mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-10-03 19:57:35 +00:00
143 lines
4.4 KiB
Python
143 lines
4.4 KiB
Python
import httpx
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import uuid
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from typing import AsyncGenerator
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from ollama import AsyncClient
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from llama_models.llama3_1.api.datatypes import (
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BuiltinTool,
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CompletionMessage,
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Message,
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StopReason,
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ToolCall,
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)
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from llama_models.llama3_1.api.tool_utils import ToolUtils
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from .api.config import OllamaImplConfig
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from .api.endpoints import (
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ChatCompletionResponse,
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ChatCompletionRequest,
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ChatCompletionResponseStreamChunk,
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CompletionRequest,
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Inference,
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)
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class OllamaInference(Inference):
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def __init__(self, config: OllamaImplConfig) -> None:
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self.config = config
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self.model = config.model
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async def initialize(self) -> None:
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self.client = AsyncClient(host=self.config.url)
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try:
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status = await self.client.pull(self.model)
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assert status['status'] == 'success', f"Failed to pull model {self.model} in ollama"
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except httpx.ConnectError:
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print("Ollama Server is not running, start it using `ollama serve` in a separate terminal")
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raise
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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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ollama_messages.append(
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{"role": message.role, "content": message.content}
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)
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return ollama_messages
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async def chat_completion(self, request: ChatCompletionRequest) -> AsyncGenerator:
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if not request.stream:
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r = await self.client.chat(
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model=self.model,
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messages=self._messages_to_ollama_messages(request.messages),
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stream=False
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)
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completion_message = decode_assistant_message_from_content(
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r['message']['content']
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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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raise NotImplementedError()
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#TODO: Consolidate this with impl in llama-models
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def decode_assistant_message_from_content(content: str) -> CompletionMessage:
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ipython = content.startswith("<|python_tag|>")
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if ipython:
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content = content[len("<|python_tag|>") :]
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if content.endswith("<|eot_id|>"):
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content = content[: -len("<|eot_id|>")]
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stop_reason = StopReason.end_of_turn
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elif content.endswith("<|eom_id|>"):
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content = content[: -len("<|eom_id|>")]
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stop_reason = StopReason.end_of_message
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else:
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# Ollama does not return <|eot_id|>
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# and hence we explicitly set it as the default.
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#TODO: Check for StopReason.out_of_tokens
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stop_reason = StopReason.end_of_turn
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tool_name = None
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tool_arguments = {}
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custom_tool_info = ToolUtils.maybe_extract_custom_tool_call(content)
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if custom_tool_info is not None:
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tool_name, tool_arguments = custom_tool_info
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# Sometimes when agent has custom tools alongside builin tools
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# Agent responds for builtin tool calls in the format of the custom tools
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# This code tries to handle that case
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if tool_name in BuiltinTool.__members__:
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tool_name = BuiltinTool[tool_name]
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tool_arguments = {
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"query": list(tool_arguments.values())[0],
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}
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else:
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builtin_tool_info = ToolUtils.maybe_extract_builtin_tool_call(content)
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if builtin_tool_info is not None:
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tool_name, query = builtin_tool_info
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tool_arguments = {
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"query": query,
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}
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if tool_name in BuiltinTool.__members__:
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tool_name = BuiltinTool[tool_name]
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elif ipython:
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tool_name = BuiltinTool.code_interpreter
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tool_arguments = {
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"code": content,
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}
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tool_calls = []
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if tool_name is not None and tool_arguments is not None:
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call_id = str(uuid.uuid4())
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tool_calls.append(
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ToolCall(
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call_id=call_id,
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tool_name=tool_name,
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arguments=tool_arguments,
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)
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)
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content = ""
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if stop_reason is None:
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stop_reason = StopReason.out_of_tokens
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return CompletionMessage(
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content=content,
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stop_reason=stop_reason,
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tool_calls=tool_calls,
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)
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