File size: 3,974 Bytes
e28221f
3a09006
e28221f
 
3a09006
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
e916990
 
 
 
 
 
 
 
 
3a09006
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6aa8b86
3a09006
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
e28221f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3a09006
 
 
e28221f
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
import argparse
import uvicorn
import sys

from fastapi import FastAPI
from pydantic import BaseModel, Field
from sse_starlette.sse import EventSourceResponse
from utils.logger import logger
from networks.message_streamer import MessageStreamer
from messagers.message_composer import MessageComposer


class ChatAPIApp:
    def __init__(self):
        self.app = FastAPI(
            docs_url="/",
            title="HuggingFace LLM API",
            swagger_ui_parameters={"defaultModelsExpandDepth": -1},
            version="1.0",
        )
        self.setup_routes()

    def get_available_models(self):
        self.available_models = [
            {
                "id": "mixtral-8x7b",
                "description": "[Mixtral-8x7B-Instruct-v0.1]: https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1",
            },
            {
                "id": "mistral-7b",
                "description": "[Mistral-7B-Instruct-v0.2]: https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2",
            },
            {
                "id": "openchat-3.5",
                "description": "[openchat_3.5]: https://huggingface.co/openchat/openchat_3.5",
            },
        ]
        return self.available_models

    class ChatCompletionsPostItem(BaseModel):
        model: str = Field(
            default="mixtral-8x7b",
            description="(str) `mixtral-8x7b`",
        )
        messages: list = Field(
            default=[{"role": "user", "content": "Hello, who are you?"}],
            description="(list) Messages",
        )
        temperature: float = Field(
            default=0.01,
            description="(float) Temperature",
        )
        max_tokens: int = Field(
            default=8192,
            description="(int) Max tokens",
        )
        stream: bool = Field(
            default=True,
            description="(bool) Stream",
        )

    def chat_completions(self, item: ChatCompletionsPostItem):
        streamer = MessageStreamer(model=item.model)
        composer = MessageComposer(model=item.model)
        composer.merge(messages=item.messages)
        return EventSourceResponse(
            streamer.chat(
                prompt=composer.merged_str,
                temperature=item.temperature,
                max_new_tokens=item.max_tokens,
                stream=item.stream,
                yield_output=True,
            ),
            media_type="text/event-stream",
        )

    def setup_routes(self):
        for prefix in ["", "/v1"]:
            self.app.get(
                prefix + "/models",
                summary="Get available models",
            )(self.get_available_models)

            self.app.post(
                prefix + "/chat/completions",
                summary="Chat completions in conversation session",
            )(self.chat_completions)


class ArgParser(argparse.ArgumentParser):
    def __init__(self, *args, **kwargs):
        super(ArgParser, self).__init__(*args, **kwargs)

        self.add_argument(
            "-s",
            "--server",
            type=str,
            default="0.0.0.0",
            help="Server IP for HF LLM Chat API",
        )
        self.add_argument(
            "-p",
            "--port",
            type=int,
            default=23333,
            help="Server Port for HF LLM Chat API",
        )

        self.add_argument(
            "-d",
            "--dev",
            default=False,
            action="store_true",
            help="Run in dev mode",
        )

        self.args = self.parse_args(sys.argv[1:])


app = ChatAPIApp().app

if __name__ == "__main__":
    args = ArgParser().args
    if args.dev:
        uvicorn.run("__main__:app", host=args.server, port=args.port, reload=True)
    else:
        uvicorn.run("__main__:app", host=args.server, port=args.port, reload=False)

    # python -m apis.chat_api      # [Docker] on product mode
    # python -m apis.chat_api -d   # [Dev]    on develop mode