mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-10-04 04:04:14 +00:00
add tools to chat completion request
This commit is contained in:
Hardik Shah
parent
9777639a1c
commit
68855ed218
26 changed files with 558 additions and 226 deletions
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@ -110,35 +110,6 @@ class Session(BaseModel):
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started_at: datetime
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@json_schema_type
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class ToolPromptFormat(Enum):
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"""This Enum refers to the prompt format for calling zero shot tools
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`json` --
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Refers to the json format for calling tools.
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The json format takes the form like
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{
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"type": "function",
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"function" : {
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"name": "function_name",
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"description": "function_description",
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"parameters": {...}
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}
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}
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`function_tag` --
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This is an example of how you could define
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your own user defined format for making tool calls.
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The function_tag format looks like this,
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<function=function_name>(parameters)</function>
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The detailed prompts for each of these formats are defined in `system_prompt.py`
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"""
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json = "json"
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function_tag = "function_tag"
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@json_schema_type
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class AgenticSystemInstanceConfig(BaseModel):
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instructions: str
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@ -56,10 +56,10 @@ from llama_toolchain.safety.api.datatypes import (
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)
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from llama_toolchain.agentic_system.api.endpoints import * # noqa
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from llama_toolchain.tools.base import BaseTool
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from llama_toolchain.tools.builtin import SingleMessageBuiltinTool
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from .safety import SafetyException, ShieldRunnerMixin
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from .system_prompt import get_agentic_prefix_messages
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from .tools.base import BaseTool
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from .tools.builtin import SingleMessageBuiltinTool
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class AgentInstance(ShieldRunnerMixin):
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@ -85,18 +85,6 @@ class AgentInstance(ShieldRunnerMixin):
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self.inference_api = inference_api
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self.safety_api = safety_api
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if prefix_messages is not None and len(prefix_messages) > 0:
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self.prefix_messages = prefix_messages
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else:
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self.prefix_messages = get_agentic_prefix_messages(
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builtin_tools,
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custom_tool_definitions,
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tool_prompt_format,
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)
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for m in self.prefix_messages:
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print(m.content)
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self.max_infer_iters = max_infer_iters
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self.tools_dict = {t.get_name(): t for t in builtin_tools}
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@ -344,7 +332,7 @@ class AgentInstance(ShieldRunnerMixin):
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stream: bool = False,
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max_gen_len: Optional[int] = None,
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) -> AsyncGenerator:
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input_messages = preprocess_dialog(input_messages, self.prefix_messages)
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input_messages = preprocess_dialog(input_messages)
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attachments = []
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@ -373,7 +361,8 @@ class AgentInstance(ShieldRunnerMixin):
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req = ChatCompletionRequest(
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model=self.model,
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messages=input_messages,
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available_tools=self.instance_config.available_tools,
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tools=self.instance_config.available_tools,
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tool_prompt_format=self.instance_config.tool_prompt_format,
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stream=True,
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sampling_params=SamplingParams(
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temperature=temperature,
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@ -601,14 +590,12 @@ def attachment_message(url: URL) -> ToolResponseMessage:
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)
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def preprocess_dialog(
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messages: List[Message], prefix_messages: List[Message]
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) -> List[Message]:
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def preprocess_dialog(messages: List[Message]) -> List[Message]:
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"""
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Preprocesses the dialog by removing the system message and
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adding the system message to the beginning of the dialog.
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"""
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ret = prefix_messages.copy()
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ret = []
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for m in messages:
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if m.role == Role.system.value:
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@ -24,17 +24,17 @@ from llama_toolchain.agentic_system.api import (
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AgenticSystemTurnCreateRequest,
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)
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from .agent_instance import AgentInstance
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from .config import AgenticSystemConfig
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from .tools.builtin import (
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from llama_toolchain.tools.builtin import (
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BraveSearchTool,
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CodeInterpreterTool,
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PhotogenTool,
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WolframAlphaTool,
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)
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from .tools.safety import with_safety
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from llama_toolchain.tools.safety import with_safety
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from .agent_instance import AgentInstance
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from .config import AgenticSystemConfig
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logger = logging.getLogger()
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@ -1,180 +0,0 @@
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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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import json
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import textwrap
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from datetime import datetime
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from typing import List
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from llama_toolchain.agentic_system.api.datatypes import ToolPromptFormat
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from llama_toolchain.inference.api import (
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BuiltinTool,
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Message,
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SystemMessage,
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ToolDefinition,
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UserMessage,
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)
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from .tools.builtin import SingleMessageBuiltinTool
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def get_agentic_prefix_messages(
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builtin_tools: List[SingleMessageBuiltinTool],
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custom_tools: List[ToolDefinition],
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tool_prompt_format: ToolPromptFormat,
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) -> List[Message]:
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messages = []
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content = ""
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if builtin_tools:
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content += "Environment: ipython\n"
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tool_str = ", ".join(
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[
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t.get_name()
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for t in builtin_tools
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if t.get_name() != BuiltinTool.code_interpreter.value
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]
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)
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if tool_str:
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content += f"Tools: {tool_str}"
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current_date = datetime.now()
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formatted_date = current_date.strftime("%d %B %Y")
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date_str = f"""
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Cutting Knowledge Date: December 2023
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Today Date: {formatted_date}\n"""
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content += date_str
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messages.append(SystemMessage(content=content))
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if custom_tools:
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if tool_prompt_format == ToolPromptFormat.function_tag:
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text = prompt_for_function_tag(custom_tools)
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messages.append(UserMessage(content=text))
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elif tool_prompt_format == ToolPromptFormat.json:
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text = prompt_for_json(custom_tools)
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messages.append(UserMessage(content=text))
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else:
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raise NotImplementedError(
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f"Tool prompt format {tool_prompt_format} is not supported"
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)
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else:
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messages.append(SystemMessage(content=content))
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return messages
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def prompt_for_json(custom_tools: List[ToolDefinition]) -> str:
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tool_defs = "\n".join(
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translate_custom_tool_definition_to_json(t) for t in custom_tools
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)
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content = textwrap.dedent(
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"""
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Answer the user's question by making use of the following functions if needed.
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If none of the function can be used, please say so.
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Here is a list of functions in JSON format:
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{tool_defs}
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Return function calls in JSON format.
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"""
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)
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content = content.lstrip("\n").format(tool_defs=tool_defs)
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return content
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def prompt_for_function_tag(custom_tools: List[ToolDefinition]) -> str:
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custom_tool_params = ""
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for t in custom_tools:
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custom_tool_params += get_instruction_string(t) + "\n"
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custom_tool_params += get_parameters_string(t) + "\n\n"
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content = f"""
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You have access to the following functions:
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{custom_tool_params}
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Think very carefully before calling functions.
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If you choose to call a function ONLY reply in the following format with no prefix or suffix:
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<function=example_function_name>{{"example_name": "example_value"}}</function>
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Reminder:
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- If looking for real time information use relevant functions before falling back to brave_search
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- Function calls MUST follow the specified format, start with <function= and end with </function>
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- Required parameters MUST be specified
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- Only call one function at a time
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- Put the entire function call reply on one line
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"""
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return content
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def get_instruction_string(custom_tool_definition) -> str:
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return f"Use the function '{custom_tool_definition.tool_name}' to '{custom_tool_definition.description}'"
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def get_parameters_string(custom_tool_definition) -> str:
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return json.dumps(
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{
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"name": custom_tool_definition.tool_name,
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"description": custom_tool_definition.description,
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"parameters": {
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name: definition.__dict__
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for name, definition in custom_tool_definition.parameters.items()
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},
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}
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)
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def translate_custom_tool_definition_to_json(tool_def):
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"""Translates ToolDefinition to json as expected by model
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eg. output for a function
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{
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"type": "function",
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"function": {
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"name": "conv_int",
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"description": "Convert serialized fract24 integer into int value.",
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"parameters": {
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"type": "object",
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"properties": [
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{
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"data": {
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"type": "object",
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"description": ""
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}
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}
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],
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"required": ["data"]
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}
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}
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}
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"""
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assert isinstance(tool_def.tool_name, str)
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func_def = {"type": "function", "function": {}}
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func_def["function"]["name"] = tool_def.tool_name
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func_def["function"]["description"] = tool_def.description or ""
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if tool_def.parameters:
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required = []
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properties = []
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for p_name, p_def in tool_def.parameters.items():
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properties.append(
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{
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p_name: {
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# TODO: see if this should not always be object
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"type": "object",
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"description": p_def.description or "",
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}
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}
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)
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if p_def.required:
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required.append(p_name)
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func_def["function"]["parameters"] = {
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"type": "object",
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"properties": properties,
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"required": required,
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}
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else:
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func_def["function"]["parameters"] = {}
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return json.dumps(func_def, indent=4)
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@ -1,5 +0,0 @@
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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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@ -1,20 +0,0 @@
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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 abc import ABC, abstractmethod
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from typing import List
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from llama_toolchain.inference.api import Message
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class BaseTool(ABC):
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@abstractmethod
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def get_name(self) -> str:
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raise NotImplementedError
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@abstractmethod
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async def run(self, messages: List[Message]) -> List[Message]:
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raise NotImplementedError
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@ -1,322 +0,0 @@
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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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import json
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import os
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import re
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from abc import abstractmethod
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from typing import List, Optional
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import requests
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from termcolor import cprint
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from .ipython_tool.code_execution import (
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CodeExecutionContext,
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CodeExecutionRequest,
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CodeExecutor,
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TOOLS_ATTACHMENT_KEY_REGEX,
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)
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from llama_toolchain.inference.api import * # noqa: F403
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from .base import BaseTool
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def interpret_content_as_attachment(content: str) -> Optional[Attachment]:
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match = re.search(TOOLS_ATTACHMENT_KEY_REGEX, content)
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if match:
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snippet = match.group(1)
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data = json.loads(snippet)
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return Attachment(
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url=URL(uri="file://" + data["filepath"]), mime_type=data["mimetype"]
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)
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return None
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class SingleMessageBuiltinTool(BaseTool):
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async def run(self, messages: List[CompletionMessage]) -> List[ToolResponseMessage]:
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assert len(messages) == 1, f"Expected single message, got {len(messages)}"
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message = messages[0]
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assert len(message.tool_calls) == 1, "Expected a single tool call"
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tool_call = messages[0].tool_calls[0]
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query = tool_call.arguments["query"]
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response: str = await self.run_impl(query)
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message = ToolResponseMessage(
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call_id=tool_call.call_id,
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tool_name=tool_call.tool_name,
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content=response,
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)
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if attachment := interpret_content_as_attachment(response):
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message.content = attachment
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return [message]
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@abstractmethod
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async def run_impl(self, query: str) -> str:
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raise NotImplementedError()
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class PhotogenTool(SingleMessageBuiltinTool):
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def __init__(self, dump_dir: str) -> None:
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self.dump_dir = dump_dir
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def get_name(self) -> str:
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return BuiltinTool.photogen.value
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async def run_impl(self, query: str) -> str:
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"""
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Implement this to give the model an ability to generate images.
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Return:
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info = {
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"filepath": str(image_filepath),
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"mimetype": "image/png",
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}
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"""
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raise NotImplementedError()
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class BraveSearchTool(SingleMessageBuiltinTool):
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def __init__(self, api_key: str) -> None:
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self.api_key = api_key
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def get_name(self) -> str:
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return BuiltinTool.brave_search.value
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async def run_impl(self, query: str) -> str:
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url = "https://api.search.brave.com/res/v1/web/search"
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headers = {
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"X-Subscription-Token": self.api_key,
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"Accept-Encoding": "gzip",
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"Accept": "application/json",
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}
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payload = {"q": query}
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response = requests.get(url=url, params=payload, headers=headers)
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return json.dumps(self._clean_brave_response(response.json()))
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def _clean_brave_response(self, search_response, top_k=3):
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query = None
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clean_response = []
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if "query" in search_response:
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if "original" in search_response["query"]:
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query = search_response["query"]["original"]
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if "mixed" in search_response:
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mixed_results = search_response["mixed"]
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for m in mixed_results["main"][:top_k]:
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r_type = m["type"]
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results = search_response[r_type]["results"]
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if r_type == "web":
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# For web data - add a single output from the search
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idx = m["index"]
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selected_keys = [
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"type",
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"title",
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"url",
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"description",
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"date",
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"extra_snippets",
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]
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cleaned = {
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k: v for k, v in results[idx].items() if k in selected_keys
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}
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elif r_type == "faq":
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# For faw data - take a list of all the questions & answers
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selected_keys = ["type", "question", "answer", "title", "url"]
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cleaned = []
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for q in results:
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cleaned.append(
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{k: v for k, v in q.items() if k in selected_keys}
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)
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elif r_type == "infobox":
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idx = m["index"]
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selected_keys = [
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"type",
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"title",
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"url",
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"description",
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"long_desc",
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]
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cleaned = {
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k: v for k, v in results[idx].items() if k in selected_keys
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}
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elif r_type == "videos":
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selected_keys = [
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"type",
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"url",
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"title",
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"description",
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"date",
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]
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cleaned = []
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for q in results:
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cleaned.append(
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{k: v for k, v in q.items() if k in selected_keys}
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)
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elif r_type == "locations":
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# For faw data - take a list of all the questions & answers
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selected_keys = [
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"type",
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"title",
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"url",
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"description",
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"coordinates",
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"postal_address",
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"contact",
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"rating",
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"distance",
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"zoom_level",
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||||
]
|
||||
cleaned = []
|
||||
for q in results:
|
||||
cleaned.append(
|
||||
{k: v for k, v in q.items() if k in selected_keys}
|
||||
)
|
||||
elif r_type == "news":
|
||||
# For faw data - take a list of all the questions & answers
|
||||
selected_keys = [
|
||||
"type",
|
||||
"title",
|
||||
"url",
|
||||
"description",
|
||||
]
|
||||
cleaned = []
|
||||
for q in results:
|
||||
cleaned.append(
|
||||
{k: v for k, v in q.items() if k in selected_keys}
|
||||
)
|
||||
else:
|
||||
cleaned = []
|
||||
|
||||
clean_response.append(cleaned)
|
||||
|
||||
return {"query": query, "top_k": clean_response}
|
||||
|
||||
|
||||
class WolframAlphaTool(SingleMessageBuiltinTool):
|
||||
def __init__(self, api_key: str) -> None:
|
||||
self.api_key = api_key
|
||||
self.url = "https://api.wolframalpha.com/v2/query"
|
||||
|
||||
def get_name(self) -> str:
|
||||
return BuiltinTool.wolfram_alpha.value
|
||||
|
||||
async def run_impl(self, query: str) -> str:
|
||||
params = {
|
||||
"input": query,
|
||||
"appid": self.api_key,
|
||||
"format": "plaintext",
|
||||
"output": "json",
|
||||
}
|
||||
response = requests.get(
|
||||
self.url,
|
||||
params=params,
|
||||
)
|
||||
|
||||
return json.dumps(self._clean_wolfram_alpha_response(response.json()))
|
||||
|
||||
def _clean_wolfram_alpha_response(self, wa_response):
|
||||
remove = {
|
||||
"queryresult": [
|
||||
"datatypes",
|
||||
"error",
|
||||
"timedout",
|
||||
"timedoutpods",
|
||||
"numpods",
|
||||
"timing",
|
||||
"parsetiming",
|
||||
"parsetimedout",
|
||||
"recalculate",
|
||||
"id",
|
||||
"host",
|
||||
"server",
|
||||
"related",
|
||||
"version",
|
||||
{
|
||||
"pods": [
|
||||
"scanner",
|
||||
"id",
|
||||
"error",
|
||||
"expressiontypes",
|
||||
"states",
|
||||
"infos",
|
||||
"position",
|
||||
"numsubpods",
|
||||
]
|
||||
},
|
||||
"assumptions",
|
||||
],
|
||||
}
|
||||
for main_key in remove:
|
||||
for key_to_remove in remove[main_key]:
|
||||
try:
|
||||
if key_to_remove == "assumptions":
|
||||
if "assumptions" in wa_response[main_key]:
|
||||
del wa_response[main_key][key_to_remove]
|
||||
if isinstance(key_to_remove, dict):
|
||||
for sub_key in key_to_remove:
|
||||
if sub_key == "pods":
|
||||
for i in range(len(wa_response[main_key][sub_key])):
|
||||
if (
|
||||
wa_response[main_key][sub_key][i]["title"]
|
||||
== "Result"
|
||||
):
|
||||
del wa_response[main_key][sub_key][i + 1 :]
|
||||
break
|
||||
sub_items = wa_response[main_key][sub_key]
|
||||
for i in range(len(sub_items)):
|
||||
for sub_key_to_remove in key_to_remove[sub_key]:
|
||||
if sub_key_to_remove in sub_items[i]:
|
||||
del sub_items[i][sub_key_to_remove]
|
||||
elif key_to_remove in wa_response[main_key]:
|
||||
del wa_response[main_key][key_to_remove]
|
||||
except KeyError:
|
||||
pass
|
||||
return wa_response
|
||||
|
||||
|
||||
class CodeInterpreterTool(BaseTool):
|
||||
def __init__(self) -> None:
|
||||
ctx = CodeExecutionContext(
|
||||
matplotlib_dump_dir=f"/tmp/{os.environ['USER']}_matplotlib_dump",
|
||||
)
|
||||
self.code_executor = CodeExecutor(ctx)
|
||||
|
||||
def get_name(self) -> str:
|
||||
return BuiltinTool.code_interpreter.value
|
||||
|
||||
async def run(self, messages: List[CompletionMessage]) -> List[ToolResponseMessage]:
|
||||
message = messages[0]
|
||||
assert len(message.tool_calls) == 1, "Expected a single tool call"
|
||||
|
||||
tool_call = messages[0].tool_calls[0]
|
||||
script = tool_call.arguments["code"]
|
||||
|
||||
req = CodeExecutionRequest(scripts=[script])
|
||||
res = self.code_executor.execute(req)
|
||||
|
||||
pieces = [res["process_status"]]
|
||||
for out_type in ["stdout", "stderr"]:
|
||||
res_out = res[out_type]
|
||||
if res_out != "":
|
||||
pieces.extend([f"[{out_type}]", res_out, f"[/{out_type}]"])
|
||||
if out_type == "stderr":
|
||||
cprint(f"ipython tool error: ↓\n{res_out}", color="red")
|
||||
|
||||
message = ToolResponseMessage(
|
||||
call_id=tool_call.call_id,
|
||||
tool_name=tool_call.tool_name,
|
||||
content="\n".join(pieces),
|
||||
)
|
||||
if attachment := interpret_content_as_attachment(res["stdout"]):
|
||||
message.content = attachment
|
||||
|
||||
return [message]
|
||||
|
|
@ -1,5 +0,0 @@
|
|||
# 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.
|
||||
|
|
@ -1,133 +0,0 @@
|
|||
# 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 errno
|
||||
|
||||
# Disabling potentially dangerous functions
|
||||
import os as _os
|
||||
from functools import partial
|
||||
|
||||
os_funcs_to_disable = [
|
||||
"kill",
|
||||
"system",
|
||||
"putenv",
|
||||
"remove",
|
||||
"removedirs",
|
||||
"rmdir",
|
||||
"fchdir",
|
||||
"setuid",
|
||||
"fork",
|
||||
"forkpty",
|
||||
"killpg",
|
||||
"rename",
|
||||
"renames",
|
||||
"truncate",
|
||||
"replace",
|
||||
# "unlink", # Commenting as this was blocking matpltlib from rendering plots correctly
|
||||
"fchmod",
|
||||
"fchown",
|
||||
"chmod",
|
||||
"chown",
|
||||
"chroot",
|
||||
"fchdir",
|
||||
"lchflags",
|
||||
"lchmod",
|
||||
"lchown",
|
||||
"chdir",
|
||||
]
|
||||
|
||||
|
||||
def call_not_allowed(*args, **kwargs):
|
||||
raise OSError(errno.EPERM, "Call are not permitted in this environment")
|
||||
|
||||
|
||||
for func_name in os_funcs_to_disable:
|
||||
if hasattr(_os, func_name):
|
||||
setattr(_os, func_name, partial(call_not_allowed, _func_name=f"os.{func_name}"))
|
||||
|
||||
import shutil as _shutil
|
||||
|
||||
for func_name in ["rmtree", "move", "chown"]:
|
||||
if hasattr(_shutil, func_name):
|
||||
setattr(
|
||||
_shutil,
|
||||
func_name,
|
||||
partial(call_not_allowed, _func_name=f"shutil.{func_name}"),
|
||||
)
|
||||
|
||||
import subprocess as _subprocess
|
||||
|
||||
|
||||
def popen_not_allowed(*args, **kwargs):
|
||||
raise _subprocess.CalledProcessError(
|
||||
-1,
|
||||
args[0] if args else "unknown",
|
||||
stderr="subprocess.Popen is not allowed in this environment",
|
||||
)
|
||||
|
||||
|
||||
_subprocess.Popen = popen_not_allowed
|
||||
|
||||
|
||||
import atexit as _atexit
|
||||
import builtins as _builtins
|
||||
import io as _io
|
||||
import json as _json
|
||||
import sys as _sys
|
||||
|
||||
# NB! The following "unused" imports crucial, make sure not not to remove
|
||||
# them with linters - they're used in code_execution.py
|
||||
from contextlib import ( # noqa
|
||||
contextmanager as _contextmanager,
|
||||
redirect_stderr as _redirect_stderr,
|
||||
redirect_stdout as _redirect_stdout,
|
||||
)
|
||||
from multiprocessing.connection import Connection as _Connection
|
||||
|
||||
# Mangle imports to avoid polluting model execution namespace.
|
||||
|
||||
_IO_SINK = _io.StringIO()
|
||||
_NETWORK_TIMEOUT = 5
|
||||
_NETWORK_CONNECTIONS = None
|
||||
|
||||
|
||||
def _open_connections():
|
||||
global _NETWORK_CONNECTIONS
|
||||
if _NETWORK_CONNECTIONS is not None:
|
||||
# Ensure connections only opened once.
|
||||
return _NETWORK_CONNECTIONS
|
||||
req_w_fd, resp_r_fd = _sys.argv[1], _sys.argv[2]
|
||||
req_con = _Connection(int(req_w_fd), readable=False)
|
||||
resp_con = _Connection(int(resp_r_fd), writable=False)
|
||||
_NETWORK_CONNECTIONS = (req_con, resp_con)
|
||||
return _NETWORK_CONNECTIONS
|
||||
|
||||
|
||||
_builtins._open_connections = _open_connections
|
||||
|
||||
|
||||
@_atexit.register
|
||||
def _close_connections():
|
||||
global _NETWORK_CONNECTIONS
|
||||
if _NETWORK_CONNECTIONS is None:
|
||||
return
|
||||
for con in _NETWORK_CONNECTIONS:
|
||||
con.close()
|
||||
del _NETWORK_CONNECTIONS
|
||||
|
||||
|
||||
def _network_call(request):
|
||||
# NOTE: We communicate with the parent process in json, encoded
|
||||
# in raw bytes. We do this because native send/recv methods use
|
||||
# pickle which involves execution of arbitrary code.
|
||||
_open_connections()
|
||||
req_con, resp_con = _NETWORK_CONNECTIONS
|
||||
|
||||
req_con.send_bytes(_json.dumps(request).encode("utf-8"))
|
||||
if resp_con.poll(timeout=_NETWORK_TIMEOUT) is None:
|
||||
raise Exception(f"Network request timed out: {_json.dumps(request)}")
|
||||
else:
|
||||
return _json.loads(resp_con.recv_bytes().decode("utf-8"))
|
||||
|
|
@ -1,256 +0,0 @@
|
|||
# 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 base64
|
||||
import json
|
||||
import multiprocessing
|
||||
import os
|
||||
import re
|
||||
import subprocess
|
||||
import sys
|
||||
import tempfile
|
||||
import textwrap
|
||||
import time
|
||||
from dataclasses import dataclass
|
||||
from datetime import datetime
|
||||
from io import BytesIO
|
||||
from pathlib import Path
|
||||
from typing import List
|
||||
|
||||
from PIL import Image
|
||||
|
||||
from .utils import get_code_env_prefix
|
||||
|
||||
TOOLS_ATTACHMENT_KEY = "__tools_attachment__"
|
||||
TOOLS_ATTACHMENT_KEY_REGEX = re.compile(r"__tools_attachment__=(\{.*?\})")
|
||||
|
||||
DIRNAME = Path(__file__).parent
|
||||
|
||||
CODE_EXEC_TIMEOUT = 20
|
||||
CODE_ENV_PREFIX = get_code_env_prefix()
|
||||
|
||||
STDOUTERR_SINK_WRAPPER_TEMPLATE = """\
|
||||
with _redirect_stdout(_IO_SINK), _redirect_stderr(_IO_SINK):
|
||||
{code}\
|
||||
"""
|
||||
|
||||
TRYEXCEPT_WRAPPER_TEMPLATE = """\
|
||||
try:
|
||||
{code}
|
||||
except:
|
||||
pass\
|
||||
"""
|
||||
|
||||
|
||||
def generate_bwrap_command(bind_dirs: List[str]) -> str:
|
||||
"""
|
||||
Generate the bwrap command string for binding all
|
||||
directories in the current directory read-only.
|
||||
"""
|
||||
bwrap_args = ""
|
||||
bwrap_args += "--ro-bind / / "
|
||||
# Add the --dev flag to mount device files
|
||||
bwrap_args += "--dev /dev "
|
||||
for d in bind_dirs:
|
||||
bwrap_args += f"--bind {d} {d} "
|
||||
|
||||
# Add the --unshare-all flag to isolate the sandbox from the rest of the system
|
||||
bwrap_args += "--unshare-all "
|
||||
# Add the --die-with-parent flag to ensure the child process dies when bwrap's parent dies
|
||||
bwrap_args += "--die-with-parent "
|
||||
return bwrap_args
|
||||
|
||||
|
||||
@dataclass
|
||||
class CodeExecutionContext:
|
||||
matplotlib_dump_dir: str
|
||||
use_proxy: bool = False
|
||||
|
||||
|
||||
@dataclass
|
||||
class CodeExecutionRequest:
|
||||
scripts: List[str]
|
||||
only_last_cell_stdouterr: bool = True
|
||||
only_last_cell_fail: bool = True
|
||||
seed: int = 0
|
||||
strip_fpaths_in_stderr: bool = True
|
||||
|
||||
|
||||
class CodeExecutor:
|
||||
def __init__(self, context: CodeExecutionContext):
|
||||
self.context = context
|
||||
|
||||
def execute(self, req: CodeExecutionRequest) -> dict:
|
||||
scripts = req.scripts
|
||||
for i in range(len(scripts) - 1):
|
||||
if req.only_last_cell_stdouterr:
|
||||
scripts[i] = STDOUTERR_SINK_WRAPPER_TEMPLATE.format(
|
||||
code=textwrap.indent(scripts[i], " " * 4)
|
||||
)
|
||||
if req.only_last_cell_fail:
|
||||
scripts[i] = TRYEXCEPT_WRAPPER_TEMPLATE.format(
|
||||
code=textwrap.indent(scripts[i], " " * 4)
|
||||
)
|
||||
|
||||
# Seeds prefix:
|
||||
seed = req.seed
|
||||
seeds_prefix = f"""\
|
||||
def _set_seeds():
|
||||
import random
|
||||
random.seed({seed})
|
||||
import numpy as np
|
||||
np.random.seed({seed})
|
||||
_set_seeds()\
|
||||
"""
|
||||
|
||||
script = "\n\n".join([seeds_prefix] + [CODE_ENV_PREFIX] + scripts)
|
||||
with tempfile.TemporaryDirectory() as dpath:
|
||||
bwrap_prefix = "bwrap " + generate_bwrap_command(bind_dirs=[dpath])
|
||||
cmd = [*bwrap_prefix.split(), sys.executable, "-c", script]
|
||||
code_fpath = os.path.join(dpath, "code.py")
|
||||
with open(code_fpath, "w") as f:
|
||||
f.write(script)
|
||||
|
||||
try:
|
||||
python_path = os.environ.get("PYTHONPATH", "")
|
||||
env = dict(
|
||||
os.environ,
|
||||
PYTHONHASHSEED=str(seed),
|
||||
MPLCONFIGDIR=dpath,
|
||||
MPLBACKEND="module://matplotlib_custom_backend",
|
||||
PYTHONPATH=f"{DIRNAME}:{python_path}",
|
||||
)
|
||||
stdout, stderr, returncode = do_subprocess(
|
||||
cmd=cmd,
|
||||
env=env,
|
||||
ctx=self.context,
|
||||
)
|
||||
|
||||
stderr = stderr.strip()
|
||||
if req.strip_fpaths_in_stderr:
|
||||
pattern = r'File "([^"]+)", line (\d+)'
|
||||
stderr = re.sub(pattern, r"line \2", stderr)
|
||||
|
||||
return {
|
||||
"process_status": "completed",
|
||||
"returncode": returncode,
|
||||
"stdout": stdout.strip(),
|
||||
"stderr": stderr,
|
||||
}
|
||||
|
||||
except subprocess.TimeoutExpired:
|
||||
return {
|
||||
"process_status": "timeout",
|
||||
"stdout": "Timed out",
|
||||
"stderr": "Timed out",
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
return {
|
||||
"process_status": "error",
|
||||
"error_type": type(e).__name__,
|
||||
"stderr": str(e),
|
||||
"stdout": str(e),
|
||||
}
|
||||
|
||||
|
||||
def process_matplotlib_response(response, matplotlib_dump_dir: str):
|
||||
image_data = response["image_data"]
|
||||
# Convert the base64 string to a bytes object
|
||||
images = [base64.b64decode(d["image_base64"]) for d in image_data]
|
||||
# Create a list of PIL images from the bytes objects
|
||||
images = [Image.open(BytesIO(img)) for img in images]
|
||||
# Create a list of image paths
|
||||
image_paths = []
|
||||
for i, img in enumerate(images):
|
||||
# create new directory for each day to better organize data:
|
||||
dump_dname = datetime.today().strftime("%Y-%m-%d")
|
||||
dump_dpath = Path(matplotlib_dump_dir, dump_dname)
|
||||
dump_dpath.mkdir(parents=True, exist_ok=True)
|
||||
# save image into a file
|
||||
dump_fname = f"matplotlib_{str(time.time()).replace('.', '_')}_{i}.png"
|
||||
dump_fpath = dump_dpath / dump_fname
|
||||
img.save(dump_fpath, "PNG")
|
||||
image_paths.append(str(dump_fpath))
|
||||
|
||||
# this is kind of convoluted, we send back this response to the subprocess which
|
||||
# prints it out
|
||||
info = {
|
||||
"filepath": str(image_paths[-1]),
|
||||
"mimetype": "image/png",
|
||||
}
|
||||
return f"{TOOLS_ATTACHMENT_KEY}={json.dumps(info)}"
|
||||
|
||||
|
||||
def execute_subprocess_request(request, ctx: CodeExecutionContext):
|
||||
"Route requests from the subprocess (via network Pipes) to the internet/tools."
|
||||
if request["type"] == "matplotlib":
|
||||
return process_matplotlib_response(request, ctx.matplotlib_dump_dir)
|
||||
else:
|
||||
raise Exception(f'Unrecognised network request type: {request["type"]}')
|
||||
|
||||
|
||||
def do_subprocess(*, cmd: list, env: dict, ctx: CodeExecutionContext):
|
||||
# Create Pipes to be used for any external tool/network requests.
|
||||
req_r, req_w = multiprocessing.Pipe(duplex=False)
|
||||
resp_r, resp_w = multiprocessing.Pipe(duplex=False)
|
||||
|
||||
cmd += [str(req_w.fileno()), str(resp_r.fileno())]
|
||||
proc = subprocess.Popen(
|
||||
cmd,
|
||||
pass_fds=(req_w.fileno(), resp_r.fileno()),
|
||||
text=True,
|
||||
stdout=subprocess.PIPE,
|
||||
stderr=subprocess.PIPE,
|
||||
close_fds=True,
|
||||
env=env,
|
||||
)
|
||||
|
||||
# Close unnecessary fds.
|
||||
req_w.close()
|
||||
resp_r.close()
|
||||
|
||||
pipe_close = False
|
||||
done_read = False
|
||||
start = time.monotonic()
|
||||
while proc.poll() is None and not pipe_close:
|
||||
if req_r.poll(0.1):
|
||||
# NB: Python pipe semantics for poll and recv mean that
|
||||
# poll() returns True is a pipe is closed.
|
||||
# CF old school PEP from '09
|
||||
# https://bugs.python.org/issue5573
|
||||
try:
|
||||
request = json.loads(req_r.recv_bytes().decode("utf-8"))
|
||||
response = execute_subprocess_request(request, ctx)
|
||||
|
||||
resp_w.send_bytes(json.dumps(response).encode("utf-8"))
|
||||
except EOFError:
|
||||
# The request pipe is closed - set a marker to exit
|
||||
# after the next attempt at reading stdout/stderr.
|
||||
pipe_close = True
|
||||
|
||||
try:
|
||||
# If lots has been printed, pipe might be full but
|
||||
# proc cannot exit until all the stdout/stderr
|
||||
# been written/read.
|
||||
stdout, stderr = proc.communicate(timeout=0.3)
|
||||
done_read = True
|
||||
except subprocess.TimeoutExpired:
|
||||
# The program has not terminated. Ignore it, there
|
||||
# may be more network/tool requests.
|
||||
continue
|
||||
if time.monotonic() - start > CODE_EXEC_TIMEOUT:
|
||||
proc.terminate()
|
||||
raise subprocess.TimeoutExpired(cmd, CODE_EXEC_TIMEOUT)
|
||||
|
||||
if not done_read:
|
||||
# Solve race condition where process terminates before
|
||||
# we hit the while loop.
|
||||
stdout, stderr = proc.communicate(timeout=0.3)
|
||||
|
||||
resp_w.close()
|
||||
req_r.close()
|
||||
return stdout, stderr, proc.returncode
|
||||
|
|
@ -1,87 +0,0 @@
|
|||
# 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.
|
||||
|
||||
"""
|
||||
A custom Matplotlib backend that overrides the show method to return image bytes.
|
||||
"""
|
||||
|
||||
import base64
|
||||
import io
|
||||
import json as _json
|
||||
|
||||
import matplotlib
|
||||
from matplotlib.backend_bases import FigureManagerBase
|
||||
|
||||
# Import necessary components from Matplotlib
|
||||
from matplotlib.backends.backend_agg import FigureCanvasAgg
|
||||
|
||||
|
||||
class CustomFigureCanvas(FigureCanvasAgg):
|
||||
def show(self):
|
||||
# Save the figure to a BytesIO object
|
||||
buf = io.BytesIO()
|
||||
self.print_png(buf)
|
||||
image_bytes = buf.getvalue()
|
||||
buf.close()
|
||||
return image_bytes
|
||||
|
||||
|
||||
class CustomFigureManager(FigureManagerBase):
|
||||
def __init__(self, canvas, num):
|
||||
super().__init__(canvas, num)
|
||||
|
||||
|
||||
# Mimic module initialization that integrates with the Matplotlib backend system
|
||||
def _create_figure_manager(num, *args, **kwargs):
|
||||
"""
|
||||
Create a custom figure manager instance.
|
||||
"""
|
||||
FigureClass = kwargs.pop("FigureClass", None) # noqa: N806
|
||||
if FigureClass is None:
|
||||
from matplotlib.figure import Figure
|
||||
|
||||
FigureClass = Figure # noqa: N806
|
||||
fig = FigureClass(*args, **kwargs)
|
||||
canvas = CustomFigureCanvas(fig)
|
||||
manager = CustomFigureManager(canvas, num)
|
||||
return manager
|
||||
|
||||
|
||||
def show():
|
||||
"""
|
||||
Handle all figures and potentially return their images as bytes.
|
||||
|
||||
This function iterates over all figures registered with the custom backend,
|
||||
renders them as images in bytes format, and could return a list of bytes objects,
|
||||
one for each figure, or handle them as needed.
|
||||
"""
|
||||
image_data = []
|
||||
for manager in matplotlib._pylab_helpers.Gcf.get_all_fig_managers():
|
||||
# Get the figure from the manager
|
||||
fig = manager.canvas.figure
|
||||
buf = io.BytesIO() # Create a buffer for the figure
|
||||
fig.savefig(buf, format="png") # Save the figure to the buffer in PNG format
|
||||
buf.seek(0) # Go to the beginning of the buffer
|
||||
image_bytes = buf.getvalue() # Retrieve bytes value
|
||||
image_base64 = base64.b64encode(image_bytes).decode("utf-8")
|
||||
image_data.append({"image_base64": image_base64})
|
||||
buf.close()
|
||||
|
||||
req_con, resp_con = _open_connections()
|
||||
|
||||
_json_dump = _json.dumps(
|
||||
{
|
||||
"type": "matplotlib",
|
||||
"image_data": image_data,
|
||||
}
|
||||
)
|
||||
req_con.send_bytes(_json_dump.encode("utf-8"))
|
||||
resp = _json.loads(resp_con.recv_bytes().decode("utf-8"))
|
||||
print(resp)
|
||||
|
||||
|
||||
FigureCanvas = CustomFigureCanvas
|
||||
FigureManager = CustomFigureManager
|
||||
|
|
@ -1,21 +0,0 @@
|
|||
# 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 os
|
||||
|
||||
DIR = os.path.dirname(os.path.realpath(__file__))
|
||||
CODE_ENV_PREFIX_FILE = os.path.join(DIR, "code_env_prefix.py")
|
||||
CODE_ENV_PREFIX = None
|
||||
|
||||
|
||||
def get_code_env_prefix() -> str:
|
||||
global CODE_ENV_PREFIX
|
||||
|
||||
if CODE_ENV_PREFIX is None:
|
||||
with open(CODE_ENV_PREFIX_FILE, "r") as f:
|
||||
CODE_ENV_PREFIX = f.read()
|
||||
|
||||
return CODE_ENV_PREFIX
|
||||
|
|
@ -1,59 +0,0 @@
|
|||
# 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.
|
||||
|
||||
from typing import List
|
||||
|
||||
from llama_toolchain.agentic_system.meta_reference.safety import ShieldRunnerMixin
|
||||
|
||||
from llama_toolchain.inference.api import Message
|
||||
from llama_toolchain.safety.api.datatypes import ShieldDefinition
|
||||
from llama_toolchain.safety.api.endpoints import Safety
|
||||
|
||||
from .builtin import BaseTool
|
||||
|
||||
|
||||
class SafeTool(BaseTool, ShieldRunnerMixin):
|
||||
"""A tool that makes other tools safety enabled"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
tool: BaseTool,
|
||||
safety_api: Safety,
|
||||
input_shields: List[ShieldDefinition] = None,
|
||||
output_shields: List[ShieldDefinition] = None,
|
||||
):
|
||||
self._tool = tool
|
||||
ShieldRunnerMixin.__init__(
|
||||
self, safety_api, input_shields=input_shields, output_shields=output_shields
|
||||
)
|
||||
|
||||
def get_name(self) -> str:
|
||||
# return the name of the wrapped tool
|
||||
return self._tool.get_name()
|
||||
|
||||
async def run(self, messages: List[Message]) -> List[Message]:
|
||||
if self.input_shields:
|
||||
await self.run_shields(messages, self.input_shields)
|
||||
# run the underlying tool
|
||||
res = await self._tool.run(messages)
|
||||
if self.output_shields:
|
||||
await self.run_shields(messages, self.output_shields)
|
||||
|
||||
return res
|
||||
|
||||
|
||||
def with_safety(
|
||||
tool: BaseTool,
|
||||
safety_api: Safety,
|
||||
input_shields: List[ShieldDefinition] = None,
|
||||
output_shields: List[ShieldDefinition] = None,
|
||||
) -> SafeTool:
|
||||
return SafeTool(
|
||||
tool,
|
||||
safety_api,
|
||||
input_shields=input_shields,
|
||||
output_shields=output_shields,
|
||||
)
|
||||
|
|
@ -1,5 +0,0 @@
|
|||
# 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.
|
||||
|
|
@ -1,106 +0,0 @@
|
|||
# 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 abc import abstractmethod
|
||||
from typing import Dict, List
|
||||
|
||||
from llama_models.llama3.api.datatypes import * # noqa: F403
|
||||
from llama_toolchain.agentic_system.api import * # noqa: F403
|
||||
|
||||
# TODO: this is symptomatic of us needing to pull more tooling related utilities
|
||||
from llama_toolchain.agentic_system.meta_reference.tools.builtin import (
|
||||
interpret_content_as_attachment,
|
||||
)
|
||||
|
||||
|
||||
class CustomTool:
|
||||
"""
|
||||
Developers can define their custom tools that models can use
|
||||
by extending this class.
|
||||
|
||||
Developers need to provide
|
||||
- name
|
||||
- description
|
||||
- params_definition
|
||||
- implement tool's behavior in `run_impl` method
|
||||
|
||||
NOTE: The return of the `run` method needs to be json serializable
|
||||
"""
|
||||
|
||||
@abstractmethod
|
||||
def get_name(self) -> str:
|
||||
raise NotImplementedError
|
||||
|
||||
@abstractmethod
|
||||
def get_description(self) -> str:
|
||||
raise NotImplementedError
|
||||
|
||||
@abstractmethod
|
||||
def get_params_definition(self) -> Dict[str, ToolParamDefinition]:
|
||||
raise NotImplementedError
|
||||
|
||||
def get_instruction_string(self) -> str:
|
||||
return f"Use the function '{self.get_name()}' to: {self.get_description()}"
|
||||
|
||||
def parameters_for_system_prompt(self) -> str:
|
||||
return json.dumps(
|
||||
{
|
||||
"name": self.get_name(),
|
||||
"description": self.get_description(),
|
||||
"parameters": {
|
||||
name: definition.__dict__
|
||||
for name, definition in self.get_params_definition().items()
|
||||
},
|
||||
}
|
||||
)
|
||||
|
||||
def get_tool_definition(self) -> AgenticSystemToolDefinition:
|
||||
return AgenticSystemToolDefinition(
|
||||
tool_name=self.get_name(),
|
||||
description=self.get_description(),
|
||||
parameters=self.get_params_definition(),
|
||||
)
|
||||
|
||||
@abstractmethod
|
||||
async def run(self, messages: List[Message]) -> List[Message]:
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
class SingleMessageCustomTool(CustomTool):
|
||||
"""
|
||||
Helper class to handle custom tools that take a single message
|
||||
Extending this class and implementing the `run_impl` method will
|
||||
allow for the tool be called by the model and the necessary plumbing.
|
||||
"""
|
||||
|
||||
async def run(self, messages: List[CompletionMessage]) -> List[ToolResponseMessage]:
|
||||
assert len(messages) == 1, "Expected single message"
|
||||
|
||||
message = messages[0]
|
||||
|
||||
tool_call = message.tool_calls[0]
|
||||
|
||||
try:
|
||||
response = await self.run_impl(**tool_call.arguments)
|
||||
response_str = json.dumps(response, ensure_ascii=False)
|
||||
except Exception as e:
|
||||
response_str = f"Error when running tool: {e}"
|
||||
|
||||
message = ToolResponseMessage(
|
||||
call_id=tool_call.call_id,
|
||||
tool_name=tool_call.tool_name,
|
||||
content=response_str,
|
||||
)
|
||||
if attachment := interpret_content_as_attachment(response_str):
|
||||
message.content = attachment
|
||||
|
||||
return [message]
|
||||
|
||||
@abstractmethod
|
||||
async def run_impl(self, *args, **kwargs):
|
||||
raise NotImplementedError()
|
||||
|
|
@ -18,7 +18,7 @@ from llama_toolchain.agentic_system.api import (
|
|||
from llama_toolchain.agentic_system.api.datatypes import ToolPromptFormat
|
||||
from llama_toolchain.agentic_system.client import AgenticSystemClient
|
||||
|
||||
from llama_toolchain.agentic_system.tools.custom.execute import (
|
||||
from llama_toolchain.agentic_system.meta_reference.execute_with_custom_tools import (
|
||||
execute_with_custom_tools,
|
||||
)
|
||||
from llama_toolchain.safety.api.datatypes import BuiltinShield, ShieldDefinition
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue