update ernie llm with eb_bot_sdk

Signed-off-by: ChengZi <chen.zhang@zilliz.com>

.github/workflows/unit_test.yml

@@ -31,6 +31,7 @@ jobs:
         pip install coverage
         pip install pytest
         pip install -r requirements.txt
+        pip install -r test_requirements.txt
     - name: Install test dependency
       shell: bash
       working-directory: tests

config.py

@@ -24,8 +24,9 @@
         }
     },
     'ernie': {
-        'ernie_api_key': None, # If None, use environment  value 'ERNIE_API_KEY'
-        'ernie_secret_key': None, # If None, use environment value 'ERNIE_SECRET_KEY'
+        'ernie_model': 'ernie-bot-turbo',  # 'ernie-bot' or 'ernie-bot-turbo'
+        'eb_api_type': None, # If None, use environment  value 'EB_API_TYPE'
+        'eb_access_token': None, # If None, use environment value 'EB_ACCESS_TOKEN'
         'llm_kwargs': {}
     },
     'minimax': {

src_langchain/llm/ernie.py

@@ -1,81 +1,52 @@
 from config import CHAT_CONFIG  # pylint: disable=C0413
-from typing import Mapping, Any, List, Optional, Tuple, Dict
-import requests
-import json
+from typing import Any, List, Dict
 import os
 import sys
 
 from langchain.chat_models.base import BaseChatModel
-from langchain.schema import BaseMessage, ChatResult, HumanMessage, AIMessage, SystemMessage, ChatMessage, ChatGeneration
+from langchain.schema import BaseMessage, ChatResult, HumanMessage, AIMessage, SystemMessage, ChatMessage, \
+    ChatGeneration
 
 sys.path.append(os.path.join(os.path.dirname(__file__), '../..'))
 
-
 CHAT_CONFIG = CHAT_CONFIG['ernie']
 llm_kwargs = CHAT_CONFIG.get('llm_kwargs', {})
 
 
 class ChatLLM(BaseChatModel):
     '''Chat with LLM given context. Must be a LangChain BaseLanguageModel to adapt agent.'''
-    api_key: str = CHAT_CONFIG['ernie_api_key']
-    secret_key: str = CHAT_CONFIG['ernie_secret_key']
-    temperature: float = llm_kwargs.get('temperature', 0)
-    max_tokens: Optional[int] = llm_kwargs.get('max_tokens', None)
-    n: int = llm_kwargs.get('n', 1)
-
-    def _generate(self, messages: List[BaseMessage], stop: Optional[List[str]] = None) -> ChatResult:
-        message_dicts, params = self._create_message_dicts(messages, stop)
-        params['messages'] = message_dicts
-        payload = json.dumps(params)
-        headers = {
-            'Content-Type': 'application/json'
-        }
-
-        url = self._create_url()
-        response = requests.request(
-            'POST', url, headers=headers, data=payload)
+    model_name: str = CHAT_CONFIG['ernie_model']
+    eb_api_type: str = CHAT_CONFIG['eb_api_type'] or os.getenv('EB_API_TYPE')
+    eb_access_token: str = CHAT_CONFIG['eb_access_token'] or os.getenv('EB_ACCESS_TOKEN')
+    llm_kwargs: dict = llm_kwargs
+
+    def _generate(self, messages: List[BaseMessage]) -> ChatResult:
+        import erniebot  # pylint: disable=C0415
+        erniebot.api_type = self.eb_api_type
+        erniebot.access_token = self.eb_access_token
+
+        message_dicts = self._create_message_dicts(messages)
+        response = erniebot.ChatCompletion.create(
+            model=self.model_name,
+            messages=message_dicts,
+            **self.llm_kwargs
+        )
         return self._create_chat_result(response)
 
-    async def _agenerate(self, messages: List[BaseMessage], stop: Optional[List[str]] = None) -> ChatResult:
-        message_dicts, params = self._create_message_dicts(messages, stop)
-        params['messages'] = message_dicts
-        payload = json.dumps(params)
-        headers = {
-            'Content-Type': 'application/json'
-        }
-        url = self._create_url()
-        response = requests.request(
-            'POST', url, headers=headers, data=payload)
-        return self._create_chat_result(response)
-
-    def _create_url(self):
-        access_token = self._get_access_token(
-            api_key=self.api_key, secret_key=self.secret_key)
-        url = 'https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions?access_token=' \
-            + access_token
-        return url
-
     def _create_message_dicts(
-        self, messages: List[BaseMessage], stop: Optional[List[str]]
-    ) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
-        params: Dict[str, Any] = {**self._default_params}
-        if stop is not None:
-            if 'stop' in params:
-                raise ValueError(
-                    '`stop` found in both the input and default params.')
-            params['stop'] = stop
+            self, messages: List[BaseMessage]
+    ) -> List[Dict[str, Any]]:
+
         message_dicts = []
         for m in messages:
-            message_dicts.append(self._convert_message_to_dict(m))
-            if isinstance(m, SystemMessage):
-                message_dicts.append(
-                    {'role': 'assistant', 'content': 'OK.'}
-                )
-        return message_dicts, params
+            m_dict = self._convert_message_to_dict(m)
+            if m_dict:
+                message_dicts.append(self._convert_message_to_dict(m))
+        return message_dicts
 
-    def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
+    def _create_chat_result(self, response: 'EBResponse') -> ChatResult:
         generations = []
-        response = response.json()
+        response = response.to_dict()
         if 'result' not in response:
             raise RuntimeError(response)
         message = self._convert_dict_to_message(
@@ -88,14 +59,13 @@ def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
 
     @staticmethod
     def _convert_message_to_dict(message: BaseMessage) -> dict:
+        message_dict = {}
         if isinstance(message, ChatMessage):
             message_dict = {'role': message.role, 'content': message.content}
-        elif isinstance(message, (HumanMessage, SystemMessage)):
+        elif isinstance(message, HumanMessage):
             message_dict = {'role': 'user', 'content': message.content}
         elif isinstance(message, AIMessage):
             message_dict = {'role': 'assistant', 'content': message.content}
-        else:
-            raise ValueError(f'Got unknown type {message}')
         if 'name' in message.additional_kwargs:
             message_dict['name'] = message.additional_kwargs['name']
         return message_dict
@@ -112,32 +82,6 @@ def _convert_dict_to_message(_dict: dict) -> BaseMessage:  # pylint: disable=C01
         else:
             return ChatMessage(content=_dict['content'], role=role)
 
-    @staticmethod
-    def _get_access_token(api_key, secret_key):
-        url = 'https://aip.baidubce.com/oauth/2.0/token'
-        params = {
-            'grant_type': 'client_credentials',
-            'client_id': api_key,
-            'client_secret': secret_key
-        }
-        return str(requests.post(url, params=params).json().get('access_token'))
-
-    @property
-    def _default_params(self) -> Dict[str, Any]:
-        '''Get the default parameters for calling OpenAI API.'''
-        return {
-            'max_tokens': self.max_tokens,
-            'n': self.n,
-            'temperature': self.temperature,
-        }
-
     @property
     def _llm_type(self) -> str:
         return 'ernie'
-
-
-# if __name__ == '__main__':
-#     chat = ChatLLM()
-#     messages = [HumanMessage(content='Translate this sentence from English to French. I love programming.')]
-#     ans = chat(messages)
-#     print(type(ans), ans)

test_requirements.txt

@@ -0,0 +1,15 @@
+langchain==0.0.230
+unstructured
+pexpect
+pdf2image
+SQLAlchemy>=2.0.15
+psycopg2-binary
+openai
+gradio>=3.30.0
+fastapi
+uvicorn
+towhee>=1.1.0
+pymilvus
+elasticsearch>=8.0.0
+prometheus-client
+erniebot

tests/unit_tests/src_langchain/llm/test_ernie.py

@@ -2,28 +2,44 @@
 import sys
 import unittest
 from unittest.mock import patch
-
-from langchain.schema import HumanMessage
-from requests import Response
+from langchain.schema import HumanMessage, AIMessage
 
 sys.path.append(os.path.join(os.path.dirname(__file__), '../../../../..'))
 
 
 class TestERNIE(unittest.TestCase):
     def test_generate(self):
-        with patch('requests.post') as mock_post, patch('requests.request') as mock_request:
-            mock_res1 = Response()
-            mock_res1._content = b'{ "access_token" : "mock_token" }'
-            mock_res2 = Response()
-            mock_res2._content = b'{ "result" : "mock answer", "usage" : 2 }'
-            mock_post.return_value = mock_res1
-            mock_request.return_value = mock_res2
+        from erniebot.response import EBResponse
+        with patch('erniebot.ChatCompletion.create') as mock_post:
+            mock_res = EBResponse(code=200,
+                                  body={'id': 'as-0000000000', 'object': 'chat.completion', 'created': 11111111,
+                                        'result': 'OK, this is a mock answer.',
+                                        'usage': {'prompt_tokens': 1, 'completion_tokens': 13, 'total_tokens': 14},
+                                        'need_clear_history': False, 'is_truncated': False},
+                                  headers={'Connection': 'keep-alive',
+                                           'Content-Security-Policy': 'frame-ancestors https://*.baidu.com/',
+                                           'Content-Type': 'application/json', 'Date': 'Mon, 23 Oct 2023 03:30:53 GMT',
+                                           'Server': 'nginx', 'Statement': 'AI-generated',
+                                           'Vary': 'Origin, Access-Control-Request-Method, Access-Control-Request-Headers',
+                                           'X-Frame-Options': 'allow-from https://*.baidu.com/',
+                                           'X-Request-Id': '0' * 32,
+                                           'Transfer-Encoding': 'chunked'}
+                                  )
+            mock_post.return_value = mock_res
+
             from src_langchain.llm.ernie import ChatLLM
 
-            chat_llm = ChatLLM(api_key='mock-key', secret_key='mock-key')
-            messages = [HumanMessage(content='hello')]
+            EB_API_TYPE = 'mock_type'
+            EB_ACCESS_TOKEN = 'mock_token'
+
+            chat_llm = ChatLLM(eb_api_type=EB_API_TYPE, eb_access_token=EB_ACCESS_TOKEN)
+            messages = [
+                HumanMessage(content='hello'),
+                AIMessage(content='hello, can I help you?'),
+                HumanMessage(content='Please give me a mock answer.'),
+            ]
             res = chat_llm._generate(messages)
-            self.assertEqual(res.generations[0].text, 'mock answer')
+            self.assertEqual(res.generations[0].text, 'OK, this is a mock answer.')
 
 
 if __name__ == '__main__':