Update the operator.

Signed-off-by: jinlingxu06 <jinling.xu@zilliz.com>

README.md

@@ -1,2 +1,135 @@
-# Azure-OpenAI
+# OpenAI Chat Completion
+
+*author: David Wang*
+
+<br />
+
+## Description
+
+A LLM operator generates answer given prompt in messages using a large language model or service.
+This operator is implemented with Chat Completion method from [Azure OpenAI](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/chatgpt?pivots=programming-language-chat-completions).
+Please note you need an [OpenAI API key](https://platform.openai.com/account/api-keys) to access OpenAI.
+
+<br />
+
+## Code Example
+
+Use the default model to continue the conversation from given messages.
+
+*Write a pipeline with explicit inputs/outputs name specifications:*
+
+```python
+from towhee import pipe, ops
+
+p = (
+    pipe.input('messages')
+        .map('messages', 'answer', ops.LLM.Azure_OpenAI(api_key=OPENAI_API_KEY, api_base=OPENAI_API_BASE))
+        .output('messages', 'answer')
+)
+
+messages=[
+        {'question': 'Who won the world series in 2020?', 'answer': 'The Los Angeles Dodgers won the World Series in 2020.'},
+        {'question': 'Where was it played?'}
+    ]
+answer = p(messages).get()[0]
+```
+
+*Write a [retrieval-augmented generation pipeline](https://towhee.io/tasks/detail/pipeline/retrieval-augmented-generation) with explicit inputs/outputs name specifications:*
+
+```python
+from towhee import pipe, ops
+
+
+temp = '''Use the following pieces of context to answer the question at the end.
+If you don't know the answer, just say that you don't know, don't try to make up an answer.
+
+{context}
+
+Question: {question}
+
+Helpful Answer:
+'''
+
+
+docs = ['You can install towhee via command `pip install towhee`.']
+history = [
+    ('What is Towhee?', 'Towhee is an open-source machine learning pipeline that helps you encode your unstructured data into embeddings.')
+]
+question = 'How to install it?'
+
+p = (
+    pipe.input('question', 'docs', 'history')
+        .map(('question', 'docs', 'history'), 'prompt', ops.prompt.template(temp, ['question', 'context']))
+        .map('prompt', 'answer',
+             ops.LLM.Azure_OpenAI(api_key=OPENAI_API_KEY, api_base=OPENAI_API_BASE, temperature=0.5, max_tokens=100)
+             )
+        .output('answer')
+)
+
+answer = p(question, docs, history).get()[0]
+```
+
+<br />
+
+## Factory Constructor
+
+Create the operator via the following factory method:
+
+***LLM.OpenAI(model_name: str, api_key: str)***
+
+**Parameters:**
+
+***model_name***: *str*
+
+The model name in string, defaults to 'gpt-3.5-turbo'. Supported model names:
+- gpt-3.5-turbo
+- gpt-3.5-turbo-16k
+- gpt-3.5-turbo-instruct
+- gpt-3.5-turbo-0613
+- gpt-3.5-turbo-16k-0613
+
+***api_type***: *str=None*
+
+The OpenAI API type in string, defaults to None.
+
+***api_version***: *str=None*
+
+The OpenAI API version in string, defaults to None.
+
+***api_key***: *str=None*
+
+The OpenAI API key in string, defaults to None.
+
+***api_base***: *str=None*
+
+The OpenAI API base in string, defaults to None.
+
+***\*\*kwargs***
+
+Other OpenAI parameters such as max_tokens, stream, temperature, etc.
+
+<br />
+
+## Interface
+
+The operator takes a piece of text in string as input.
+It returns answer in json.
+
+***\_\_call\_\_(txt)***
+
+**Parameters:**
+
+***messages***: *list*
+
+​	A list of messages to set up chat.
+Must be a list of dictionaries with key value from "system", "question", "answer". For example, [{"question": "a past question?", "answer": "a past answer."}, {"question": "current question?"}]
+
+**Returns**:
+
+*answer: str*
+
+​	The next answer generated by role "assistant".
+
+<br />
+
 

__init__.py

@@ -0,0 +1,5 @@
+from .azure_openai_chat import AzureOpenaiChat
+
+
+def AzureOpenAI(*args, **kwargs):
+    return AzureOpenaiChat(*args, **kwargs)

azure_openai_chat.py

@@ -0,0 +1,87 @@
+# Copyright 2021 Zilliz. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+from typing import List
+
+import openai
+from towhee.operator.base import PyOperator
+
+class AzureOpenaiChat(PyOperator):
+    '''Wrapper of OpenAI Chat API'''
+    def __init__(self,
+                 model_name: str = 'gpt-3.5-turbo',
+                 api_type: str = 'azure', 
+                 api_version: str = '2023-07-01-preview',
+                 api_key: str = None,
+                 api_base = None,
+                 **kwargs
+                 ):
+        openai.api_key = api_key or os.getenv('OPENAI_API_KEY')
+        openai.api_base = api_base or os.getenv('OPENAI_API_BASE')
+        self._model = model_name
+        self.stream = kwargs.pop('stream') if 'stream' in kwargs else False
+        self.kwargs = kwargs
+
+    def __call__(self, messages: List[dict]):
+        messages = self.parse_inputs(messages)
+        response = openai.ChatCompletion.create(
+            model=self._model,
+            messages=messages,
+            n=1,
+            stream=self.stream,
+            **self.kwargs
+        )
+        if self.stream:
+            return self.stream_output(response)
+        else:
+            answer = response['choices'][0]['message']['content']
+            return answer
+
+    def parse_inputs(self, messages: List[dict]):
+        assert isinstance(messages, list), \
+            'Inputs must be a list of dictionaries with keys from ["system", "question", "answer"].'
+        new_messages = []
+        for m in messages:
+            if ('role' and 'content' in m) and (m['role'] in ['system', 'assistant', 'user']):
+                new_messages.append(m)
+            else:
+                for k, v in m.items():
+                    if k == 'question':
+                        new_m = {'role': 'user', 'content': v}
+                    elif k == 'answer':
+                        new_m = {'role': 'assistant', 'content': v}
+                    elif k == 'system':
+                        new_m = {'role': 'system', 'content': v}
+                    else:
+                        raise KeyError('Invalid message key: only accept key value from ["system", "question", "answer"].')
+                    new_messages.append(new_m)
+        return new_messages
+    
+    def stream_output(self, response):
+        for resp in response:
+            yield resp['choices'][0]['delta']
+
+    @staticmethod
+    def supported_model_names():
+        model_list = [
+            'gpt-3.5-turbo',
+            'gpt-3.5-turbo-16k',
+            'gpt-3.5-turbo-instruct',
+            'gpt-3.5-turbo-0613',
+            'gpt-3.5-turbo-16k-0613'
+        ]
+        model_list.sort()
+        return model_list
+