chore: 修改包名

pkg/provider/modelmgr.py

@@ -0,0 +1,139 @@
+"""OpenAI 接口底层封装
+
+目前使用的对话接口有：
+ChatCompletion - gpt-3.5-turbo 等模型
+Completion - text-davinci-003 等模型
+此模块封装此两个接口的请求实现，为上层提供统一的调用方式
+"""
+import tiktoken
+import openai
+
+from ..provider.api import model as api_model
+from ..provider.api import completion as api_completion
+from ..provider.api import chat_completion as api_chat_completion
+
+COMPLETION_MODELS = {
+    "gpt-3.5-turbo-instruct",
+}
+
+CHAT_COMPLETION_MODELS = {
+    # GPT 4 系列
+    "gpt-4-1106-preview",
+    "gpt-4-vision-preview",
+    "gpt-4",
+    "gpt-4-32k",
+    "gpt-4-0613",
+    "gpt-4-32k-0613",
+    "gpt-4-0314", # legacy
+    "gpt-4-32k-0314", # legacy
+    # GPT 3.5 系列
+    "gpt-3.5-turbo-1106",
+    "gpt-3.5-turbo",
+    "gpt-3.5-turbo-16k",
+    "gpt-3.5-turbo-0613", # legacy
+    "gpt-3.5-turbo-16k-0613", # legacy
+    "gpt-3.5-turbo-0301", # legacy
+    # One-API 接入
+    "SparkDesk",
+    "chatglm_pro",
+    "chatglm_std",
+    "chatglm_lite",
+    "qwen-v1",
+    "qwen-plus-v1",
+    "ERNIE-Bot",
+    "ERNIE-Bot-turbo",
+    "gemini-pro",
+}
+
+EDIT_MODELS = {
+
+}
+
+IMAGE_MODELS = {
+
+}
+
+
+def select_request_cls(client: openai.Client, model_name: str, messages: list, args: dict) -> api_model.RequestBase:
+    if model_name in CHAT_COMPLETION_MODELS:
+        return api_chat_completion.ChatCompletionRequest(client, model_name, messages, **args)
+    elif model_name in COMPLETION_MODELS:
+        return api_completion.CompletionRequest(client, model_name, messages, **args)
+    raise ValueError("不支持模型[{}]，请检查配置文件".format(model_name))
+
+
+def count_chat_completion_tokens(messages: list, model: str) -> int:
+    """Return the number of tokens used by a list of messages."""
+    try:
+        encoding = tiktoken.encoding_for_model(model)
+    except KeyError:
+        print("Warning: model not found. Using cl100k_base encoding.")
+        encoding = tiktoken.get_encoding("cl100k_base")
+    if model in {
+        "gpt-3.5-turbo-0613",
+        "gpt-3.5-turbo-16k-0613",
+        "gpt-4-0314",
+        "gpt-4-32k-0314",
+        "gpt-4-0613",
+        "gpt-4-32k-0613",
+        "SparkDesk",
+        "chatglm_pro",
+        "chatglm_std",
+        "chatglm_lite",
+        "qwen-v1",
+        "qwen-plus-v1",
+        "ERNIE-Bot",
+        "ERNIE-Bot-turbo",
+        "gemini-pro",
+        }:
+        tokens_per_message = 3
+        tokens_per_name = 1
+    elif model == "gpt-3.5-turbo-0301":
+        tokens_per_message = 4  # every message follows <|start|>{role/name}\n{content}<|end|>\n
+        tokens_per_name = -1  # if there's a name, the role is omitted
+    elif "gpt-3.5-turbo" in model:
+        # print("Warning: gpt-3.5-turbo may update over time. Returning num tokens assuming gpt-3.5-turbo-0613.")
+        return count_chat_completion_tokens(messages, model="gpt-3.5-turbo-0613")
+    elif "gpt-4" in model:
+        # print("Warning: gpt-4 may update over time. Returning num tokens assuming gpt-4-0613.")
+        return count_chat_completion_tokens(messages, model="gpt-4-0613")
+    else:
+        raise NotImplementedError(
+            f"""count_chat_completion_tokens() is not implemented for model {model}. See https://github.com/openai/openai-python/blob/main/chatml.md for information on how messages are converted to tokens."""
+        )
+    num_tokens = 0
+    for message in messages:
+        num_tokens += tokens_per_message
+        for key, value in message.items():
+            num_tokens += len(encoding.encode(value))
+            if key == "name":
+                num_tokens += tokens_per_name
+    num_tokens += 3  # every reply is primed with <|start|>assistant<|message|>
+    return num_tokens
+
+
+def count_completion_tokens(messages: list, model: str) -> int:
+
+    try:
+        encoding = tiktoken.encoding_for_model(model)
+    except KeyError:
+        print("Warning: model not found. Using cl100k_base encoding.")
+        encoding = tiktoken.get_encoding("cl100k_base")
+
+    text = ""
+
+    for message in messages:
+        text += message['role'] + message['content'] + "\n"
+
+    text += "assistant: "
+
+    return len(encoding.encode(text))
+
+
+def count_tokens(messages: list, model: str):
+
+    if model in CHAT_COMPLETION_MODELS:
+        return count_chat_completion_tokens(messages, model)
+    elif model in COMPLETION_MODELS:
+        return count_completion_tokens(messages, model)
+    raise ValueError("不支持模型[{}]，请检查配置文件".format(model))