forked from phoenix-oss/llama-stack-mirror
ec4fc800cc
This is yet another of those large PRs (hopefully we will have less and less of them as things mature fast). This one introduces substantial improvements and some simplifications to the stack. Most important bits: * Agents reference implementation now has support for session / turn persistence. The default implementation uses sqlite but there's also support for using Redis. * We have re-architected the structure of the Stack APIs to allow for more flexible routing. The motivating use cases are: - routing model A to ollama and model B to a remote provider like Together - routing shield A to local impl while shield B to a remote provider like Bedrock - routing a vector memory bank to Weaviate while routing a keyvalue memory bank to Redis * Support for provider specific parameters to be passed from the clients. A client can pass data using `x_llamastack_provider_data` parameter which can be type-checked and provided to the Adapter implementations.
121 lines
3.9 KiB
Python
121 lines
3.9 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, List, Optional
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import fire
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import httpx
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from pydantic import BaseModel
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from llama_models.llama3.api import * # noqa: F403
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from llama_stack.apis.inference import * # noqa: F403
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from termcolor import cprint
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from llama_stack.distribution.datatypes import RemoteProviderConfig
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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(
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self,
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model: str,
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messages: List[Message],
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sampling_params: Optional[SamplingParams] = SamplingParams(),
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tools: Optional[List[ToolDefinition]] = None,
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tool_choice: Optional[ToolChoice] = ToolChoice.auto,
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tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
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stream: Optional[bool] = False,
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logprobs: Optional[LogProbConfig] = None,
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) -> AsyncGenerator:
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request = ChatCompletionRequest(
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model=model,
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messages=messages,
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sampling_params=sampling_params,
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tools=tools or [],
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tool_choice=tool_choice,
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tool_prompt_format=tool_prompt_format,
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stream=stream,
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logprobs=logprobs,
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)
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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(
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content="hello world, write me a 2 sentence poem about the moon"
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)
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cprint(f"User>{message.content}", "green")
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iterator = client.chat_completion(
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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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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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