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API Updates (#73)
* 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>
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213 changed files with 1725 additions and 1204 deletions
84
llama_stack/providers/utils/inference/prepare_messages.py
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llama_stack/providers/utils/inference/prepare_messages.py
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# 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 llama_models.llama3.api.datatypes import * # noqa: F403
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from llama_stack.apis.inference import * # noqa: F403
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from llama_models.llama3.prompt_templates import (
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BuiltinToolGenerator,
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FunctionTagCustomToolGenerator,
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JsonCustomToolGenerator,
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SystemDefaultGenerator,
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)
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def prepare_messages(request: ChatCompletionRequest) -> List[Message]:
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assert request.tool_choice == ToolChoice.auto, "Only `ToolChoice.auto` supported"
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existing_messages = request.messages
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existing_system_message = None
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if existing_messages[0].role == Role.system.value:
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existing_system_message = existing_messages.pop(0)
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assert (
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existing_messages[0].role != Role.system.value
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), "Should only have 1 system message"
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messages = []
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default_gen = SystemDefaultGenerator()
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default_template = default_gen.gen()
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sys_content = ""
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tool_template = None
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if request.tools:
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tool_gen = BuiltinToolGenerator()
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tool_template = tool_gen.gen(request.tools)
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sys_content += tool_template.render()
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sys_content += "\n"
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sys_content += default_template.render()
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if existing_system_message:
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# TODO: this fn is needed in many places
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def _process(c):
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if isinstance(c, str):
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return c
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else:
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return "<media>"
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sys_content += "\n"
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if isinstance(existing_system_message.content, str):
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sys_content += _process(existing_system_message.content)
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elif isinstance(existing_system_message.content, list):
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sys_content += "\n".join(
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[_process(c) for c in existing_system_message.content]
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)
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messages.append(SystemMessage(content=sys_content))
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has_custom_tools = any(isinstance(dfn.tool_name, str) for dfn in request.tools)
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if has_custom_tools:
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if request.tool_prompt_format == ToolPromptFormat.json:
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tool_gen = JsonCustomToolGenerator()
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elif request.tool_prompt_format == ToolPromptFormat.function_tag:
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tool_gen = FunctionTagCustomToolGenerator()
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else:
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raise ValueError(
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f"Non supported ToolPromptFormat {request.tool_prompt_format}"
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)
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custom_tools = [t for t in request.tools if isinstance(t.tool_name, str)]
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custom_template = tool_gen.gen(custom_tools)
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messages.append(UserMessage(content=custom_template.render()))
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# Add back existing messages from the request
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messages += existing_messages
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return messages
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