增加fastchat打字机输出

models/base/base.py

@@ -6,6 +6,7 @@ from queue import Queue
 from threading import Thread
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from models.loader import LoaderCheckPoint
+from pydantic import BaseModel
 import torch
 import transformers
 
@@ -23,13 +24,12 @@ class ListenerToken:
         self._scores = _scores
 
 
-class AnswerResult:
+class AnswerResult(BaseModel):
     """
     消息实体
     """
     history: List[List[str]] = []
     llm_output: Optional[dict] = None
-    listenerToken: ListenerToken = None
 
 
 class AnswerResultStream:
@@ -167,8 +167,6 @@ class BaseAnswer(ABC):
 
         with generate_with_streaming(inputs=inputs, run_manager=run_manager) as generator:
             for answerResult in generator:
-                if answerResult.listenerToken:
-                    output = answerResult.listenerToken.input_ids
                 yield answerResult
 
     @abstractmethod

models/chatglm_llm.py

@@ -94,8 +94,6 @@ class ChatGLMLLMChain(BaseAnswer, Chain, ABC):
                 answer_result = AnswerResult()
                 answer_result.history = history
                 answer_result.llm_output = {"answer": stream_resp}
-                if listenerQueue.listenerQueue.__len__() > 0:
-                    answer_result.listenerToken = listenerQueue.listenerQueue.pop()
                 generate_with_callback(answer_result)
             self.checkPoint.clear_torch_cache()
         else:
@@ -114,8 +112,6 @@ class ChatGLMLLMChain(BaseAnswer, Chain, ABC):
             answer_result = AnswerResult()
             answer_result.history = history
             answer_result.llm_output = {"answer": response}
-            if listenerQueue.listenerQueue.__len__() > 0:
-                answer_result.listenerToken = listenerQueue.listenerQueue.pop()
 
             generate_with_callback(answer_result)
 

models/fastchat_openai_llm.py

 from abc import ABC
 from langchain.chains.base import Chain
-from typing import Any, Dict, List, Optional, Generator, Collection
+from typing import (
+    Any, Dict, List, Optional, Generator, Collection, Set,
+    Callable,
+    Tuple,
+    Union)
+
 from models.loader import LoaderCheckPoint
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from models.base import (BaseAnswer,
@@ -8,9 +13,26 @@ from models.base import (BaseAnswer,
                          AnswerResult,
                          AnswerResultStream,
                          AnswerResultQueueSentinelTokenListenerQueue)
+from tenacity import (
+    before_sleep_log,
+    retry,
+    retry_if_exception_type,
+    stop_after_attempt,
+    wait_exponential,
+)
+from pydantic import Extra, Field, root_validator
+
+from openai import (
+    ChatCompletion
+)
+
+import openai
+import logging
 import torch
 import transformers
 
+logger = logging.getLogger(__name__)
+
 
 def _build_message_template() -> Dict[str, str]:
     """
@@ -25,12 +47,18 @@ def _build_message_template() -> Dict[str, str]:
 # 将历史对话数组转换为文本格式
 def build_message_list(query, history: List[List[str]]) -> Collection[Dict[str, str]]:
     build_messages: Collection[Dict[str, str]] = []
+
+    system_build_message = _build_message_template()
+    system_build_message['role'] = 'system'
+    system_build_message['content'] = "You are a helpful assistant."
+    build_messages.append(system_build_message)
+
     for i, (old_query, response) in enumerate(history):
         user_build_message = _build_message_template()
         user_build_message['role'] = 'user'
         user_build_message['content'] = old_query
         system_build_message = _build_message_template()
-        system_build_message['role'] = 'system'
+        system_build_message['role'] = 'assistant'
         system_build_message['content'] = response
         build_messages.append(user_build_message)
         build_messages.append(system_build_message)
@@ -43,6 +71,9 @@ def build_message_list(query, history: List[List[str]]) -> Collection[Dict[str,
 
 
 class FastChatOpenAILLMChain(RemoteRpcModel, Chain, ABC):
+    client: Any
+    """Timeout for requests to OpenAI completion API. Default is 600 seconds."""
+    max_retries: int = 6
     api_base_url: str = "http://localhost:8000/v1"
     model_name: str = "chatglm-6b"
     max_token: int = 10000
@@ -108,6 +139,35 @@ class FastChatOpenAILLMChain(RemoteRpcModel, Chain, ABC):
     def call_model_name(self, model_name):
         self.model_name = model_name
 
+    def _create_retry_decorator(self) -> Callable[[Any], Any]:
+        min_seconds = 1
+        max_seconds = 60
+        # Wait 2^x * 1 second between each retry starting with
+        # 4 seconds, then up to 10 seconds, then 10 seconds afterwards
+        return retry(
+            reraise=True,
+            stop=stop_after_attempt(self.max_retries),
+            wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
+            retry=(
+                    retry_if_exception_type(openai.error.Timeout)
+                    | retry_if_exception_type(openai.error.APIError)
+                    | retry_if_exception_type(openai.error.APIConnectionError)
+                    | retry_if_exception_type(openai.error.RateLimitError)
+                    | retry_if_exception_type(openai.error.ServiceUnavailableError)
+            ),
+            before_sleep=before_sleep_log(logger, logging.WARNING),
+        )
+
+    def completion_with_retry(self, **kwargs: Any) -> Any:
+        """Use tenacity to retry the completion call."""
+        retry_decorator = self._create_retry_decorator()
+
+        @retry_decorator
+        def _completion_with_retry(**kwargs: Any) -> Any:
+            return self.client.create(**kwargs)
+
+        return _completion_with_retry(**kwargs)
+
     def _call(
             self,
             inputs: Dict[str, Any],
@@ -124,29 +184,70 @@ class FastChatOpenAILLMChain(RemoteRpcModel, Chain, ABC):
         history = inputs[self.history_key]
         streaming = inputs[self.streaming_key]
         prompt = inputs[self.prompt_key]
+        stop = inputs['stop']
         print(f"__call:{prompt}")
         try:
 
-            import openai
             # Not support yet
             # openai.api_key = "EMPTY"
             openai.api_key = self.api_key
             openai.api_base = self.api_base_url
-        except ImportError:
+            self.client = openai.ChatCompletion
+        except AttributeError:
             raise ValueError(
-                "Could not import openai python package. "
-                "Please install it with `pip install openai`."
+                "`openai` has no `ChatCompletion` attribute, this is likely "
+                "due to an old version of the openai package. Try upgrading it "
+                "with `pip install --upgrade openai`."
             )
-        # create a chat completion
-        completion = openai.ChatCompletion.create(
-            model=self.model_name,
-            messages=build_message_list(prompt)
-        )
-        print(f"response:{completion.choices[0].message.content}")
-        print(f"+++++++++++++++++++++++++++++++++++")
-
-        history += [[prompt, completion.choices[0].message.content]]
-        answer_result = AnswerResult()
-        answer_result.history = history
-        answer_result.llm_output = {"answer": completion.choices[0].message.content}
-        generate_with_callback(answer_result)
+        msg = build_message_list(prompt, history=history)
+
+        if streaming:
+            params = {"stream": streaming,
+                      "model": self.model_name,
+                      "stop": stop}
+            for stream_resp in self.completion_with_retry(
+                    messages=msg,
+                    **params
+            ):
+                role = stream_resp["choices"][0]["delta"].get("role", "")
+                token = stream_resp["choices"][0]["delta"].get("content", "")
+                history += [[prompt, token]]
+                answer_result = AnswerResult()
+                answer_result.history = history
+                answer_result.llm_output = {"answer": token}
+                generate_with_callback(answer_result)
+        else:
+
+            params = {"stream": streaming,
+                      "model": self.model_name,
+                      "stop": stop}
+            response = self.completion_with_retry(
+                messages=msg,
+                **params
+            )
+            role = response["choices"][0]["message"].get("role", "")
+            content = response["choices"][0]["message"].get("content", "")
+            history += [[prompt, content]]
+            answer_result = AnswerResult()
+            answer_result.history = history
+            answer_result.llm_output = {"answer": content}
+            generate_with_callback(answer_result)
+
+
+if __name__ == "__main__":
+
+    chain = FastChatOpenAILLMChain()
+
+    chain.set_api_key("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")
+    chain.set_api_base_url("https://api.openai.com/v1")
+    chain.call_model_name("gpt-3.5-turbo")
+
+    answer_result_stream_result = chain({"streaming": False,
+                                         "stop": "",
+                                         "prompt": "你好",
+                                         "history": []
+                                         })
+
+    for answer_result in answer_result_stream_result['answer_result_stream']:
+        resp = answer_result.llm_output["answer"]
+        print(resp)

models/llama_llm.py

@@ -186,7 +186,5 @@ class LLamaLLMChain(BaseAnswer, Chain, ABC):
         answer_result = AnswerResult()
         history += [[prompt, reply]]
         answer_result.history = history
-        if listenerQueue.listenerQueue.__len__() > 0:
-            answer_result.listenerToken = listenerQueue.listenerQueue.pop()
         answer_result.llm_output = {"answer": reply}
         generate_with_callback(answer_result)