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More robust log step class, and new examples

This commit is contained in:
Aymeric 2024-12-09 18:34:29 +01:00
parent 146ee3dd32
commit a830b1721a
10 changed files with 787 additions and 281 deletions
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2
agents/agent_types.py
View File

@ -156,7 +156,7 @@ class AgentImage(AgentType, ImageType):
directory = tempfile.mkdtemp() directory = tempfile.mkdtemp()
self._path = os.path.join(directory, str(uuid.uuid4()) + ".png") self._path = os.path.join(directory, str(uuid.uuid4()) + ".png")
img.save(self._path) img.save(self._path, format="png")
return self._path return self._path

359
agents/agents.py
View File

@ -15,16 +15,14 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
import json import json
import logging
import re import re
import time import time
from typing import Any, Callable, Dict, List, Optional, Tuple, Union from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import rich from dataclasses import dataclass
from rich import markdown as rich_markdown
from transformers.utils import is_torch_available from transformers.utils import is_torch_available
import logging import logging
from .utils import console from .utils import console, parse_code_blob, parse_json_tool_call
from .agent_types import AgentAudio, AgentImage from .agent_types import AgentAudio, AgentImage
from .default_tools import BASE_PYTHON_TOOLS, FinalAnswerTool, setup_default_tools from .default_tools import BASE_PYTHON_TOOLS, FinalAnswerTool, setup_default_tools
from .llm_engine import HfApiEngine, MessageRole from .llm_engine import HfApiEngine, MessageRole
@ -47,200 +45,15 @@ from .tools import (
Tool, Tool,
get_tool_description_with_args, get_tool_description_with_args,
load_tool, load_tool,
Toolbox,
) )
LENGTH_TRUNCATE_REPORTS = 10000
def parse_json_blob(json_blob: str) -> Dict[str, str]:
try:
first_accolade_index = json_blob.find("{")
last_accolade_index = [a.start() for a in list(re.finditer("}", json_blob))][-1]
json_blob = json_blob[first_accolade_index : last_accolade_index + 1].replace('\\"', "'")
json_data = json.loads(json_blob, strict=False)
return json_data
except json.JSONDecodeError as e:
place = e.pos
if json_blob[place - 1 : place + 2] == "},\n":
raise ValueError(
"JSON is invalid: you probably tried to provide multiple tool calls in one action. PROVIDE ONLY ONE TOOL CALL."
)
raise ValueError(
f"The JSON blob you used is invalid due to the following error: {e}.\n"
f"JSON blob was: {json_blob}, decoding failed on that specific part of the blob:\n"
f"'{json_blob[place-4:place+5]}'."
)
except Exception as e:
raise ValueError(f"Error in parsing the JSON blob: {e}")
def parse_code_blob(code_blob: str) -> str:
try:
pattern = r"```(?:py|python)?\n(.*?)\n```"
match = re.search(pattern, code_blob, re.DOTALL)
return match.group(1).strip()
except Exception as e:
raise ValueError(
f"""
The code blob you used is invalid: due to the following error: {e}
This means that the regex pattern {pattern} was not respected: make sure to include code with the correct pattern, for instance:
Thoughts: Your thoughts
Code:
```py
# Your python code here
```<end_action>"""
)
def parse_json_tool_call(json_blob: str) -> Tuple[str, Dict[str, str]]:
json_blob = json_blob.replace("```json", "").replace("```", "")
tool_call = parse_json_blob(json_blob)
if "action" in tool_call and "action_input" in tool_call:
return tool_call["action"], tool_call["action_input"]
elif "action" in tool_call:
return tool_call["action"], None
else:
missing_keys = [key for key in ['action', 'action_input'] if key not in tool_call]
error_msg = f"Missing keys: {missing_keys} in blob {tool_call}"
console.print(f"[bold red]{error_msg}[/bold red]")
raise ValueError(error_msg)
def parse_text_tool_call(text: str) -> Tuple[str, Union[str, Dict[str, str]]]:
"""
Expects a text in the format: 'Action:', 'Action input:', 'Observation:'. 'Action input:' contains a json string with input arguments.
"""
try:
if "Observation:" in text:
text = text.split("Observation:")[0]
if "Action:" in text:
text = text.split("Action:")[1]
tool_name, tool_input = text.split("Action input:")
if "{" in tool_input:
tool_input = parse_json_blob(tool_input)
else:
tool_input = tool_input.strip().replace('"', "")
return tool_name.strip().replace('"', "").replace("\\", ""), tool_input
except Exception as e:
raise ValueError(
f"Error in parsing the text tool call: {e}. Be sure to provide the correct format. DO NOT repeat your previous incorrect tool call."
)
def to_text(input: Union[List[Dict[str, str]], Dict[str, str], str]) -> str:
if isinstance(input, list):
return "\n".join([m["content"] for m in input])
elif isinstance(input, dict):
return input["content"]
else:
return input
HUGGINGFACE_DEFAULT_TOOLS = {} HUGGINGFACE_DEFAULT_TOOLS = {}
_tools_are_initialized = False _tools_are_initialized = False
class Toolbox:
"""
The toolbox contains all tools that the agent can perform operations with, as well as a few methods to
manage them.
Args:
tools (`List[Tool]`):
The list of tools to instantiate the toolbox with
add_base_tools (`bool`, defaults to `False`, *optional*, defaults to `False`):
Whether to add the tools available within `transformers` to the toolbox.
"""
def __init__(self, tools: List[Tool], add_base_tools: bool = False):
self._tools = {tool.name: tool for tool in tools}
if add_base_tools:
self.add_base_tools()
# self._load_tools_if_needed()
def add_base_tools(self, add_python_interpreter: bool = False):
global _tools_are_initialized
global HUGGINGFACE_DEFAULT_TOOLS
if not _tools_are_initialized:
HUGGINGFACE_DEFAULT_TOOLS = setup_default_tools()
_tools_are_initialized = True
for tool in HUGGINGFACE_DEFAULT_TOOLS.values():
if tool.name != "python_interpreter" or add_python_interpreter:
self.add_tool(tool)
# self._load_tools_if_needed()
@property
def tools(self) -> Dict[str, Tool]:
"""Get all tools currently in the toolbox"""
return self._tools
def show_tool_descriptions(self, tool_description_template: str = None) -> str:
"""
Returns the description of all tools in the toolbox
Args:
tool_description_template (`str`, *optional*):
The template to use to describe the tools. If not provided, the default template will be used.
"""
return "\n".join(
[get_tool_description_with_args(tool, tool_description_template) for tool in self._tools.values()]
)
def add_tool(self, tool: Tool):
"""
Adds a tool to the toolbox
Args:
tool (`Tool`):
The tool to add to the toolbox.
"""
if tool.name in self._tools:
raise KeyError(f"Error: tool '{tool.name}' already exists in the toolbox.")
self._tools[tool.name] = tool
def remove_tool(self, tool_name: str):
"""
Removes a tool from the toolbox
Args:
tool_name (`str`):
The tool to remove from the toolbox.
"""
if tool_name not in self._tools:
raise KeyError(
f"Error: tool {tool_name} not found in toolbox for removal, should be instead one of {list(self._tools.keys())}."
)
del self._tools[tool_name]
def update_tool(self, tool: Tool):
"""
Updates a tool in the toolbox according to its name.
Args:
tool (`Tool`):
The tool to update to the toolbox.
"""
if tool.name not in self._tools:
raise KeyError(
f"Error: tool {tool.name} not found in toolbox for update, should be instead one of {list(self._tools.keys())}."
)
self._tools[tool.name] = tool
def clear_toolbox(self):
"""Clears the toolbox"""
self._tools = {}
# def _load_tools_if_needed(self):
# for name, tool in self._tools.items():
# if not isinstance(tool, Tool):
# task_or_repo_id = tool.task if tool.repo_id is None else tool.repo_id
# self._tools[name] = load_tool(task_or_repo_id)
def __repr__(self):
toolbox_description = "Toolbox contents:\n"
for tool in self._tools.values():
toolbox_description += f"\t{tool.name}: {tool.description}\n"
return toolbox_description
class AgentError(Exception): class AgentError(Exception):
"""Base class for other agent-related exceptions""" """Base class for other agent-related exceptions"""
@ -274,6 +87,25 @@ class AgentGenerationError(AgentError):
pass pass
@dataclass
class ActionStep:
tool_call: str | None = None
start_time: float | None = None
end_time: float | None = None
iteration: int | None = None
final_answer: Any = None
error: AgentError | None = None
step_duration: float | None = None
@dataclass
class PlanningStep:
plan: str
facts: str
@dataclass
class TaskStep:
system_prompt: str
task: str
def format_prompt_with_tools(toolbox: Toolbox, prompt_template: str, tool_description_template: str) -> str: def format_prompt_with_tools(toolbox: Toolbox, prompt_template: str, tool_description_template: str) -> str:
tool_descriptions = toolbox.show_tool_descriptions(tool_description_template) tool_descriptions = toolbox.show_tool_descriptions(tool_description_template)
@ -392,7 +224,7 @@ class Agent:
self.system_prompt = format_prompt_with_imports( self.system_prompt = format_prompt_with_imports(
self.system_prompt, list(set(LIST_SAFE_MODULES) | set(self.authorized_imports)) self.system_prompt, list(set(LIST_SAFE_MODULES) | set(self.authorized_imports))
) )
self.logs = [{"system_prompt": self.system_prompt, "task": self.task}] self.logs = [TaskStep(system_prompt=self.system_prompt, task=self.task)]
console.rule("New task", characters='=') console.rule("New task", characters='=')
console.print(self.task) console.print(self.task)
@ -401,53 +233,56 @@ class Agent:
Reads past llm_outputs, actions, and observations or errors from the logs into a series of messages Reads past llm_outputs, actions, and observations or errors from the logs into a series of messages
that can be used as input to the LLM. that can be used as input to the LLM.
""" """
prompt_message = {"role": MessageRole.SYSTEM, "content": self.logs[0]["system_prompt"]} prompt_message = {"role": MessageRole.SYSTEM, "content": self.logs[0].system_prompt}
task_message = { task_message = {
"role": MessageRole.USER, "role": MessageRole.USER,
"content": "Task: " + self.logs[0]["task"], "content": "Task: " + self.logs[0].task,
} }
if summary_mode: if summary_mode:
memory = [task_message] memory = [task_message]
else: else:
memory = [prompt_message, task_message] memory = [prompt_message, task_message]
for i, step_log in enumerate(self.logs[1:]): for i, step_log in enumerate(self.logs[1:]):
if "llm_output" in step_log and not summary_mode:
thought_message = {"role": MessageRole.ASSISTANT, "content": step_log["llm_output"].strip()} if isinstance(step_log, PlanningStep):
memory.append(thought_message)
if "facts" in step_log:
thought_message = { thought_message = {
"role": MessageRole.ASSISTANT, "role": MessageRole.ASSISTANT,
"content": "[FACTS LIST]:\n" + step_log["facts"].strip(), "content": "[FACTS LIST]:\n" + step_log.facts.strip(),
} }
memory.append(thought_message) memory.append(thought_message)
if "plan" in step_log and not summary_mode: if not summary_mode:
thought_message = {"role": MessageRole.ASSISTANT, "content": "[PLAN]:\n" + step_log["plan"].strip()} thought_message = {"role": MessageRole.ASSISTANT, "content": "[PLAN]:\n" + step_log.plan.strip()}
memory.append(thought_message) memory.append(thought_message)
if "tool_call" in step_log and summary_mode: elif isinstance(step_log, TaskStep):
task_message = {
"role": MessageRole.USER,
"content": "New task:\n" + step_log.task,
}
memory.append(task_message)
elif isinstance(step_log, ActionStep):
if step_log.llm_output is not None and not summary_mode:
thought_message = {"role": MessageRole.ASSISTANT, "content": step_log.llm_output.strip()}
memory.append(thought_message)
if step_log.tool_call is not None and summary_mode:
tool_call_message = { tool_call_message = {
"role": MessageRole.ASSISTANT, "role": MessageRole.ASSISTANT,
"content": f"[STEP {i} TOOL CALL]: " + str(step_log["tool_call"]).strip(), "content": f"[STEP {i} TOOL CALL]: " + str(step_log.tool_call).strip(),
} }
memory.append(tool_call_message) memory.append(tool_call_message)
if "task" in step_log: if step_log.error is not None or step_log.observation is not None:
tool_call_message = { if step_log.error is not None:
"role": MessageRole.USER,
"content": "New task:\n" + step_log["task"],
}
memory.append(tool_call_message)
if "error" in step_log or "observation" in step_log:
if "error" in step_log:
message_content = ( message_content = (
f"[OUTPUT OF STEP {i}] -> Error:\n" f"[OUTPUT OF STEP {i}] -> Error:\n"
+ str(step_log["error"]) + str(step_log.error)
+ "\nNow let's retry: take care not to repeat previous errors! If you have retried several times, try a completely different approach.\n" + "\nNow let's retry: take care not to repeat previous errors! If you have retried several times, try a completely different approach.\n"
) )
elif "observation" in step_log: elif step_log.observation is not None:
message_content = f"[OUTPUT OF STEP {i}] -> Observation:\n{step_log['observation']}" message_content = f"[OUTPUT OF STEP {i}] -> Observation:\n{step_log.observation}"
tool_response_message = {"role": MessageRole.TOOL_RESPONSE, "content": message_content} tool_response_message = {"role": MessageRole.TOOL_RESPONSE, "content": message_content}
memory.append(tool_response_message) memory.append(tool_response_message)
@ -742,7 +577,7 @@ class ReactAgent(Agent):
if reset: if reset:
self.initialize_for_run() self.initialize_for_run()
else: else:
self.logs.append({"task": task}) self.logs.append(TaskStep(task=task))
if stream: if stream:
return self.stream_run(task) return self.stream_run(task)
else: else:
@ -756,31 +591,31 @@ class ReactAgent(Agent):
iteration = 0 iteration = 0
while final_answer is None and iteration < self.max_iterations: while final_answer is None and iteration < self.max_iterations:
step_start_time = time.time() step_start_time = time.time()
step_log_entry = {"iteration": iteration, "start_time": step_start_time} step_log = ActionStep(iteration=iteration, start_time=step_start_time)
try: try:
self.step(step_log_entry) self.step(step_log)
if "final_answer" in step_log_entry: if step_log.final_answer is not None:
final_answer = step_log_entry["final_answer"] final_answer = step_log.final_answer
except AgentError as e: except AgentError as e:
step_log_entry["error"] = e step_log.error = e
finally: finally:
step_end_time = time.time() step_log.step_end_time = time.time()
step_log_entry["step_end_time"] = step_end_time step_log.step_duration = step_log.step_end_time - step_start_time
step_log_entry["step_duration"] = step_end_time - step_start_time self.logs.append(step_log)
self.logs.append(step_log_entry)
for callback in self.step_callbacks: for callback in self.step_callbacks:
callback(step_log_entry) callback(step_log)
iteration += 1 iteration += 1
yield step_log_entry yield step_log
if final_answer is None and iteration == self.max_iterations: if final_answer is None and iteration == self.max_iterations:
error_message = "Reached max iterations." error_message = "Reached max iterations."
final_step_log = {"error": AgentMaxIterationsError(error_message)}
self.logs.append(final_step_log)
console.print(f"[bold red]{error_message}") console.print(f"[bold red]{error_message}")
final_step_log = ActionStep(error=AgentMaxIterationsError(error_message))
self.logs.append(final_step_log)
final_answer = self.provide_final_answer(task) final_answer = self.provide_final_answer(task)
final_step_log["final_answer"] = final_answer final_step_log.final_answer = final_answer
final_step_log["step_duration"] = 0 final_step_log.step_end_time = time.time()
final_step_log.step_duration = step_log.step_end_time - step_start_time
for callback in self.step_callbacks: for callback in self.step_callbacks:
callback(final_step_log) callback(final_step_log)
yield final_step_log yield final_step_log
@ -795,32 +630,32 @@ class ReactAgent(Agent):
iteration = 0 iteration = 0
while final_answer is None and iteration < self.max_iterations: while final_answer is None and iteration < self.max_iterations:
step_start_time = time.time() step_start_time = time.time()
step_log_entry = {"iteration": iteration, "start_time": step_start_time} step_log = ActionStep(iteration=iteration, start_time=step_start_time)
try: try:
if self.planning_interval is not None and iteration % self.planning_interval == 0: if self.planning_interval is not None and iteration % self.planning_interval == 0:
self.planning_step(task, is_first_step=(iteration == 0), iteration=iteration) self.planning_step(task, is_first_step=(iteration == 0), iteration=iteration)
self.step(step_log_entry) self.step(step_log)
if "final_answer" in step_log_entry: if step_log.final_answer is not None:
final_answer = step_log_entry["final_answer"] final_answer = step_log.final_answer
except AgentError as e: except AgentError as e:
step_log_entry["error"] = e step_log.error = e
finally: finally:
step_end_time = time.time() step_end_time = time.time()
step_log_entry["step_end_time"] = step_end_time step_log.step_end_time = step_end_time
step_log_entry["step_duration"] = step_end_time - step_start_time step_log.step_duration = step_end_time - step_start_time
self.logs.append(step_log_entry) self.logs.append(step_log)
for callback in self.step_callbacks: for callback in self.step_callbacks:
callback(step_log_entry) callback(step_log)
iteration += 1 iteration += 1
if final_answer is None and iteration == self.max_iterations: if final_answer is None and iteration == self.max_iterations:
error_message = "Reached max iterations." error_message = "Reached max iterations."
final_step_log = {"error": AgentMaxIterationsError(error_message)} final_step_log = ActionStep(error=AgentMaxIterationsError(error_message))
self.logs.append(final_step_log) self.logs.append(final_step_log)
console.print(f"[bold red]{error_message}") console.print(f"[bold red]{error_message}")
final_answer = self.provide_final_answer(task) final_answer = self.provide_final_answer(task)
final_step_log["final_answer"] = final_answer final_step_log.final_answer = final_answer
final_step_log["step_duration"] = 0 final_step_log.step_duration = 0
for callback in self.step_callbacks: for callback in self.step_callbacks:
callback(final_step_log) callback(final_step_log)
@ -875,7 +710,7 @@ Now begin!""",
``` ```
{answer_facts} {answer_facts}
```""".strip() ```""".strip()
self.logs.append({"plan": final_plan_redaction, "facts": final_facts_redaction}) self.logs.append(PlanningStep(plan=final_plan_redaction, facts=final_facts_redaction))
console.rule("[orange]Initial plan") console.rule("[orange]Initial plan")
console.print(final_plan_redaction) console.print(final_plan_redaction)
else: # update plan else: # update plan
@ -921,8 +756,8 @@ Now begin!""",
``` ```
{facts_update} {facts_update}
```""" ```"""
self.logs.append({"plan": final_plan_redaction, "facts": final_facts_redaction}) self.logs.append(PlanningStep(plan=final_plan_redaction, facts=final_facts_redaction))
console.rule("Updated plan") console.rule("[orange]Updated plan")
console.print(final_plan_redaction) console.print(final_plan_redaction)
@ -959,7 +794,7 @@ class ReactJsonAgent(ReactAgent):
**kwargs, **kwargs,
) )
def step(self, log_entry: Dict[str, Any]): def step(self, log_entry: ActionStep):
""" """
Perform one step in the ReAct framework: the agent thinks, acts, and observes the result. Perform one step in the ReAct framework: the agent thinks, acts, and observes the result.
The errors are raised here, they are caught and logged in the run() method. The errors are raised here, they are caught and logged in the run() method.
@ -970,7 +805,7 @@ class ReactJsonAgent(ReactAgent):
console.rule("New step") console.rule("New step")
# Add new step in logs # Add new step in logs
log_entry["agent_memory"] = agent_memory.copy() log_entry.agent_memory = agent_memory.copy()
if self.verbose: if self.verbose:
console.rule("Calling LLM with this last message:") console.rule("Calling LLM with this last message:")
@ -985,7 +820,7 @@ class ReactJsonAgent(ReactAgent):
raise AgentGenerationError(f"Error in generating llm output: {e}.") raise AgentGenerationError(f"Error in generating llm output: {e}.")
console.rule("===== Output message of the LLM: =====") console.rule("===== Output message of the LLM: =====")
console.print(llm_output) console.print(llm_output)
log_entry["llm_output"] = llm_output log_entry.llm_output = llm_output
# Parse # Parse
console.rule("===== Extracting action =====") console.rule("===== Extracting action =====")
@ -996,8 +831,8 @@ class ReactJsonAgent(ReactAgent):
except Exception as e: except Exception as e:
raise AgentParsingError(f"Could not parse the given action: {e}.") raise AgentParsingError(f"Could not parse the given action: {e}.")
log_entry["rationale"] = rationale log_entry.rationale = rationale
log_entry["tool_call"] = {"tool_name": tool_name, "tool_arguments": arguments} log_entry.tool_call = {"tool_name": tool_name, "tool_arguments": arguments}
# Execute # Execute
console.print("=== Agent thoughts:") console.print("=== Agent thoughts:")
@ -1015,7 +850,7 @@ class ReactJsonAgent(ReactAgent):
answer = arguments answer = arguments
else: else:
answer = arguments answer = arguments
log_entry["final_answer"] = answer log_entry.final_answer = answer
return answer return answer
else: else:
if arguments is None: if arguments is None:
@ -1033,7 +868,7 @@ class ReactJsonAgent(ReactAgent):
updated_information = f"Stored '{observation_name}' in memory." updated_information = f"Stored '{observation_name}' in memory."
else: else:
updated_information = str(observation).strip() updated_information = str(observation).strip()
log_entry["observation"] = updated_information log_entry.observation = updated_information
return log_entry return log_entry
@ -1088,7 +923,7 @@ class ReactCodeAgent(ReactAgent):
console.rule("New step") console.rule("New step")
# Add new step in logs # Add new step in logs
log_entry["agent_memory"] = agent_memory.copy() log_entry.agent_memory = agent_memory.copy()
if self.verbose: if self.verbose:
console.print("===== Calling LLM with these last messages: =====") console.print("===== Calling LLM with these last messages: =====")
@ -1105,7 +940,7 @@ class ReactCodeAgent(ReactAgent):
if self.verbose: if self.verbose:
console.rule("Output message of the LLM:") console.rule("Output message of the LLM:")
console.print(llm_output) console.print(llm_output)
log_entry["llm_output"] = llm_output log_entry.llm_output = llm_output
# Parse # Parse
try: try:
@ -1120,8 +955,8 @@ class ReactCodeAgent(ReactAgent):
error_msg = f"Error in code parsing: {e}. Make sure to provide correct code" error_msg = f"Error in code parsing: {e}. Make sure to provide correct code"
raise AgentParsingError(error_msg) raise AgentParsingError(error_msg)
log_entry["rationale"] = rationale log_entry.rationale = rationale
log_entry["tool_call"] = {"tool_name": "code interpreter", "tool_arguments": code_action} log_entry.tool_call = {"tool_name": "code interpreter", "tool_arguments": code_action}
# Execute # Execute
self.log_rationale_code_action(rationale, code_action) self.log_rationale_code_action(rationale, code_action)
@ -1145,8 +980,8 @@ class ReactCodeAgent(ReactAgent):
if result is not None: if result is not None:
console.print("Last output from code snippet:") console.print("Last output from code snippet:")
console.print(str(result)) console.print(str(result))
observation += "Last output from code snippet:\n" + str(result)[:100000] observation += "Last output from code snippet:\n" + str(result)[:LENGTH_TRUNCATE_REPORTS]
log_entry["observation"] = observation log_entry.observation = observation
except Exception as e: except Exception as e:
error_msg = f"Code execution failed due to the following error:\n{str(e)}" error_msg = f"Code execution failed due to the following error:\n{str(e)}"
if "'dict' object has no attribute 'read'" in str(e): if "'dict' object has no attribute 'read'" in str(e):
@ -1156,13 +991,9 @@ class ReactCodeAgent(ReactAgent):
if line[: len("final_answer")] == "final_answer": if line[: len("final_answer")] == "final_answer":
console.print("Final answer:") console.print("Final answer:")
console.print(f"[bold]{result}") console.print(f"[bold]{result}")
log_entry["final_answer"] = result log_entry.final_answer = result
return result return result
LENGTH_TRUNCATE_REPORTS = 1000
class ManagedAgent: class ManagedAgent:
def __init__(self, agent, name, description, additional_prompting=None, provide_run_summary=False): def __init__(self, agent, name, description, additional_prompting=None, provide_run_summary=False):
self.agent = agent self.agent = agent

6
agents/monitoring.py
View File

@ -102,7 +102,7 @@ class Monitor:
self.total_output_token_count = 0 self.total_output_token_count = 0
def update_metrics(self, step_log): def update_metrics(self, step_log):
step_duration = step_log["step_duration"] step_duration = step_log.step_duration
self.step_durations.append(step_duration) self.step_durations.append(step_duration)
console.print(f"Step {len(self.step_durations)}:") console.print(f"Step {len(self.step_durations)}:")
console.print(f"- Time taken: {step_duration:.2f} seconds") console.print(f"- Time taken: {step_duration:.2f} seconds")
@ -110,6 +110,6 @@ class Monitor:
if getattr(self.tracked_llm_engine, "last_input_token_count", None) is not None: if getattr(self.tracked_llm_engine, "last_input_token_count", None) is not None:
self.total_input_token_count += self.tracked_llm_engine.last_input_token_count self.total_input_token_count += self.tracked_llm_engine.last_input_token_count
self.total_output_token_count += self.tracked_llm_engine.last_output_token_count self.total_output_token_count += self.tracked_llm_engine.last_output_token_count
console.print(f"- Input tokens: {self.total_input_token_count}") console.print(f"- Input tokens: {self.total_input_token_count:,}")
console.print(f"- Output tokens: {self.total_output_token_count}") console.print(f"- Output tokens: {self.total_output_token_count:,}")

103
agents/tools.py
View File

@ -1000,3 +1000,106 @@ def tool(tool_function: Callable) -> Tool:
SpecificTool.__name__ = class_name SpecificTool.__name__ = class_name
return SpecificTool() return SpecificTool()
class Toolbox:
"""
The toolbox contains all tools that the agent can perform operations with, as well as a few methods to
manage them.
Args:
tools (`List[Tool]`):
The list of tools to instantiate the toolbox with
add_base_tools (`bool`, defaults to `False`, *optional*, defaults to `False`):
Whether to add the tools available within `transformers` to the toolbox.
"""
def __init__(self, tools: List[Tool], add_base_tools: bool = False):
self._tools = {tool.name: tool for tool in tools}
if add_base_tools:
self.add_base_tools()
# self._load_tools_if_needed()
def add_base_tools(self, add_python_interpreter: bool = False):
global _tools_are_initialized
global HUGGINGFACE_DEFAULT_TOOLS
if not _tools_are_initialized:
HUGGINGFACE_DEFAULT_TOOLS = setup_default_tools()
_tools_are_initialized = True
for tool in HUGGINGFACE_DEFAULT_TOOLS.values():
if tool.name != "python_interpreter" or add_python_interpreter:
self.add_tool(tool)
# self._load_tools_if_needed()
@property
def tools(self) -> Dict[str, Tool]:
"""Get all tools currently in the toolbox"""
return self._tools
def show_tool_descriptions(self, tool_description_template: str = None) -> str:
"""
Returns the description of all tools in the toolbox
Args:
tool_description_template (`str`, *optional*):
The template to use to describe the tools. If not provided, the default template will be used.
"""
return "\n".join(
[get_tool_description_with_args(tool, tool_description_template) for tool in self._tools.values()]
)
def add_tool(self, tool: Tool):
"""
Adds a tool to the toolbox
Args:
tool (`Tool`):
The tool to add to the toolbox.
"""
if tool.name in self._tools:
raise KeyError(f"Error: tool '{tool.name}' already exists in the toolbox.")
self._tools[tool.name] = tool
def remove_tool(self, tool_name: str):
"""
Removes a tool from the toolbox
Args:
tool_name (`str`):
The tool to remove from the toolbox.
"""
if tool_name not in self._tools:
raise KeyError(
f"Error: tool {tool_name} not found in toolbox for removal, should be instead one of {list(self._tools.keys())}."
)
del self._tools[tool_name]
def update_tool(self, tool: Tool):
"""
Updates a tool in the toolbox according to its name.
Args:
tool (`Tool`):
The tool to update to the toolbox.
"""
if tool.name not in self._tools:
raise KeyError(
f"Error: tool {tool.name} not found in toolbox for update, should be instead one of {list(self._tools.keys())}."
)
self._tools[tool.name] = tool
def clear_toolbox(self):
"""Clears the toolbox"""
self._tools = {}
# def _load_tools_if_needed(self):
# for name, tool in self._tools.items():
# if not isinstance(tool, Tool):
# task_or_repo_id = tool.task if tool.repo_id is None else tool.repo_id
# self._tools[name] = load_tool(task_or_repo_id)
def __repr__(self):
toolbox_description = "Toolbox contents:\n"
for tool in self._tools.values():
toolbox_description += f"\t{tool.name}: {tool.description}\n"
return toolbox_description

73
agents/utils.py
View File

@ -1,3 +1,22 @@
#!/usr/bin/env python
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import re
from typing import Tuple, Dict
from transformers.utils.import_utils import _is_package_available from transformers.utils.import_utils import _is_package_available
@ -8,3 +27,57 @@ def is_pygments_available():
from rich.console import Console from rich.console import Console
console = Console() console = Console()
def parse_json_blob(json_blob: str) -> Dict[str, str]:
try:
first_accolade_index = json_blob.find("{")
last_accolade_index = [a.start() for a in list(re.finditer("}", json_blob))][-1]
json_blob = json_blob[first_accolade_index : last_accolade_index + 1].replace('\\"', "'")
json_data = json.loads(json_blob, strict=False)
return json_data
except json.JSONDecodeError as e:
place = e.pos
if json_blob[place - 1 : place + 2] == "},\n":
raise ValueError(
"JSON is invalid: you probably tried to provide multiple tool calls in one action. PROVIDE ONLY ONE TOOL CALL."
)
raise ValueError(
f"The JSON blob you used is invalid due to the following error: {e}.\n"
f"JSON blob was: {json_blob}, decoding failed on that specific part of the blob:\n"
f"'{json_blob[place-4:place+5]}'."
)
except Exception as e:
raise ValueError(f"Error in parsing the JSON blob: {e}")
def parse_code_blob(code_blob: str) -> str:
try:
pattern = r"```(?:py|python)?\n(.*?)\n```"
match = re.search(pattern, code_blob, re.DOTALL)
return match.group(1).strip()
except Exception as e:
raise ValueError(
f"""
The code blob you used is invalid: due to the following error: {e}
This means that the regex pattern {pattern} was not respected: make sure to include code with the correct pattern, for instance:
Thoughts: Your thoughts
Code:
```py
# Your python code here
```<end_action>"""
)
def parse_json_tool_call(json_blob: str) -> Tuple[str, Dict[str, str]]:
json_blob = json_blob.replace("```json", "").replace("```", "")
tool_call = parse_json_blob(json_blob)
if "action" in tool_call and "action_input" in tool_call:
return tool_call["action"], tool_call["action_input"]
elif "action" in tool_call:
return tool_call["action"], None
else:
missing_keys = [key for key in ['action', 'action_input'] if key not in tool_call]
error_msg = f"Missing keys: {missing_keys} in blob {tool_call}"
console.print(f"[bold red]{error_msg}[/bold red]")
raise ValueError(error_msg)

36
examples/agent_planning.py Normal file
View File

@ -0,0 +1,36 @@
from agents import load_tool, ReactCodeAgent, ReactJsonAgent, HfApiEngine
from agents.default_tools import PythonInterpreterTool
# Import tool from Hub
image_generation_tool = load_tool("m-ric/text-to-image", cache=False)
from agents.search import DuckDuckGoSearchTool
search_tool = DuckDuckGoSearchTool()
llm_engine = HfApiEngine("Qwen/Qwen2.5-72B-Instruct")
agent = ReactCodeAgent(tools=[search_tool], llm_engine=llm_engine, planning_interval=3)
# Run it!
print("Let's run the Code agent:")
result = agent.run(
"How long would a cheetah at full speed take to run the length of Pont Alexandre III?",
)
print("RESULT:", result)
code_tool = PythonInterpreterTool()
agent = ReactJsonAgent(tools=[search_tool, code_tool], llm_engine=llm_engine, planning_interval=3)
print("====================")
print("====================")
print("Now let's run the JSON agent:")
result = agent.run(
"How long would a cheetah at full speed take to run the length of Pont Alexandre III?",
)
print("RESULT:", result)

4
examples/agent_with_tools.py
View File

@ -1,11 +1,9 @@
from agents import load_tool, ReactCodeAgent, HfApiEngine from agents import load_tool, ReactCodeAgent, HfApiEngine
from agents.search import DuckDuckGoSearchTool
# Import tool from Hub # Import tool from Hub
image_generation_tool = load_tool("m-ric/text-to-image", cache=False) image_generation_tool = load_tool("m-ric/text-to-image", cache=False)
# Import tool from LangChain
from agents.search import DuckDuckGoSearchTool
search_tool = DuckDuckGoSearchTool() search_tool = DuckDuckGoSearchTool()
llm_engine = HfApiEngine("Qwen/Qwen2.5-72B-Instruct") llm_engine = HfApiEngine("Qwen/Qwen2.5-72B-Instruct")

116
examples/local_smollm.py Normal file
View File

@ -0,0 +1,116 @@
from agents.llm_engine import TransformersEngine
from agents import CodeAgent, ReactJsonAgent
import requests
from datetime import datetime
model_repo="andito/SmolLM2-1.7B-Instruct-F16-GGUF"
model_filename="smollm2-1.7b-8k-dpo-f16.gguf"
import random
from llama_cpp import Llama
model = Llama.from_pretrained(
repo_id=model_repo,
filename=model_filename,
n_ctx=8192,
verbose=False
)
print("Model initialized")
def llm_engine(messages, stop_sequences=["Task", "<|endoftext|>"]) -> str:
output = ""
for chunk in model.create_chat_completion(
messages=messages,
max_tokens=2048,
temperature=0.0,
top_p=1.0,
top_k=50,
repeat_penalty=1.0,
stream=True
):
content = chunk['choices'][0]['delta'].get('content')
if content:
if content in ["<end_action>", "<|endoftext|>"]:
break
output += content
return output
system_prompt = """You are an expert in composing functions. You are given a question and a set of possible functions.
Based on the question, you will need to make one or more function/tool calls to achieve the purpose.
If none of the functions can be used, point it out and refuse to answer.
If the given question lacks the parameters required by the function, also point it out.
You have access to the following tools:
<<tool_descriptions>>
<<managed_agents_descriptions>>
You can use imports in your code, but only from the following list of modules: <<authorized_imports>>
The output MUST strictly adhere to the following format, and NO other text MUST be included.
The example format is as follows. Please make sure the parameter type is correct. If no function call is needed, please make the tool calls an empty list '[]'.
<tool_call>[
{"name": "func_name1", "arguments": {"argument1": "value1", "argument2": "value2"}},
... (more tool calls as required)
]</tool_call>"""
from agents import tool
import webbrowser
@tool
def get_random_number_between(min: int, max: int) -> int:
"""
Gets a random number between min and max.
Args:
min: The minimum number.
max: The maximum number.
Returns:
A random number between min and max.
"""
return random.randint(min, max)
@tool
def get_weather(city: str) -> str:
"""
Returns the weather forecast for a given city.
Args:
city: The name of the city.
Returns:
A string with a mock weather forecast.
"""
url = 'https://wttr.in/{}?format=+%C,+%t'.format(city)
res = requests.get(url).text
return f"The weather in {city} is {res.split(',')[0]} with a high of {res.split(',')[1][:-2]} degrees Celsius."
@tool
def get_current_time() -> str:
"""
This is a tool that returns the current time.
It returns the current time as HH:MM.
"""
return f"The current time is {datetime.now().hour}:{datetime.now().minute}."
@tool
def open_webbrowser(url: str) -> str:
"""
This is a tool that opens a web browser to the given website.
If the user asks to open a website or a browser, you should use this tool.
Args:
url: The url to open.
"""
webbrowser.open(url)
return f"I opened {url.replace('https://', '').replace('www.', '')} in the browser."
agent = ReactJsonAgent(llm_engine = llm_engine, tools=[get_current_time, open_webbrowser, get_random_number_between, get_weather])
print("Agent initialized!")
agent.run("What's the weather like in London?")

347
poetry.lock generated
View File

@ -136,6 +136,17 @@ files = [
{file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"}, {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
] ]
[[package]]
name = "diskcache"
version = "5.6.3"
description = "Disk Cache -- Disk and file backed persistent cache."
optional = false
python-versions = ">=3"
files = [
{file = "diskcache-5.6.3-py3-none-any.whl", hash = "sha256:5e31b2d5fbad117cc363ebaf6b689474db18a1f6438bc82358b024abd4c2ca19"},
{file = "diskcache-5.6.3.tar.gz", hash = "sha256:2c3a3fa2743d8535d832ec61c2054a1641f41775aa7c556758a109941e33e4fc"},
]
[[package]] [[package]]
name = "exceptiongroup" name = "exceptiongroup"
version = "1.2.2" version = "1.2.2"
@ -264,6 +275,45 @@ files = [
{file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"}, {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
] ]
[[package]]
name = "jinja2"
version = "3.1.4"
description = "A very fast and expressive template engine."
optional = false
python-versions = ">=3.7"
files = [
{file = "jinja2-3.1.4-py3-none-any.whl", hash = "sha256:bc5dd2abb727a5319567b7a813e6a2e7318c39f4f487cfe6c89c6f9c7d25197d"},
{file = "jinja2-3.1.4.tar.gz", hash = "sha256:4a3aee7acbbe7303aede8e9648d13b8bf88a429282aa6122a993f0ac800cb369"},
]
[package.dependencies]
MarkupSafe = ">=2.0"
[package.extras]
i18n = ["Babel (>=2.7)"]
[[package]]
name = "llama-cpp-python"
version = "0.3.4"
description = "Python bindings for the llama.cpp library"
optional = false
python-versions = ">=3.8"
files = [
{file = "llama_cpp_python-0.3.4.tar.gz", hash = "sha256:52b37129c282753edf89213e852b8b46664b059116ae2213da9cb4a45374d23d"},
]
[package.dependencies]
diskcache = ">=5.6.1"
jinja2 = ">=2.11.3"
numpy = ">=1.20.0"
typing-extensions = ">=4.5.0"
[package.extras]
all = ["llama_cpp_python[dev,server,test]"]
dev = ["black (>=23.3.0)", "httpx (>=0.24.1)", "mkdocs (>=1.4.3)", "mkdocs-material (>=9.1.18)", "mkdocstrings[python] (>=0.22.0)", "pytest (>=7.4.0)", "twine (>=4.0.2)"]
server = ["PyYAML (>=5.1)", "fastapi (>=0.100.0)", "pydantic-settings (>=2.0.1)", "sse-starlette (>=1.6.1)", "starlette-context (>=0.3.6,<0.4)", "uvicorn (>=0.22.0)"]
test = ["fastapi (>=0.100.0)", "httpx (>=0.24.1)", "huggingface-hub (>=0.23.0)", "pydantic-settings (>=2.0.1)", "pytest (>=7.4.0)", "scipy (>=1.10)", "sse-starlette (>=1.6.1)", "starlette-context (>=0.3.6,<0.4)"]
[[package]] [[package]]
name = "markdown-it-py" name = "markdown-it-py"
version = "3.0.0" version = "3.0.0"
@ -288,6 +338,76 @@ profiling = ["gprof2dot"]
rtd = ["jupyter_sphinx", "mdit-py-plugins", "myst-parser", "pyyaml", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinx_book_theme"] rtd = ["jupyter_sphinx", "mdit-py-plugins", "myst-parser", "pyyaml", "sphinx", "sphinx-copybutton", "sphinx-design", "sphinx_book_theme"]
testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"] testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"]
[[package]]
name = "markupsafe"
version = "3.0.2"
description = "Safely add untrusted strings to HTML/XML markup."
optional = false
python-versions = ">=3.9"
files = [
{file = "MarkupSafe-3.0.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:7e94c425039cde14257288fd61dcfb01963e658efbc0ff54f5306b06054700f8"},
{file = "MarkupSafe-3.0.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9e2d922824181480953426608b81967de705c3cef4d1af983af849d7bd619158"},
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[[package]] [[package]]
name = "urllib3" name = "urllib3"
version = "2.2.3" version = "2.2.3"
@ -956,4 +1301,4 @@ zstd = ["zstandard (>=0.18.0)"]
[metadata] [metadata]
lock-version = "2.0" lock-version = "2.0"
python-versions = ">=3.10,<3.13" python-versions = ">=3.10,<3.13"
content-hash = "985797a96ac1b58e4892479d96bbd1c696b452d760fa421a28b91ea8b3dbf977" content-hash = "6c3841968936d66bf70e11c6c8e0a16fec6c2f4d88d79cd8ac5a412225e7cf56"

4
pyproject.toml
View File

@ -63,6 +63,10 @@ transformers = ">=4.0.0"
pytest = {version = ">=8.1.0", optional = true} pytest = {version = ">=8.1.0", optional = true}
requests = "^2.32.3" requests = "^2.32.3"
rich = "^13.9.4" rich = "^13.9.4"
pandas = "^2.2.3"
jinja2 = "^3.1.4"
pillow = "^11.0.0"
llama-cpp-python = "^0.3.4"
[build-system] [build-system]