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* API Keys passed from Client instead of distro configuration * delete distribution registry * Rename the "package" word away * Introduce a "Router" layer for providers Some providers need to be factorized and considered as thin routing layers on top of other providers. Consider two examples: - The inference API should be a routing layer over inference providers, routed using the "model" key - The memory banks API is another instance where various memory bank types will be provided by independent providers (e.g., a vector store is served by Chroma while a keyvalue memory can be served by Redis or PGVector) This commit introduces a generalized routing layer for this purpose. * update `apis_to_serve` * llama_toolchain -> llama_stack * Codemod from llama_toolchain -> llama_stack - added providers/registry - cleaned up api/ subdirectories and moved impls away - restructured api/api.py - from llama_stack.apis.<api> import foo should work now - update imports to do llama_stack.apis.<api> - update many other imports - added __init__, fixed some registry imports - updated registry imports - create_agentic_system -> create_agent - AgenticSystem -> Agent * Moved some stuff out of common/; re-generated OpenAPI spec * llama-toolchain -> llama-stack (hyphens) * add control plane API * add redis adapter + sqlite provider * move core -> distribution * Some more toolchain -> stack changes * small naming shenanigans * Removing custom tool and agent utilities and moving them client side * Move control plane to distribution server for now * Remove control plane from API list * no codeshield dependency randomly plzzzzz * Add "fire" as a dependency * add back event loggers * stack configure fixes * use brave instead of bing in the example client * add init file so it gets packaged * add init files so it gets packaged * Update MANIFEST * bug fix --------- Co-authored-by: Hardik Shah <hjshah@fb.com> Co-authored-by: Xi Yan <xiyan@meta.com> Co-authored-by: Ashwin Bharambe <ashwin@meta.com>
184 lines
7 KiB
Python
184 lines
7 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 Optional
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from llama_models.llama3.api.datatypes import * # noqa: F403
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from llama_models.llama3.api.tool_utils import ToolUtils
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from llama_stack.apis.agents import AgentTurnResponseEventType, StepType
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from termcolor import cprint
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class LogEvent:
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def __init__(
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self,
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role: Optional[str] = None,
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content: str = "",
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end: str = "\n",
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color="white",
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):
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self.role = role
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self.content = content
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self.color = color
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self.end = "\n" if end is None else end
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def __str__(self):
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if self.role is not None:
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return f"{self.role}> {self.content}"
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else:
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return f"{self.content}"
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def print(self, flush=True):
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cprint(f"{str(self)}", color=self.color, end=self.end, flush=flush)
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EventType = AgentTurnResponseEventType
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class EventLogger:
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async def log(
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self,
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event_generator,
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stream=True,
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tool_prompt_format: ToolPromptFormat = ToolPromptFormat.json,
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):
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previous_event_type = None
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previous_step_type = None
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async for chunk in event_generator:
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if not hasattr(chunk, "event"):
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# Need to check for custom tool first
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# since it does not produce event but instead
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# a Message
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if isinstance(chunk, ToolResponseMessage):
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yield chunk, LogEvent(
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role="CustomTool", content=chunk.content, color="grey"
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)
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continue
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event = chunk.event
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event_type = event.payload.event_type
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if event_type in {
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EventType.turn_start.value,
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EventType.turn_complete.value,
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}:
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# Currently not logging any turn realted info
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yield event, None
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continue
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step_type = event.payload.step_type
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# handle safety
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if (
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step_type == StepType.shield_call
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and event_type == EventType.step_complete.value
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):
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response = event.payload.step_details.response
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if not response.is_violation:
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yield event, LogEvent(
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role=step_type, content="No Violation", color="magenta"
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)
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else:
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yield event, LogEvent(
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role=step_type,
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content=f"{response.violation_type} {response.violation_return_message}",
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color="red",
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)
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# handle inference
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if step_type == StepType.inference:
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if stream:
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if event_type == EventType.step_start.value:
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# TODO: Currently this event is never received
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yield event, LogEvent(
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role=step_type, content="", end="", color="yellow"
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)
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elif event_type == EventType.step_progress.value:
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# HACK: if previous was not step/event was not inference's step_progress
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# this is the first time we are getting model inference response
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# aka equivalent to step_start for inference. Hence,
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# start with "Model>".
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if (
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previous_event_type != EventType.step_progress.value
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and previous_step_type != StepType.inference
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):
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yield event, LogEvent(
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role=step_type, content="", end="", color="yellow"
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)
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if event.payload.tool_call_delta:
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if isinstance(event.payload.tool_call_delta.content, str):
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yield event, LogEvent(
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role=None,
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content=event.payload.tool_call_delta.content,
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end="",
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color="cyan",
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)
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else:
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yield event, LogEvent(
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role=None,
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content=event.payload.model_response_text_delta,
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end="",
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color="yellow",
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)
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else:
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# step_complete
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yield event, LogEvent(role=None, content="")
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else:
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# Not streaming
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if event_type == EventType.step_complete.value:
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response = event.payload.step_details.model_response
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if response.tool_calls:
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content = ToolUtils.encode_tool_call(
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response.tool_calls[0], tool_prompt_format
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)
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else:
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content = response.content
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yield event, LogEvent(
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role=step_type,
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content=content,
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color="yellow",
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)
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# handle tool_execution
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if (
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step_type == StepType.tool_execution
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and
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# Only print tool calls and responses at the step_complete event
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event_type == EventType.step_complete.value
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):
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details = event.payload.step_details
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for t in details.tool_calls:
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yield event, LogEvent(
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role=step_type,
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content=f"Tool:{t.tool_name} Args:{t.arguments}",
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color="green",
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)
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for r in details.tool_responses:
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yield event, LogEvent(
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role=step_type,
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content=f"Tool:{r.tool_name} Response:{r.content}",
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color="green",
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)
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if (
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step_type == StepType.memory_retrieval
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and event_type == EventType.step_complete.value
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):
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details = event.payload.step_details
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content = interleaved_text_media_as_str(details.inserted_context)
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content = content[:200] + "..." if len(content) > 200 else content
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yield event, LogEvent(
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role=step_type,
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content=f"Retrieved context from banks: {details.memory_bank_ids}.\n====\n{content}\n>",
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color="cyan",
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)
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preivous_event_type = event_type
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previous_step_type = step_type
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