelTypeConst.LLM, openai_llm_model_credential,
+              AnthropicChatModel),
+    ModelInfo('claude-3-5-haiku-20241022', '', ModelTypeConst.LLM, openai_llm_model_credential,
+              AnthropicChatModel),
+    ModelInfo('claude-3-5-sonnet-20241022', '', ModelTypeConst.LLM, openai_llm_model_credential,
+              AnthropicChatModel),
+]
+
+image_model_info = [
+    ModelInfo('claude-3-5-sonnet-20241022', '', ModelTypeConst.IMAGE, openai_image_model_credential,
+              AnthropicImage),
+]
+
+model_info_manage = (
+    ModelInfoManage.builder()
+    .append_model_info_list(model_info_list)
+    .append_default_model_info(model_info_list[0])
+    .append_model_info_list(image_model_info)
+    .append_default_model_info(image_model_info[0])
+    .build()
+)
+
+
+class AnthropicModelProvider(IModelProvider):
+
+    def get_model_info_manage(self):
+        return model_info_manage
+
+    def get_model_provide_info(self):
+        return ModelProvideInfo(provider='model_anthropic_provider', name='Anthropic', icon=get_file_content(
+            os.path.join(PROJECT_DIR, "apps", "setting", 'models_provider', 'impl', 'anthropic_model_provider', 'icon',
+                         'anthropic_icon_svg')))

apps/setting/models_provider/impl/anthropic_model_provider/credential/image.py

@@ -0,0 +1,65 @@
+# coding=utf-8
+import base64
+import os
+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, TooltipLabel
+from setting.models_provider.base_model_provider import BaseModelCredential, ValidCode
+
+class AnthropicImageModelParams(BaseForm):
+    temperature = forms.SliderField(TooltipLabel('温度', '较高的数值会使输出更加随机，而较低的数值会使其更加集中和确定'),
+                                    required=True, default_value=0.7,
+                                    _min=0.1,
+                                    _max=1.0,
+                                    _step=0.01,
+                                    precision=2)
+
+    max_tokens = forms.SliderField(
+        TooltipLabel('输出最大Tokens', '指定模型可生成的最大token个数'),
+        required=True, default_value=800,
+        _min=1,
+        _max=100000,
+        _step=1,
+        precision=0)
+
+
+
+class AnthropicImageModelCredential(BaseForm, BaseModelCredential):
+    api_base = forms.TextInputField('API 域名', required=True)
+    api_key = forms.PasswordInputField('API Key', required=True)
+
+    def is_valid(self, model_type: str, model_name, model_credential: Dict[str, object], model_params, 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 ['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)
+            res = model.stream([HumanMessage(content=[{"type": "text", "text": "你好"}])])
+            for chunk in res:
+                print(chunk)
+        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', ''))}
+
+    def get_model_params_setting_form(self, model_name):
+        return AnthropicImageModelParams()

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

@@ -0,0 +1,69 @@
+# coding=utf-8
+"""
+    @project: MaxKB
+    @Author：虎
+    @file： llm.py
+    @date：2024/7/11 18:32
+    @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, TooltipLabel
+from setting.models_provider.base_model_provider import BaseModelCredential, ValidCode
+
+
+class AnthropicLLMModelParams(BaseForm):
+    temperature = forms.SliderField(TooltipLabel('温度', '较高的数值会使输出更加随机，而较低的数值会使其更加集中和确定'),
+                                    required=True, default_value=0.7,
+                                    _min=0.1,
+                                    _max=1.0,
+                                    _step=0.01,
+                                    precision=2)
+
+    max_tokens = forms.SliderField(
+        TooltipLabel('输出最大Tokens', '指定模型可生成的最大token个数'),
+        required=True, default_value=800,
+        _min=1,
+        _max=100000,
+        _step=1,
+        precision=0)
+
+
+class AnthropicLLMModelCredential(BaseForm, BaseModelCredential):
+
+    def is_valid(self, model_type: str, model_name, model_credential: Dict[str, object], model_params, 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 ['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.invoke([HumanMessage(content='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)
+
+    def get_model_params_setting_form(self, model_name):
+        return AnthropicLLMModelParams()

apps/setting/models_provider/impl/anthropic_model_provider/model/image.py

@@ -0,0 +1,26 @@
+from typing import Dict
+
+from langchain_anthropic import ChatAnthropic
+
+from common.config.tokenizer_manage_config import TokenizerManage
+from setting.models_provider.base_model_provider import MaxKBBaseModel
+
+
+def custom_get_token_ids(text: str):
+    tokenizer = TokenizerManage.get_tokenizer()
+    return tokenizer.encode(text)
+
+
+class AnthropicImage(MaxKBBaseModel, ChatAnthropic):
+
+    @staticmethod
+    def new_instance(model_type, model_name, model_credential: Dict[str, object], **model_kwargs):
+        optional_params = MaxKBBaseModel.filter_optional_params(model_kwargs)
+        return AnthropicImage(
+            model=model_name,
+            anthropic_api_url=model_credential.get('api_base'),
+            anthropic_api_key=model_credential.get('api_key'),
+            # stream_options={"include_usage": True},
+            streaming=True,
+            **optional_params,
+        )

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

@@ -0,0 +1,53 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： llm.py
+    @date：2024/4/18 15:28
+    @desc:
+"""
+from typing import List, Dict
+
+from langchain_anthropic import ChatAnthropic
+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
+
+
+def custom_get_token_ids(text: str):
+    tokenizer = TokenizerManage.get_tokenizer()
+    return tokenizer.encode(text)
+
+
+class AnthropicChatModel(MaxKBBaseModel, ChatAnthropic):
+
+    @staticmethod
+    def is_cache_model():
+        return False
+
+    @staticmethod
+    def new_instance(model_type, model_name, model_credential: Dict[str, object], **model_kwargs):
+        optional_params = MaxKBBaseModel.filter_optional_params(model_kwargs)
+        azure_chat_open_ai = AnthropicChatModel(
+            model=model_name,
+            anthropic_api_url=model_credential.get('api_base'),
+            anthropic_api_key=model_credential.get('api_key'),
+            **optional_params,
+            custom_get_token_ids=custom_get_token_ids
+        )
+        return azure_chat_open_ai
+
+    def get_num_tokens_from_messages(self, messages: List[BaseMessage]) -> int:
+        try:
+            return super().get_num_tokens_from_messages(messages)
+        except Exception as e:
+            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:
+        try:
+            return super().get_num_tokens(text)
+        except Exception as e:
+            tokenizer = TokenizerManage.get_tokenizer()
+            return len(tokenizer.encode(text))

pyproject.toml

@@ -41,6 +41,7 @@ httpx = "^0.27.0"
 httpx-sse = "^0.4.0"
 websockets = "^13.0"
 langchain-google-genai = "^1.0.3"
+langchain-anthropic= "^0.1.0"
 openpyxl = "^3.1.2"
 xlrd = "^2.0.1"
 gunicorn = "^22.0.0"