forked from phoenix-oss/llama-stack-mirror
5712566061
* [1/n] migrate inference/chat_completion * migrate inference/completion * inference/completion * inference regenerate openapi spec * safety api * migrate agentic system * migrate apis without implementations * re-generate openapi spec * remove hack from openapi generator * fix inference * fix inference * openapi generator rerun * Simplified Telemetry API and tying it to logger (#57) * Simplified Telemetry API and tying it to logger * small update which adds a METRIC type * move span events one level down into structured log events --------- Co-authored-by: Ashwin Bharambe <ashwin@meta.com> * fix api to work with openapi generator * fix agentic calling inference * together adapter inference * update inference adapters --------- Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com> Co-authored-by: Ashwin Bharambe <ashwin@meta.com>
106 lines
3.2 KiB
Python
106 lines
3.2 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import asyncio
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import json
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from typing import Any, AsyncGenerator
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import fire
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import httpx
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from llama_toolchain.core.datatypes import RemoteProviderConfig
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from pydantic import BaseModel
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from termcolor import cprint
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from .api import (
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ChatCompletionRequest,
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ChatCompletionResponse,
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ChatCompletionResponseStreamChunk,
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CompletionRequest,
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Inference,
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UserMessage,
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)
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from .event_logger import EventLogger
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async def get_client_impl(config: RemoteProviderConfig, _deps: Any) -> Inference:
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return InferenceClient(config.url)
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def encodable_dict(d: BaseModel):
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return json.loads(d.json())
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class InferenceClient(Inference):
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def __init__(self, base_url: str):
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self.base_url = base_url
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async def initialize(self) -> None:
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pass
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async def shutdown(self) -> None:
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pass
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async def completion(self, request: CompletionRequest) -> AsyncGenerator:
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raise NotImplementedError()
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async def chat_completion(self, request: ChatCompletionRequest) -> AsyncGenerator:
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async with httpx.AsyncClient() as client:
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async with client.stream(
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"POST",
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f"{self.base_url}/inference/chat_completion",
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json=encodable_dict(request),
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headers={"Content-Type": "application/json"},
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timeout=20,
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) as response:
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if response.status_code != 200:
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content = await response.aread()
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cprint(
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f"Error: HTTP {response.status_code} {content.decode()}", "red"
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)
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return
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async for line in response.aiter_lines():
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if line.startswith("data:"):
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data = line[len("data: ") :]
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try:
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if request.stream:
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if "error" in data:
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cprint(data, "red")
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continue
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yield ChatCompletionResponseStreamChunk(
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**json.loads(data)
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)
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else:
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yield ChatCompletionResponse(**json.loads(data))
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except Exception as e:
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print(data)
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print(f"Error with parsing or validation: {e}")
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async def run_main(host: str, port: int, stream: bool):
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client = InferenceClient(f"http://{host}:{port}")
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message = UserMessage(content="hello world, troll me in two-paragraphs about 42")
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cprint(f"User>{message.content}", "green")
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iterator = client.chat_completion(
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ChatCompletionRequest(
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model="Meta-Llama3.1-8B-Instruct",
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messages=[message],
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stream=stream,
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)
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)
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async for log in EventLogger().log(iterator):
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log.print()
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def main(host: str, port: int, stream: bool = True):
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asyncio.run(run_main(host, port, stream))
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if __name__ == "__main__":
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fire.Fire(main)
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