llama-stack/llama_toolchain/agentic_system/meta_reference/system_prompt.py
Ashwin Bharambe e830814399
Introduce Llama stack distributions (#22)
* Add distribution CLI scaffolding

* More progress towards `llama distribution install`

* getting closer to a distro definition, distro install + configure works

* Distribution server now functioning

* read existing configuration, save enums properly

* Remove inference uvicorn server entrypoint and llama inference CLI command

* updated dependency and client model name

* Improved exception handling

* local imports for faster cli

* undo a typo, add a passthrough distribution

* implement full-passthrough in the server

* add safety adapters, configuration handling, server + clients

* cleanup, moving stuff to common, nuke utils

* Add a Path() wrapper at the earliest place

* fixes

* Bring agentic system api to toolchain

Add adapter dependencies and resolve adapters using a topological sort

* refactor to reduce size of `agentic_system`

* move straggler files and fix some important existing bugs

* ApiSurface -> Api

* refactor a method out

* Adapter -> Provider

* Make each inference provider into its own subdirectory

* installation fixes

* Rename Distribution -> DistributionSpec, simplify RemoteProviders

* dict key instead of attr

* update inference config to take model and not model_dir

* Fix passthrough streaming, send headers properly not part of body :facepalm

* update safety to use model sku ids and not model dirs

* Update cli_reference.md

* minor fixes

* add DistributionConfig, fix a bug in model download

* Make install + start scripts do proper configuration automatically

* Update CLI_reference

* Nuke fp8_requirements, fold fbgemm into common requirements

* Update README, add newline between API surface configurations

* Refactor download functionality out of the Command so can be reused

* Add `llama model download` alias for `llama download`

* Show message about checksum file so users can check themselves

* Simpler intro statements

* get ollama working

* Reduce a bunch of dependencies from toolchain

Some improvements to the distribution install script

* Avoid using `conda run` since it buffers everything

* update dependencies and rely on LLAMA_TOOLCHAIN_DIR for dev purposes

* add validation for configuration input

* resort imports

* make optional subclasses default to yes for configuration

* Remove additional_pip_packages; move deps to providers

* for inline make 8b model the default

* Add scripts to MANIFEST

* allow installing from test.pypi.org

* Fix #2 to help with testing packages

* Must install llama-models at that same version first

* fix PIP_ARGS

---------

Co-authored-by: Hardik Shah <hjshah@fb.com>
Co-authored-by: Hardik Shah <hjshah@meta.com>
2024-08-08 13:38:41 -07:00

152 lines
4.6 KiB
Python

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import json
from datetime import datetime
from typing import List
from llama_toolchain.inference.api import (
BuiltinTool,
Message,
SystemMessage,
ToolDefinition,
)
from .tools.builtin import SingleMessageBuiltinTool
def get_agentic_prefix_messages(
builtin_tools: List[SingleMessageBuiltinTool], custom_tools: List[ToolDefinition]
) -> List[Message]:
messages = []
content = ""
if builtin_tools:
content += "Environment: ipython\n"
tool_str = ", ".join(
[
t.get_name()
for t in builtin_tools
if t.get_name() != BuiltinTool.code_interpreter.value
]
)
if tool_str:
content += f"Tools: {tool_str}\n"
current_date = datetime.now()
formatted_date = current_date.strftime("%d %B %Y")
date_str = f"""
Cutting Knowledge Date: December 2023
Today Date: {formatted_date}\n\n"""
content += date_str
if custom_tools:
custom_message = get_system_prompt_for_custom_tools(custom_tools)
content += custom_message
# TODO: Replace this hard coded message with instructions coming in the request
if False:
content += "You are a helpful Assistant."
messages.append(SystemMessage(content=content))
return messages
def get_system_prompt_for_custom_tools(custom_tools: List[ToolDefinition]) -> str:
custom_tool_params = ""
for t in custom_tools:
custom_tool_params += get_instruction_string(t) + "\n"
custom_tool_params += get_parameters_string(t) + "\n\n"
content = f"""
You have access to the following functions:
{custom_tool_params}
Think very carefully before calling functions.
If you choose to call a function ONLY reply in the following format with no prefix or suffix:
<function=example_function_name>{{"example_name": "example_value"}}</function>
Reminder:
- If looking for real time information use relevant functions before falling back to brave_search
- Function calls MUST follow the specified format, start with <function= and end with </function>
- Required parameters MUST be specified
- Only call one function at a time
- Put the entire function call reply on one line
"""
return content
def get_instruction_string(custom_tool_definition) -> str:
return f"Use the function '{custom_tool_definition.tool_name}' to '{custom_tool_definition.description}'"
def get_parameters_string(custom_tool_definition) -> str:
return json.dumps(
{
"name": custom_tool_definition.tool_name,
"description": custom_tool_definition.description,
"parameters": {
name: definition.__dict__
for name, definition in custom_tool_definition.parameters.items()
},
}
)
# NOTE: Unused right now
def translate_custom_tool_definition_to_json(tool_def):
"""Translates ToolDefinition to json as expected by model
eg. output for a function
{
"type": "function",
"function": {
"name": "conv_int",
"description": "Convert serialized fract24 integer into int value.",
"parameters": {
"type": "object",
"properties": [
{
"data": {
"type": "object",
"description": ""
}
}
],
"required": ["data"]
}
}
}
"""
assert isinstance(tool_def.tool_name, str)
func_def = {"type": "function", "function": {}}
func_def["function"]["name"] = tool_def.tool_name
func_def["function"]["description"] = tool_def.description or ""
if tool_def.parameters:
required = []
properties = []
for p_name, p_def in tool_def.parameters.items():
properties.append(
{
p_name: {
# TODO: see if this should not always be object
"type": "object",
"description": p_def.description or "",
}
}
)
if p_def.required:
required.append(p_name)
func_def["function"]["parameters"] = {
"type": "object",
"properties": properties,
"required": required,
}
else:
func_def["function"]["parameters"] = {}
return json.dumps(func_def)