mirror of
https://github.com/meta-llama/llama-stack.git
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277 lines
11 KiB
Python
277 lines
11 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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from typing import AsyncGenerator
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import httpx
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from llama_models.llama3.api.chat_format import ChatFormat
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from llama_models.llama3.api.datatypes import Message, StopReason
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from llama_models.llama3.api.tokenizer import Tokenizer
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from llama_models.sku_list import resolve_model
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from ollama import AsyncClient
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from llama_stack.apis.inference import * # noqa: F403
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from llama_stack.providers.utils.inference.prepare_messages import prepare_messages
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from termcolor import cprint
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# TODO: Eventually this will move to the llama cli model list command
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# mapping of Model SKUs to ollama models
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OLLAMA_SUPPORTED_SKUS = {
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# "Meta-Llama3.1-8B-Instruct": "llama3.1",
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"Meta-Llama3.1-8B-Instruct": "llama3.1:8b-instruct-fp16",
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"Meta-Llama3.1-70B-Instruct": "llama3.1:70b-instruct-fp16",
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}
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class OllamaInferenceAdapter(Inference):
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def __init__(self, url: str) -> None:
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self.url = url
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tokenizer = Tokenizer.get_instance()
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self.formatter = ChatFormat(tokenizer)
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@property
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def client(self) -> AsyncClient:
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return AsyncClient(host=self.url)
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async def initialize(self) -> None:
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pass
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# try:
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# await self.client.ps()
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# except httpx.ConnectError as e:
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# raise RuntimeError(
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# "Ollama Server is not running, start it using `ollama serve` in a separate terminal"
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# ) from e
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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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def _messages_to_ollama_messages(self, messages: list[Message]) -> list:
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ollama_messages = []
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for message in messages:
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if message.role == "ipython":
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role = "tool"
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else:
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role = message.role
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ollama_messages.append({"role": role, "content": message.content})
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return ollama_messages
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def resolve_ollama_model(self, model_name: str) -> str:
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model = resolve_model(model_name)
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assert (
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model is not None
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and model.descriptor(shorten_default_variant=True) in OLLAMA_SUPPORTED_SKUS
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), f"Unsupported model: {model_name}, use one of the supported models: {','.join(OLLAMA_SUPPORTED_SKUS.keys())}"
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return OLLAMA_SUPPORTED_SKUS.get(model.descriptor(shorten_default_variant=True))
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def get_ollama_chat_options(self, request: ChatCompletionRequest) -> dict:
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options = {}
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if request.sampling_params is not None:
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for attr in {"temperature", "top_p", "top_k", "max_tokens"}:
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if getattr(request.sampling_params, attr):
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options[attr] = getattr(request.sampling_params, attr)
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if (
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request.sampling_params.repetition_penalty is not None
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and request.sampling_params.repetition_penalty != 1.0
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):
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options["repeat_penalty"] = request.sampling_params.repetition_penalty
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return options
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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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cprint("!! calling remote ollama {}, url={}!!".format(model, self.url), "red")
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.start,
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delta="",
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)
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)
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.progress,
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delta="model={}, url={}".format(model, self.url),
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)
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)
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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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# messages = prepare_messages(request)
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# # accumulate sampling params and other options to pass to ollama
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# options = self.get_ollama_chat_options(request)
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# ollama_model = self.resolve_ollama_model(request.model)
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# res = await self.client.ps()
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# need_model_pull = True
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# for r in res["models"]:
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# if ollama_model == r["model"]:
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# need_model_pull = False
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# break
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# if need_model_pull:
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# print(f"Pulling model: {ollama_model}")
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# status = await self.client.pull(ollama_model)
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# assert (
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# status["status"] == "success"
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# ), f"Failed to pull model {self.model} in ollama"
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# if not request.stream:
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# r = await self.client.chat(
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# model=ollama_model,
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# messages=self._messages_to_ollama_messages(messages),
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# stream=False,
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# options=options,
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# )
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# stop_reason = None
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# if r["done"]:
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# if r["done_reason"] == "stop":
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# stop_reason = StopReason.end_of_turn
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# elif r["done_reason"] == "length":
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# stop_reason = StopReason.out_of_tokens
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# completion_message = self.formatter.decode_assistant_message_from_content(
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# r["message"]["content"], stop_reason
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# )
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# yield ChatCompletionResponse(
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# completion_message=completion_message,
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# logprobs=None,
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# )
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# else:
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# yield ChatCompletionResponseStreamChunk(
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# event=ChatCompletionResponseEvent(
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# event_type=ChatCompletionResponseEventType.start,
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# delta="",
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# )
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# )
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# stream = await self.client.chat(
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# model=ollama_model,
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# messages=self._messages_to_ollama_messages(messages),
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# stream=True,
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# options=options,
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# )
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# buffer = ""
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# ipython = False
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# stop_reason = None
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# async for chunk in stream:
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# if chunk["done"]:
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# if stop_reason is None and chunk["done_reason"] == "stop":
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# stop_reason = StopReason.end_of_turn
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# elif stop_reason is None and chunk["done_reason"] == "length":
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# stop_reason = StopReason.out_of_tokens
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# break
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# text = chunk["message"]["content"]
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# # check if its a tool call ( aka starts with <|python_tag|> )
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# if not ipython and text.startswith("<|python_tag|>"):
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# ipython = True
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# yield ChatCompletionResponseStreamChunk(
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# event=ChatCompletionResponseEvent(
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# event_type=ChatCompletionResponseEventType.progress,
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# delta=ToolCallDelta(
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# content="",
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# parse_status=ToolCallParseStatus.started,
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# ),
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# )
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# )
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# buffer += text
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# continue
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# if ipython:
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# if text == "<|eot_id|>":
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# stop_reason = StopReason.end_of_turn
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# text = ""
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# continue
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# elif text == "<|eom_id|>":
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# stop_reason = StopReason.end_of_message
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# text = ""
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# continue
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# buffer += text
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# delta = ToolCallDelta(
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# content=text,
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# parse_status=ToolCallParseStatus.in_progress,
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# )
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# yield ChatCompletionResponseStreamChunk(
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# event=ChatCompletionResponseEvent(
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# event_type=ChatCompletionResponseEventType.progress,
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# delta=delta,
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# stop_reason=stop_reason,
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# )
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# )
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# else:
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# buffer += text
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# yield ChatCompletionResponseStreamChunk(
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# event=ChatCompletionResponseEvent(
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# event_type=ChatCompletionResponseEventType.progress,
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# delta=text,
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# stop_reason=stop_reason,
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# )
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# )
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# # parse tool calls and report errors
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# message = self.formatter.decode_assistant_message_from_content(
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# buffer, stop_reason
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# )
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# parsed_tool_calls = len(message.tool_calls) > 0
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# if ipython and not parsed_tool_calls:
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# yield ChatCompletionResponseStreamChunk(
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# event=ChatCompletionResponseEvent(
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# event_type=ChatCompletionResponseEventType.progress,
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# delta=ToolCallDelta(
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# content="",
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# parse_status=ToolCallParseStatus.failure,
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# ),
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# stop_reason=stop_reason,
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# )
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# )
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# for tool_call in message.tool_calls:
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# yield ChatCompletionResponseStreamChunk(
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# event=ChatCompletionResponseEvent(
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# event_type=ChatCompletionResponseEventType.progress,
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# delta=ToolCallDelta(
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# content=tool_call,
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# parse_status=ToolCallParseStatus.success,
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# ),
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# stop_reason=stop_reason,
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# )
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# )
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# yield ChatCompletionResponseStreamChunk(
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# event=ChatCompletionResponseEvent(
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# event_type=ChatCompletionResponseEventType.complete,
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# delta="",
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# stop_reason=stop_reason,
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# )
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# )
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