mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-08-03 09:21:45 +00:00
remove all usages of builtin tools in agents
This commit is contained in:
Dinesh Yeduguru
parent
f90e9c2003
commit
dcdf9da6ef
17 changed files with 89 additions and 1200 deletions
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@ -47,78 +47,6 @@ class Attachment(BaseModel):
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mime_type: str
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class AgentTool(Enum):
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brave_search = "brave_search"
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wolfram_alpha = "wolfram_alpha"
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photogen = "photogen"
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code_interpreter = "code_interpreter"
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function_call = "function_call"
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memory = "memory"
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class ToolDefinitionCommon(BaseModel):
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input_shields: Optional[List[str]] = Field(default_factory=list)
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output_shields: Optional[List[str]] = Field(default_factory=list)
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class SearchEngineType(Enum):
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bing = "bing"
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brave = "brave"
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tavily = "tavily"
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@json_schema_type
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class SearchToolDefinition(ToolDefinitionCommon):
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# NOTE: brave_search is just a placeholder since model always uses
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# brave_search as tool call name
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type: Literal[AgentTool.brave_search.value] = AgentTool.brave_search.value
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api_key: str
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engine: SearchEngineType = SearchEngineType.brave
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remote_execution: Optional[RestAPIExecutionConfig] = None
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@json_schema_type
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class WolframAlphaToolDefinition(ToolDefinitionCommon):
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type: Literal[AgentTool.wolfram_alpha.value] = AgentTool.wolfram_alpha.value
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api_key: str
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remote_execution: Optional[RestAPIExecutionConfig] = None
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@json_schema_type
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class PhotogenToolDefinition(ToolDefinitionCommon):
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type: Literal[AgentTool.photogen.value] = AgentTool.photogen.value
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remote_execution: Optional[RestAPIExecutionConfig] = None
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@json_schema_type
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class CodeInterpreterToolDefinition(ToolDefinitionCommon):
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type: Literal[AgentTool.code_interpreter.value] = AgentTool.code_interpreter.value
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enable_inline_code_execution: bool = True
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remote_execution: Optional[RestAPIExecutionConfig] = None
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@json_schema_type
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class FunctionCallToolDefinition(ToolDefinitionCommon):
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type: Literal[AgentTool.function_call.value] = AgentTool.function_call.value
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function_name: str
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description: str
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parameters: Dict[str, ToolParamDefinition]
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remote_execution: Optional[RestAPIExecutionConfig] = None
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AgentToolDefinition = Annotated[
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Union[
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SearchToolDefinition,
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WolframAlphaToolDefinition,
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PhotogenToolDefinition,
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CodeInterpreterToolDefinition,
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FunctionCallToolDefinition,
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],
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Field(discriminator="type"),
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]
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class StepCommon(BaseModel):
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turn_id: str
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step_id: str
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@ -211,10 +139,6 @@ class AgentConfigCommon(BaseModel):
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input_shields: Optional[List[str]] = Field(default_factory=list)
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output_shields: Optional[List[str]] = Field(default_factory=list)
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tools: Optional[List[AgentToolDefinition]] = Field(
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default_factory=list, deprecated=True
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)
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available_tools: Optional[List[str]] = Field(default_factory=list)
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preprocessing_tools: Optional[List[str]] = Field(default_factory=list)
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tool_choice: Optional[ToolChoice] = Field(default=ToolChoice.auto)
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@ -21,6 +21,8 @@ class ToolParameter(BaseModel):
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name: str
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parameter_type: str
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description: str
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required: bool
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default: Optional[Any] = None
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@json_schema_type
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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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@ -5,13 +5,15 @@
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# the root directory of this source tree.
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import copy
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import json
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import logging
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import os
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import re
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import secrets
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import string
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import uuid
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from datetime import datetime
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from typing import AsyncGenerator, Dict, List
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from typing import AsyncGenerator, List
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from urllib.parse import urlparse
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import httpx
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@ -29,16 +31,11 @@ from llama_stack.apis.agents import (
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AgentTurnResponseTurnCompletePayload,
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AgentTurnResponseTurnStartPayload,
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Attachment,
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CodeInterpreterToolDefinition,
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FunctionCallToolDefinition,
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InferenceStep,
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PhotogenToolDefinition,
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SearchToolDefinition,
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ShieldCallStep,
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StepType,
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ToolExecutionStep,
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Turn,
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WolframAlphaToolDefinition,
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)
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from llama_stack.apis.common.content_types import (
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URL,
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@ -67,15 +64,6 @@ from llama_stack.providers.utils.telemetry import tracing
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from .persistence import AgentPersistence
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from .safety import SafetyException, ShieldRunnerMixin
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from .tools.base import BaseTool
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from .tools.builtin import (
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CodeInterpreterTool,
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PhotogenTool,
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SearchTool,
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WolframAlphaTool,
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interpret_content_as_attachment,
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)
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from .tools.safety import SafeTool
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log = logging.getLogger(__name__)
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@ -86,6 +74,9 @@ def make_random_string(length: int = 8):
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)
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TOOLS_ATTACHMENT_KEY_REGEX = re.compile(r"__tools_attachment__=(\{.*?\})")
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class ChatAgent(ShieldRunnerMixin):
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def __init__(
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self,
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@ -111,29 +102,6 @@ class ChatAgent(ShieldRunnerMixin):
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self.tool_runtime_api = tool_runtime_api
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self.tool_groups_api = tool_groups_api
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builtin_tools = []
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for tool_defn in agent_config.tools:
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if isinstance(tool_defn, WolframAlphaToolDefinition):
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tool = WolframAlphaTool(tool_defn.api_key)
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elif isinstance(tool_defn, SearchToolDefinition):
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tool = SearchTool(tool_defn.engine, tool_defn.api_key)
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elif isinstance(tool_defn, CodeInterpreterToolDefinition):
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tool = CodeInterpreterTool()
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elif isinstance(tool_defn, PhotogenToolDefinition):
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tool = PhotogenTool(dump_dir=self.tempdir)
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else:
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continue
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builtin_tools.append(
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SafeTool(
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tool,
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safety_api,
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tool_defn.input_shields,
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tool_defn.output_shields,
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)
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)
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self.tools_dict = {t.get_name(): t for t in builtin_tools}
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ShieldRunnerMixin.__init__(
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self,
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safety_api,
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@ -453,7 +421,7 @@ class ChatAgent(ShieldRunnerMixin):
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async for chunk in await self.inference_api.chat_completion(
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self.agent_config.model,
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input_messages,
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tools=self._get_tools(),
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tools=await self._get_tools(),
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tool_prompt_format=self.agent_config.tool_prompt_format,
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stream=True,
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sampling_params=sampling_params,
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@ -595,7 +563,8 @@ class ChatAgent(ShieldRunnerMixin):
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},
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) as span:
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result_messages = await execute_tool_call_maybe(
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self.tools_dict,
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self.tool_runtime_api,
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session_id,
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[message],
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)
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assert (
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@ -627,6 +596,20 @@ class ChatAgent(ShieldRunnerMixin):
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# TODO: add tool-input touchpoint and a "start" event for this step also
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# but that needs a lot more refactoring of Tool code potentially
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def interpret_content_as_attachment(
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content: str,
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) -> 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"]),
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mime_type=data["mimetype"],
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)
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return None
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if out_attachment := interpret_content_as_attachment(
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result_message.content
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):
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@ -639,25 +622,25 @@ class ChatAgent(ShieldRunnerMixin):
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n_iter += 1
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def _get_tools(self) -> List[ToolDefinition]:
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async def _get_tools(self) -> List[ToolDefinition]:
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ret = []
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for t in self.agent_config.tools:
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if isinstance(t, SearchToolDefinition):
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ret.append(ToolDefinition(tool_name=BuiltinTool.brave_search))
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elif isinstance(t, WolframAlphaToolDefinition):
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ret.append(ToolDefinition(tool_name=BuiltinTool.wolfram_alpha))
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elif isinstance(t, PhotogenToolDefinition):
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ret.append(ToolDefinition(tool_name=BuiltinTool.photogen))
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elif isinstance(t, CodeInterpreterToolDefinition):
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ret.append(ToolDefinition(tool_name=BuiltinTool.code_interpreter))
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elif isinstance(t, FunctionCallToolDefinition):
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ret.append(
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ToolDefinition(
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tool_name=t.function_name,
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description=t.description,
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parameters=t.parameters,
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)
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for tool_name in self.agent_config.available_tools:
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tool = await self.tool_groups_api.get_tool(tool_name)
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params = {}
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for param in tool.parameters:
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params[param.name] = ToolParamDefinition(
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param_type=param.parameter_type,
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description=param.description,
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required=param.required,
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default=param.default,
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)
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ret.append(
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ToolDefinition(
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tool_name=tool.identifier,
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description=tool.description,
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parameters=params,
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)
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)
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return ret
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@ -696,7 +679,7 @@ async def attachment_message(tempdir: str, urls: List[URL]) -> ToolResponseMessa
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async def execute_tool_call_maybe(
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tools_dict: Dict[str, BaseTool], messages: List[CompletionMessage]
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tool_runtime_api: ToolRuntime, session_id: str, messages: List[CompletionMessage]
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) -> List[ToolResponseMessage]:
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# While Tools.run interface takes a list of messages,
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# All tools currently only run on a single message
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@ -712,7 +695,17 @@ async def execute_tool_call_maybe(
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name = name.value
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assert name in tools_dict, f"Tool {name} not found"
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tool = tools_dict[name]
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result_messages = await tool.run(messages)
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return result_messages
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result = await tool_runtime_api.invoke_tool(
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tool_name=name,
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args=dict(
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session_id=session_id,
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**tool_call.arguments,
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),
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)
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return [
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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=result.content,
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)
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]
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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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|
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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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|
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@ -1,93 +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 unittest
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from llama_models.llama3.api.datatypes import (
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Attachment,
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BuiltinTool,
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CompletionMessage,
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StopReason,
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ToolCall,
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)
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from ..tools.builtin import CodeInterpreterTool
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class TestCodeInterpreter(unittest.IsolatedAsyncioTestCase):
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async def test_matplotlib(self):
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tool = CodeInterpreterTool()
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code = """
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import matplotlib.pyplot as plt
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import numpy as np
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x = np.array([1, 1])
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y = np.array([0, 10])
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plt.plot(x, y)
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plt.title('x = 1')
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plt.xlabel('x')
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plt.ylabel('y')
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plt.grid(True)
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plt.axvline(x=1, color='r')
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plt.show()
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"""
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message = CompletionMessage(
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role="assistant",
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content="",
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tool_calls=[
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ToolCall(
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call_id="call_id",
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tool_name=BuiltinTool.code_interpreter,
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arguments={"code": code},
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)
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],
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stop_reason=StopReason.end_of_message,
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)
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ret = await tool.run([message])
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self.assertEqual(len(ret), 1)
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output = ret[0].content
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self.assertIsInstance(output, Attachment)
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self.assertEqual(output.mime_type, "image/png")
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async def test_path_unlink(self):
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tool = CodeInterpreterTool()
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code = """
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import os
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from pathlib import Path
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import tempfile
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dpath = Path(os.environ["MPLCONFIGDIR"])
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with open(dpath / "test", "w") as f:
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f.write("hello")
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Path(dpath / "test").unlink()
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print("_OK_")
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"""
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message = CompletionMessage(
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role="assistant",
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content="",
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tool_calls=[
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ToolCall(
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call_id="call_id",
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tool_name=BuiltinTool.code_interpreter,
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arguments={"code": code},
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)
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],
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stop_reason=StopReason.end_of_message,
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)
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ret = await tool.run([message])
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self.assertEqual(len(ret), 1)
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output = ret[0].content
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self.assertTrue("_OK_" in output)
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if __name__ == "__main__":
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unittest.main()
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|
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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_stack.apis.inference 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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|
|
@ -1,396 +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 logging
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import re
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import tempfile
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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 .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_stack.apis.inference import * # noqa: F403
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from llama_stack.apis.agents import * # noqa: F403
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from .base import BaseTool
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log = logging.getLogger(__name__)
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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,
|
||||
tool_name=tool_call.tool_name,
|
||||
content=response,
|
||||
)
|
||||
return [message]
|
||||
|
||||
@abstractmethod
|
||||
async def run_impl(self, query: str) -> str:
|
||||
raise NotImplementedError()
|
||||
|
||||
|
||||
class PhotogenTool(SingleMessageBuiltinTool):
|
||||
def __init__(self, dump_dir: str) -> None:
|
||||
self.dump_dir = dump_dir
|
||||
|
||||
def get_name(self) -> str:
|
||||
return BuiltinTool.photogen.value
|
||||
|
||||
async def run_impl(self, query: str) -> str:
|
||||
"""
|
||||
Implement this to give the model an ability to generate images.
|
||||
|
||||
Return:
|
||||
info = {
|
||||
"filepath": str(image_filepath),
|
||||
"mimetype": "image/png",
|
||||
}
|
||||
"""
|
||||
raise NotImplementedError()
|
||||
|
||||
|
||||
class SearchTool(SingleMessageBuiltinTool):
|
||||
def __init__(self, engine: SearchEngineType, api_key: str, **kwargs) -> None:
|
||||
self.api_key = api_key
|
||||
self.engine_type = engine
|
||||
if engine == SearchEngineType.bing:
|
||||
self.engine = BingSearch(api_key, **kwargs)
|
||||
elif engine == SearchEngineType.brave:
|
||||
self.engine = BraveSearch(api_key, **kwargs)
|
||||
elif engine == SearchEngineType.tavily:
|
||||
self.engine = TavilySearch(api_key, **kwargs)
|
||||
else:
|
||||
raise ValueError(f"Unknown search engine: {engine}")
|
||||
|
||||
def get_name(self) -> str:
|
||||
return BuiltinTool.brave_search.value
|
||||
|
||||
async def run_impl(self, query: str) -> str:
|
||||
return await self.engine.search(query)
|
||||
|
||||
|
||||
class BingSearch:
|
||||
def __init__(self, api_key: str, top_k: int = 3, **kwargs) -> None:
|
||||
self.api_key = api_key
|
||||
self.top_k = top_k
|
||||
|
||||
async def search(self, query: str) -> str:
|
||||
url = "https://api.bing.microsoft.com/v7.0/search"
|
||||
headers = {
|
||||
"Ocp-Apim-Subscription-Key": self.api_key,
|
||||
}
|
||||
params = {
|
||||
"count": self.top_k,
|
||||
"textDecorations": True,
|
||||
"textFormat": "HTML",
|
||||
"q": query,
|
||||
}
|
||||
|
||||
response = requests.get(url=url, params=params, headers=headers)
|
||||
response.raise_for_status()
|
||||
clean = self._clean_response(response.json())
|
||||
return json.dumps(clean)
|
||||
|
||||
def _clean_response(self, search_response):
|
||||
clean_response = []
|
||||
query = search_response["queryContext"]["originalQuery"]
|
||||
if "webPages" in search_response:
|
||||
pages = search_response["webPages"]["value"]
|
||||
for p in pages:
|
||||
selected_keys = {"name", "url", "snippet"}
|
||||
clean_response.append(
|
||||
{k: v for k, v in p.items() if k in selected_keys}
|
||||
)
|
||||
if "news" in search_response:
|
||||
clean_news = []
|
||||
news = search_response["news"]["value"]
|
||||
for n in news:
|
||||
selected_keys = {"name", "url", "description"}
|
||||
clean_news.append({k: v for k, v in n.items() if k in selected_keys})
|
||||
|
||||
clean_response.append(clean_news)
|
||||
|
||||
return {"query": query, "top_k": clean_response}
|
||||
|
||||
|
||||
class BraveSearch:
|
||||
def __init__(self, api_key: str) -> None:
|
||||
self.api_key = api_key
|
||||
|
||||
async def search(self, query: str) -> str:
|
||||
url = "https://api.search.brave.com/res/v1/web/search"
|
||||
headers = {
|
||||
"X-Subscription-Token": self.api_key,
|
||||
"Accept-Encoding": "gzip",
|
||||
"Accept": "application/json",
|
||||
}
|
||||
payload = {"q": query}
|
||||
response = requests.get(url=url, params=payload, headers=headers)
|
||||
return json.dumps(self._clean_brave_response(response.json()))
|
||||
|
||||
def _clean_brave_response(self, search_response, top_k=3):
|
||||
query = None
|
||||
clean_response = []
|
||||
if "query" in search_response:
|
||||
if "original" in search_response["query"]:
|
||||
query = search_response["query"]["original"]
|
||||
if "mixed" in search_response:
|
||||
mixed_results = search_response["mixed"]
|
||||
for m in mixed_results["main"][:top_k]:
|
||||
r_type = m["type"]
|
||||
results = search_response[r_type]["results"]
|
||||
if r_type == "web":
|
||||
# For web data - add a single output from the search
|
||||
idx = m["index"]
|
||||
selected_keys = [
|
||||
"type",
|
||||
"title",
|
||||
"url",
|
||||
"description",
|
||||
"date",
|
||||
"extra_snippets",
|
||||
]
|
||||
cleaned = {
|
||||
k: v for k, v in results[idx].items() if k in selected_keys
|
||||
}
|
||||
elif r_type == "faq":
|
||||
# For faw data - take a list of all the questions & answers
|
||||
selected_keys = ["type", "question", "answer", "title", "url"]
|
||||
cleaned = []
|
||||
for q in results:
|
||||
cleaned.append(
|
||||
{k: v for k, v in q.items() if k in selected_keys}
|
||||
)
|
||||
elif r_type == "infobox":
|
||||
idx = m["index"]
|
||||
selected_keys = [
|
||||
"type",
|
||||
"title",
|
||||
"url",
|
||||
"description",
|
||||
"long_desc",
|
||||
]
|
||||
cleaned = {
|
||||
k: v for k, v in results[idx].items() if k in selected_keys
|
||||
}
|
||||
elif r_type == "videos":
|
||||
selected_keys = [
|
||||
"type",
|
||||
"url",
|
||||
"title",
|
||||
"description",
|
||||
"date",
|
||||
]
|
||||
cleaned = []
|
||||
for q in results:
|
||||
cleaned.append(
|
||||
{k: v for k, v in q.items() if k in selected_keys}
|
||||
)
|
||||
elif r_type == "locations":
|
||||
# For faw data - take a list of all the questions & answers
|
||||
selected_keys = [
|
||||
"type",
|
||||
"title",
|
||||
"url",
|
||||
"description",
|
||||
"coordinates",
|
||||
"postal_address",
|
||||
"contact",
|
||||
"rating",
|
||||
"distance",
|
||||
"zoom_level",
|
||||
]
|
||||
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 TavilySearch:
|
||||
def __init__(self, api_key: str) -> None:
|
||||
self.api_key = api_key
|
||||
|
||||
async def search(self, query: str) -> str:
|
||||
response = requests.post(
|
||||
"https://api.tavily.com/search",
|
||||
json={"api_key": self.api_key, "query": query},
|
||||
)
|
||||
return json.dumps(self._clean_tavily_response(response.json()))
|
||||
|
||||
def _clean_tavily_response(self, search_response, top_k=3):
|
||||
return {"query": search_response["query"], "top_k": search_response["results"]}
|
||||
|
||||
|
||||
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=tempfile.mkdtemp(),
|
||||
)
|
||||
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":
|
||||
log.error(f"ipython tool error: ↓\n{res_out}")
|
||||
|
||||
message = ToolResponseMessage(
|
||||
call_id=tool_call.call_id,
|
||||
tool_name=tool_call.tool_name,
|
||||
content="\n".join(pieces),
|
||||
)
|
||||
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
|
||||
|
|
@ -1,90 +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 logging
|
||||
|
||||
import matplotlib
|
||||
from matplotlib.backend_bases import FigureManagerBase
|
||||
|
||||
# Import necessary components from Matplotlib
|
||||
from matplotlib.backends.backend_agg import FigureCanvasAgg
|
||||
|
||||
log = logging.getLogger(__name__)
|
||||
|
||||
|
||||
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"))
|
||||
log.info(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
|
||||
|
|
@ -4,6 +4,7 @@
|
|||
# This source code is licensed under the terms described in the LICENSE file in
|
||||
# the root directory of this source tree.
|
||||
|
||||
import os
|
||||
import tempfile
|
||||
|
||||
import pytest
|
||||
|
|
@ -68,7 +69,14 @@ def tool_runtime_memory() -> ProviderFixture:
|
|||
provider_id="memory-runtime",
|
||||
provider_type="inline::memory-runtime",
|
||||
config={},
|
||||
)
|
||||
),
|
||||
Provider(
|
||||
provider_id="brave-search",
|
||||
provider_type="inline::brave-search",
|
||||
config={
|
||||
"api_key": os.environ["BRAVE_SEARCH_API_KEY"],
|
||||
},
|
||||
),
|
||||
],
|
||||
)
|
||||
|
||||
|
|
@ -131,6 +139,20 @@ async def agents_stack(request, inference_model, safety_shield):
|
|||
)
|
||||
)
|
||||
tool_groups = [
|
||||
ToolGroupInput(
|
||||
tool_group_id="brave_search_group",
|
||||
tool_group=UserDefinedToolGroupDef(
|
||||
tools=[
|
||||
ToolDef(
|
||||
name="brave_search",
|
||||
description="brave_search",
|
||||
parameters=[],
|
||||
metadata={},
|
||||
),
|
||||
],
|
||||
),
|
||||
provider_id="brave-search",
|
||||
),
|
||||
ToolGroupInput(
|
||||
tool_group_id="memory_group",
|
||||
tool_group=UserDefinedToolGroupDef(
|
||||
|
|
@ -163,7 +185,7 @@ async def agents_stack(request, inference_model, safety_shield):
|
|||
],
|
||||
),
|
||||
provider_id="memory-runtime",
|
||||
)
|
||||
),
|
||||
]
|
||||
|
||||
test_stack = await construct_stack_for_test(
|
||||
|
|
|
|||
|
|
@ -50,7 +50,8 @@ def common_params(inference_model):
|
|||
sampling_params=SamplingParams(temperature=0.7, top_p=0.95),
|
||||
input_shields=[],
|
||||
output_shields=[],
|
||||
tools=[],
|
||||
available_tools=[],
|
||||
preprocessing_tools=[],
|
||||
max_infer_iters=5,
|
||||
)
|
||||
|
||||
|
|
@ -91,7 +92,7 @@ async def create_agent_turn_with_search_tool(
|
|||
agents_stack: Dict[str, object],
|
||||
search_query_messages: List[object],
|
||||
common_params: Dict[str, str],
|
||||
search_tool_definition: SearchToolDefinition,
|
||||
tool_name: str,
|
||||
) -> None:
|
||||
"""
|
||||
Create an agent turn with a search tool.
|
||||
|
|
@ -107,7 +108,7 @@ async def create_agent_turn_with_search_tool(
|
|||
agent_config = AgentConfig(
|
||||
**{
|
||||
**common_params,
|
||||
"tools": [search_tool_definition],
|
||||
"available_tools": [tool_name],
|
||||
}
|
||||
)
|
||||
|
||||
|
|
@ -254,7 +255,6 @@ class TestAgents:
|
|||
agent_config = AgentConfig(
|
||||
**{
|
||||
**common_params,
|
||||
"tools": [],
|
||||
"preprocessing_tools": ["memory"],
|
||||
"tool_choice": ToolChoice.auto,
|
||||
}
|
||||
|
|
@ -295,29 +295,11 @@ class TestAgents:
|
|||
if "BRAVE_SEARCH_API_KEY" not in os.environ:
|
||||
pytest.skip("BRAVE_SEARCH_API_KEY not set, skipping test")
|
||||
|
||||
search_tool_definition = SearchToolDefinition(
|
||||
type=AgentTool.brave_search.value,
|
||||
api_key=os.environ["BRAVE_SEARCH_API_KEY"],
|
||||
engine=SearchEngineType.brave,
|
||||
)
|
||||
await create_agent_turn_with_search_tool(
|
||||
agents_stack, search_query_messages, common_params, search_tool_definition
|
||||
)
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_create_agent_turn_with_tavily_search(
|
||||
self, agents_stack, search_query_messages, common_params
|
||||
):
|
||||
if "TAVILY_SEARCH_API_KEY" not in os.environ:
|
||||
pytest.skip("TAVILY_SEARCH_API_KEY not set, skipping test")
|
||||
|
||||
search_tool_definition = SearchToolDefinition(
|
||||
type=AgentTool.brave_search.value, # place holder only
|
||||
api_key=os.environ["TAVILY_SEARCH_API_KEY"],
|
||||
engine=SearchEngineType.tavily,
|
||||
)
|
||||
await create_agent_turn_with_search_tool(
|
||||
agents_stack, search_query_messages, common_params, search_tool_definition
|
||||
agents_stack,
|
||||
search_query_messages,
|
||||
common_params,
|
||||
"brave_search",
|
||||
)
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue