mirror of
https://github.com/OpenHands/OpenHands.git
synced 2025-12-26 05:48:36 +08:00
Extract logic from init from langchains_agent and codeact_agent (#167)
This commit is contained in:
Anas DORBANI
GitHub
parent
b1944a63ef
commit
82c215ed5d
@ -1,130 +1,4 @@
|
||||
import re
|
||||
from typing import List, Mapping
|
||||
|
||||
from termcolor import colored
|
||||
|
||||
from opendevin.agent import Agent
|
||||
from opendevin.state import State
|
||||
from opendevin.action import (
|
||||
Action,
|
||||
CmdRunAction,
|
||||
AgentEchoAction,
|
||||
AgentFinishAction,
|
||||
)
|
||||
from opendevin.observation import (
|
||||
CmdOutputObservation,
|
||||
AgentMessageObservation,
|
||||
)
|
||||
|
||||
from opendevin.llm.llm import LLM
|
||||
|
||||
SYSTEM_MESSAGE = """You are a helpful assistant. You will be provided access (as root) to a bash shell to complete user-provided tasks.
|
||||
You will be able to execute commands in the bash shell, interact with the file system, install packages, and receive the output of your commands.
|
||||
|
||||
DO NOT provide code in ```triple backticks```. Instead, you should execute bash command on behalf of the user by wrapping them with <execute> and </execute>.
|
||||
For example:
|
||||
|
||||
You can list the files in the current directory by executing the following command:
|
||||
<execute>ls</execute>
|
||||
|
||||
You can also install packages using pip:
|
||||
<execute> pip install numpy </execute>
|
||||
|
||||
You can also write a block of code to a file:
|
||||
<execute>
|
||||
echo "import math
|
||||
print(math.pi)" > math.py
|
||||
</execute>
|
||||
|
||||
When you are done, execute "exit" to close the shell and end the conversation.
|
||||
"""
|
||||
|
||||
INVALID_INPUT_MESSAGE = (
|
||||
"I don't understand your input. \n"
|
||||
"If you want to execute command, please use <execute> YOUR_COMMAND_HERE </execute>.\n"
|
||||
"If you already completed the task, please exit the shell by generating: <execute> exit </execute>."
|
||||
)
|
||||
|
||||
|
||||
def parse_response(response) -> str:
|
||||
action = response.choices[0].message.content
|
||||
if "<execute>" in action and "</execute>" not in action:
|
||||
action += "</execute>"
|
||||
return action
|
||||
|
||||
|
||||
class CodeActAgent(Agent):
|
||||
def __init__(
|
||||
self,
|
||||
llm: LLM,
|
||||
) -> None:
|
||||
"""
|
||||
Initializes a new instance of the CodeActAgent class.
|
||||
|
||||
Parameters:
|
||||
- instruction (str): The instruction for the agent to execute.
|
||||
- max_steps (int): The maximum number of steps to run the agent.
|
||||
"""
|
||||
super().__init__(llm)
|
||||
self.messages: List[Mapping[str, str]] = []
|
||||
self.instruction: str = ""
|
||||
|
||||
def step(self, state: State) -> Action:
|
||||
if len(self.messages) == 0:
|
||||
assert self.instruction, "Expecting instruction to be set"
|
||||
self.messages = [
|
||||
{"role": "system", "content": SYSTEM_MESSAGE},
|
||||
{"role": "user", "content": self.instruction},
|
||||
]
|
||||
print(colored("===USER:===\n" + self.instruction, "green"))
|
||||
updated_info = state.updated_info
|
||||
if updated_info:
|
||||
for prev_action, obs in updated_info:
|
||||
assert isinstance(prev_action, (CmdRunAction, AgentEchoAction)), "Expecting CmdRunAction or AgentEchoAction for Action"
|
||||
if isinstance(obs, AgentMessageObservation): # warning message from itself
|
||||
self.messages.append({"role": "user", "content": obs.content})
|
||||
print(colored("===USER:===\n" + obs.content, "green"))
|
||||
elif isinstance(obs, CmdOutputObservation):
|
||||
content = "OBSERVATION:\n" + obs.content
|
||||
content += f"\n[Command {obs.command_id} finished with exit code {obs.exit_code}]]"
|
||||
self.messages.append({"role": "user", "content": content})
|
||||
print(colored("===ENV OBSERVATION:===\n" + content, "blue"))
|
||||
else:
|
||||
raise NotImplementedError(f"Unknown observation type: {obs.__class__}")
|
||||
response = self.llm.completion(
|
||||
messages=self.messages,
|
||||
stop=["</execute>"],
|
||||
temperature=0.0,
|
||||
seed=42,
|
||||
)
|
||||
action_str: str = parse_response(response)
|
||||
self.messages.append({"role": "assistant", "content": action_str})
|
||||
print(colored("===ASSISTANT:===\n" + action_str, "yellow"))
|
||||
|
||||
command = re.search(r"<execute>(.*)</execute>", action_str, re.DOTALL)
|
||||
if command is not None:
|
||||
# a command was found
|
||||
command_group = command.group(1)
|
||||
if command_group.strip() == "exit":
|
||||
print(colored("Exit received. Exiting...", "red"))
|
||||
return AgentFinishAction()
|
||||
return CmdRunAction(command = command_group)
|
||||
# # execute the code
|
||||
# # TODO: does exit_code get loaded into Message?
|
||||
# exit_code, observation = self.env.execute(command_group)
|
||||
# self._history.append(Message(Role.ASSISTANT, observation))
|
||||
# print(colored("===ENV OBSERVATION:===\n" + observation, "blue"))
|
||||
else:
|
||||
# we could provide a error message for the model to continue similar to
|
||||
# https://github.com/xingyaoww/mint-bench/blob/main/mint/envs/general_env.py#L18-L23
|
||||
# observation = INVALID_INPUT_MESSAGE
|
||||
# self._history.append(Message(Role.ASSISTANT, observation))
|
||||
# print(colored("===ENV OBSERVATION:===\n" + observation, "blue"))
|
||||
return AgentEchoAction(content=INVALID_INPUT_MESSAGE) # warning message to itself
|
||||
|
||||
|
||||
def search_memory(self, query: str) -> List[str]:
|
||||
raise NotImplementedError("Implement this abstract method")
|
||||
|
||||
from .codeact_agent import CodeActAgent
|
||||
|
||||
Agent.register("CodeActAgent", CodeActAgent)
|
||||
|
||||
128
agenthub/codeact_agent/codeact_agent.py
Normal file
128
agenthub/codeact_agent/codeact_agent.py
Normal file
@ -0,0 +1,128 @@
|
||||
import re
|
||||
from typing import List, Mapping
|
||||
|
||||
from termcolor import colored
|
||||
|
||||
from opendevin.agent import Agent
|
||||
from opendevin.state import State
|
||||
from opendevin.action import (
|
||||
Action,
|
||||
CmdRunAction,
|
||||
AgentEchoAction,
|
||||
AgentFinishAction,
|
||||
)
|
||||
from opendevin.observation import (
|
||||
CmdOutputObservation,
|
||||
AgentMessageObservation,
|
||||
)
|
||||
|
||||
from opendevin.llm.llm import LLM
|
||||
|
||||
SYSTEM_MESSAGE = """You are a helpful assistant. You will be provided access (as root) to a bash shell to complete user-provided tasks.
|
||||
You will be able to execute commands in the bash shell, interact with the file system, install packages, and receive the output of your commands.
|
||||
|
||||
DO NOT provide code in ```triple backticks```. Instead, you should execute bash command on behalf of the user by wrapping them with <execute> and </execute>.
|
||||
For example:
|
||||
|
||||
You can list the files in the current directory by executing the following command:
|
||||
<execute>ls</execute>
|
||||
|
||||
You can also install packages using pip:
|
||||
<execute> pip install numpy </execute>
|
||||
|
||||
You can also write a block of code to a file:
|
||||
<execute>
|
||||
echo "import math
|
||||
print(math.pi)" > math.py
|
||||
</execute>
|
||||
|
||||
When you are done, execute "exit" to close the shell and end the conversation.
|
||||
"""
|
||||
|
||||
INVALID_INPUT_MESSAGE = (
|
||||
"I don't understand your input. \n"
|
||||
"If you want to execute command, please use <execute> YOUR_COMMAND_HERE </execute>.\n"
|
||||
"If you already completed the task, please exit the shell by generating: <execute> exit </execute>."
|
||||
)
|
||||
|
||||
|
||||
def parse_response(response) -> str:
|
||||
action = response.choices[0].message.content
|
||||
if "<execute>" in action and "</execute>" not in action:
|
||||
action += "</execute>"
|
||||
return action
|
||||
|
||||
|
||||
class CodeActAgent(Agent):
|
||||
def __init__(
|
||||
self,
|
||||
llm: LLM,
|
||||
) -> None:
|
||||
"""
|
||||
Initializes a new instance of the CodeActAgent class.
|
||||
|
||||
Parameters:
|
||||
- instruction (str): The instruction for the agent to execute.
|
||||
- max_steps (int): The maximum number of steps to run the agent.
|
||||
"""
|
||||
super().__init__(llm)
|
||||
self.messages: List[Mapping[str, str]] = []
|
||||
self.instruction: str = ""
|
||||
|
||||
def step(self, state: State) -> Action:
|
||||
if len(self.messages) == 0:
|
||||
assert self.instruction, "Expecting instruction to be set"
|
||||
self.messages = [
|
||||
{"role": "system", "content": SYSTEM_MESSAGE},
|
||||
{"role": "user", "content": self.instruction},
|
||||
]
|
||||
print(colored("===USER:===\n" + self.instruction, "green"))
|
||||
updated_info = state.updated_info
|
||||
if updated_info:
|
||||
for prev_action, obs in updated_info:
|
||||
assert isinstance(prev_action, (CmdRunAction, AgentEchoAction)), "Expecting CmdRunAction or AgentEchoAction for Action"
|
||||
if isinstance(obs, AgentMessageObservation): # warning message from itself
|
||||
self.messages.append({"role": "user", "content": obs.content})
|
||||
print(colored("===USER:===\n" + obs.content, "green"))
|
||||
elif isinstance(obs, CmdOutputObservation):
|
||||
content = "OBSERVATION:\n" + obs.content
|
||||
content += f"\n[Command {obs.command_id} finished with exit code {obs.exit_code}]]"
|
||||
self.messages.append({"role": "user", "content": content})
|
||||
print(colored("===ENV OBSERVATION:===\n" + content, "blue"))
|
||||
else:
|
||||
raise NotImplementedError(f"Unknown observation type: {obs.__class__}")
|
||||
response = self.llm.completion(
|
||||
messages=self.messages,
|
||||
stop=["</execute>"],
|
||||
temperature=0.0,
|
||||
seed=42,
|
||||
)
|
||||
action_str: str = parse_response(response)
|
||||
self.messages.append({"role": "assistant", "content": action_str})
|
||||
print(colored("===ASSISTANT:===\n" + action_str, "yellow"))
|
||||
|
||||
command = re.search(r"<execute>(.*)</execute>", action_str, re.DOTALL)
|
||||
if command is not None:
|
||||
# a command was found
|
||||
command_group = command.group(1)
|
||||
if command_group.strip() == "exit":
|
||||
print(colored("Exit received. Exiting...", "red"))
|
||||
return AgentFinishAction()
|
||||
return CmdRunAction(command = command_group)
|
||||
# # execute the code
|
||||
# # TODO: does exit_code get loaded into Message?
|
||||
# exit_code, observation = self.env.execute(command_group)
|
||||
# self._history.append(Message(Role.ASSISTANT, observation))
|
||||
# print(colored("===ENV OBSERVATION:===\n" + observation, "blue"))
|
||||
else:
|
||||
# we could provide a error message for the model to continue similar to
|
||||
# https://github.com/xingyaoww/mint-bench/blob/main/mint/envs/general_env.py#L18-L23
|
||||
# observation = INVALID_INPUT_MESSAGE
|
||||
# self._history.append(Message(Role.ASSISTANT, observation))
|
||||
# print(colored("===ENV OBSERVATION:===\n" + observation, "blue"))
|
||||
return AgentEchoAction(content=INVALID_INPUT_MESSAGE) # warning message to itself
|
||||
|
||||
|
||||
def search_memory(self, query: str) -> List[str]:
|
||||
raise NotImplementedError("Implement this abstract method")
|
||||
|
||||
@ -1,182 +1,4 @@
|
||||
from typing import List
|
||||
|
||||
from opendevin.llm.llm import LLM
|
||||
from opendevin.agent import Agent
|
||||
from opendevin.state import State
|
||||
from opendevin.action import Action
|
||||
import agenthub.langchains_agent.utils.prompts as prompts
|
||||
from agenthub.langchains_agent.utils.monologue import Monologue
|
||||
from agenthub.langchains_agent.utils.memory import LongTermMemory
|
||||
|
||||
from opendevin.action import (
|
||||
NullAction,
|
||||
CmdRunAction,
|
||||
CmdKillAction,
|
||||
BrowseURLAction,
|
||||
FileReadAction,
|
||||
FileWriteAction,
|
||||
AgentRecallAction,
|
||||
AgentThinkAction,
|
||||
AgentFinishAction,
|
||||
)
|
||||
from opendevin.observation import (
|
||||
CmdOutputObservation,
|
||||
)
|
||||
|
||||
|
||||
MAX_MONOLOGUE_LENGTH = 20000
|
||||
MAX_OUTPUT_LENGTH = 5000
|
||||
|
||||
INITIAL_THOUGHTS = [
|
||||
"I exist!",
|
||||
"Hmm...looks like I can type in a command line prompt",
|
||||
"Looks like I have a web browser too!",
|
||||
"Here's what I want to do: $TASK",
|
||||
"How am I going to get there though?",
|
||||
"It seems like I have some kind of short term memory.",
|
||||
"Each of my thoughts seems to be stored in a numbered list.",
|
||||
"It seems whatever I say next will be added to the list.",
|
||||
"But no one has perfect short-term memory. My list of thoughts will be summarized and condensed over time, losing information in the process.",
|
||||
"Fortunately I have long term memory!",
|
||||
"I can just say RECALL, followed by the thing I want to remember. And then related thoughts just spill out!",
|
||||
"Sometimes they're random thoughts that don't really have to do with what I wanted to remember. But usually they're exactly what I need!",
|
||||
"Let's try it out!",
|
||||
"RECALL what it is I want to do",
|
||||
"Here's what I want to do: $TASK",
|
||||
"How am I going to get there though?",
|
||||
"Neat! And it looks like it's easy for me to use the command line too! I just have to say RUN followed by the command I want to run. The command output just jumps into my head!",
|
||||
'RUN echo "hello world"',
|
||||
"hello world",
|
||||
"Cool! I bet I can read and edit files too.",
|
||||
"RUN echo \"console.log('hello world')\" > test.js",
|
||||
"",
|
||||
"I just created test.js. I'll try and run it now.",
|
||||
"RUN node test.js",
|
||||
"hello world",
|
||||
"it works!",
|
||||
"And if I want to use the browser, I just need to say BROWSE, followed by a website I want to visit, or an action I want to take on the current site",
|
||||
"Let's try that...",
|
||||
"BROWSE google.com",
|
||||
'<form><input type="text"></input><button type="submit"></button></form>',
|
||||
"Very cool. Now to accomplish my task.",
|
||||
"I'll need a strategy. And as I make progress, I'll need to keep refining that strategy. I'll need to set goals, and break them into sub-goals.",
|
||||
"In between actions, I must always take some time to think, strategize, and set new goals. I should never take two actions in a row.",
|
||||
"OK so my task is to $TASK. I haven't made any progress yet. Where should I start?",
|
||||
"It seems like there might be an existing project here. I should probably start by running `ls` to see what's here.",
|
||||
]
|
||||
|
||||
|
||||
class LangchainsAgent(Agent):
|
||||
_initialized = False
|
||||
|
||||
def __init__(self, llm: LLM):
|
||||
super().__init__(llm)
|
||||
self.monologue = Monologue()
|
||||
self.memory = LongTermMemory()
|
||||
|
||||
def _add_event(self, event: dict):
|
||||
if 'output' in event['args'] and len(event['args']['output']) > MAX_OUTPUT_LENGTH:
|
||||
event['args']['output'] = event['args']['output'][:MAX_OUTPUT_LENGTH] + "..."
|
||||
|
||||
self.monologue.add_event(event)
|
||||
self.memory.add_event(event)
|
||||
if self.monologue.get_total_length() > MAX_MONOLOGUE_LENGTH:
|
||||
self.monologue.condense(self.llm)
|
||||
|
||||
def _initialize(self):
|
||||
if self._initialized:
|
||||
return
|
||||
|
||||
if self.instruction is None or self.instruction == "":
|
||||
raise ValueError("Instruction must be provided")
|
||||
self.monologue = Monologue()
|
||||
self.memory = LongTermMemory()
|
||||
|
||||
next_is_output = False
|
||||
for thought in INITIAL_THOUGHTS:
|
||||
thought = thought.replace("$TASK", self.instruction)
|
||||
if next_is_output:
|
||||
d = {"action": "output", "args": {"output": thought}}
|
||||
next_is_output = False
|
||||
else:
|
||||
if thought.startswith("RUN"):
|
||||
command = thought.split("RUN ")[1]
|
||||
d = {"action": "run", "args": {"command": command}}
|
||||
next_is_output = True
|
||||
|
||||
elif thought.startswith("RECALL"):
|
||||
query = thought.split("RECALL ")[1]
|
||||
d = {"action": "recall", "args": {"query": query}}
|
||||
next_is_output = True
|
||||
|
||||
elif thought.startswith("BROWSE"):
|
||||
url = thought.split("BROWSE ")[1]
|
||||
d = {"action": "browse", "args": {"url": url}}
|
||||
next_is_output = True
|
||||
else:
|
||||
d = {"action": "think", "args": {"thought": thought}}
|
||||
|
||||
self._add_event(d)
|
||||
self._initialized = True
|
||||
|
||||
def step(self, state: State) -> Action:
|
||||
self._initialize()
|
||||
# TODO: make langchains agent use Action & Observation
|
||||
# completly from ground up
|
||||
|
||||
# Translate state to action_dict
|
||||
for prev_action, obs in state.updated_info:
|
||||
d = None
|
||||
if isinstance(obs, CmdOutputObservation):
|
||||
if obs.error:
|
||||
d = {"action": "error", "args": {"output": obs.content}}
|
||||
else:
|
||||
d = {"action": "output", "args": {"output": obs.content}}
|
||||
else:
|
||||
d = {"action": "output", "args": {"output": obs.content}}
|
||||
if d is not None:
|
||||
self._add_event(d)
|
||||
|
||||
d = None
|
||||
if isinstance(prev_action, CmdRunAction):
|
||||
d = {"action": "run", "args": {"command": prev_action.command}}
|
||||
elif isinstance(prev_action, CmdKillAction):
|
||||
d = {"action": "kill", "args": {"id": prev_action.id}}
|
||||
elif isinstance(prev_action, BrowseURLAction):
|
||||
d = {"action": "browse", "args": {"url": prev_action.url}}
|
||||
elif isinstance(prev_action, FileReadAction):
|
||||
d = {"action": "read", "args": {"file": prev_action.path}}
|
||||
elif isinstance(prev_action, FileWriteAction):
|
||||
d = {"action": "write", "args": {"file": prev_action.path, "content": prev_action.contents}}
|
||||
elif isinstance(prev_action, AgentRecallAction):
|
||||
d = {"action": "recall", "args": {"query": prev_action.query}}
|
||||
elif isinstance(prev_action, AgentThinkAction):
|
||||
d = {"action": "think", "args": {"thought": prev_action.thought}}
|
||||
elif isinstance(prev_action, AgentFinishAction):
|
||||
d = {"action": "finish"}
|
||||
elif isinstance(prev_action, NullAction):
|
||||
d = None
|
||||
else:
|
||||
raise ValueError(f"Unknown action type: {prev_action}")
|
||||
if d is not None:
|
||||
self._add_event(d)
|
||||
|
||||
state.updated_info = []
|
||||
|
||||
prompt = prompts.get_request_action_prompt(
|
||||
self.instruction,
|
||||
self.monologue.get_thoughts(),
|
||||
state.background_commands_obs,
|
||||
)
|
||||
messages = [{"content": prompt,"role": "user"}]
|
||||
resp = self.llm.completion(messages=messages)
|
||||
action_resp = resp['choices'][0]['message']['content']
|
||||
action = prompts.parse_action_response(action_resp)
|
||||
self.latest_action = action
|
||||
return action
|
||||
|
||||
def search_memory(self, query: str) -> List[str]:
|
||||
return self.memory.search(query)
|
||||
|
||||
from .langchains_agent import LangchainsAgent
|
||||
|
||||
Agent.register("LangchainsAgent", LangchainsAgent)
|
||||
|
||||
180
agenthub/langchains_agent/langchains_agent.py
Normal file
180
agenthub/langchains_agent/langchains_agent.py
Normal file
@ -0,0 +1,180 @@
|
||||
from typing import List
|
||||
|
||||
from opendevin.llm.llm import LLM
|
||||
from opendevin.agent import Agent
|
||||
from opendevin.state import State
|
||||
from opendevin.action import Action
|
||||
import agenthub.langchains_agent.utils.prompts as prompts
|
||||
from agenthub.langchains_agent.utils.monologue import Monologue
|
||||
from agenthub.langchains_agent.utils.memory import LongTermMemory
|
||||
|
||||
from opendevin.action import (
|
||||
NullAction,
|
||||
CmdRunAction,
|
||||
CmdKillAction,
|
||||
BrowseURLAction,
|
||||
FileReadAction,
|
||||
FileWriteAction,
|
||||
AgentRecallAction,
|
||||
AgentThinkAction,
|
||||
AgentFinishAction,
|
||||
)
|
||||
from opendevin.observation import (
|
||||
CmdOutputObservation,
|
||||
)
|
||||
|
||||
|
||||
MAX_MONOLOGUE_LENGTH = 20000
|
||||
MAX_OUTPUT_LENGTH = 5000
|
||||
|
||||
INITIAL_THOUGHTS = [
|
||||
"I exist!",
|
||||
"Hmm...looks like I can type in a command line prompt",
|
||||
"Looks like I have a web browser too!",
|
||||
"Here's what I want to do: $TASK",
|
||||
"How am I going to get there though?",
|
||||
"It seems like I have some kind of short term memory.",
|
||||
"Each of my thoughts seems to be stored in a numbered list.",
|
||||
"It seems whatever I say next will be added to the list.",
|
||||
"But no one has perfect short-term memory. My list of thoughts will be summarized and condensed over time, losing information in the process.",
|
||||
"Fortunately I have long term memory!",
|
||||
"I can just say RECALL, followed by the thing I want to remember. And then related thoughts just spill out!",
|
||||
"Sometimes they're random thoughts that don't really have to do with what I wanted to remember. But usually they're exactly what I need!",
|
||||
"Let's try it out!",
|
||||
"RECALL what it is I want to do",
|
||||
"Here's what I want to do: $TASK",
|
||||
"How am I going to get there though?",
|
||||
"Neat! And it looks like it's easy for me to use the command line too! I just have to say RUN followed by the command I want to run. The command output just jumps into my head!",
|
||||
'RUN echo "hello world"',
|
||||
"hello world",
|
||||
"Cool! I bet I can read and edit files too.",
|
||||
"RUN echo \"console.log('hello world')\" > test.js",
|
||||
"",
|
||||
"I just created test.js. I'll try and run it now.",
|
||||
"RUN node test.js",
|
||||
"hello world",
|
||||
"it works!",
|
||||
"And if I want to use the browser, I just need to say BROWSE, followed by a website I want to visit, or an action I want to take on the current site",
|
||||
"Let's try that...",
|
||||
"BROWSE google.com",
|
||||
'<form><input type="text"></input><button type="submit"></button></form>',
|
||||
"Very cool. Now to accomplish my task.",
|
||||
"I'll need a strategy. And as I make progress, I'll need to keep refining that strategy. I'll need to set goals, and break them into sub-goals.",
|
||||
"In between actions, I must always take some time to think, strategize, and set new goals. I should never take two actions in a row.",
|
||||
"OK so my task is to $TASK. I haven't made any progress yet. Where should I start?",
|
||||
"It seems like there might be an existing project here. I should probably start by running `ls` to see what's here.",
|
||||
]
|
||||
|
||||
|
||||
class LangchainsAgent(Agent):
|
||||
_initialized = False
|
||||
|
||||
def __init__(self, llm: LLM):
|
||||
super().__init__(llm)
|
||||
self.monologue = Monologue()
|
||||
self.memory = LongTermMemory()
|
||||
|
||||
def _add_event(self, event: dict):
|
||||
if 'output' in event['args'] and len(event['args']['output']) > MAX_OUTPUT_LENGTH:
|
||||
event['args']['output'] = event['args']['output'][:MAX_OUTPUT_LENGTH] + "..."
|
||||
|
||||
self.monologue.add_event(event)
|
||||
self.memory.add_event(event)
|
||||
if self.monologue.get_total_length() > MAX_MONOLOGUE_LENGTH:
|
||||
self.monologue.condense(self.llm)
|
||||
|
||||
def _initialize(self):
|
||||
if self._initialized:
|
||||
return
|
||||
|
||||
if self.instruction is None or self.instruction == "":
|
||||
raise ValueError("Instruction must be provided")
|
||||
self.monologue = Monologue()
|
||||
self.memory = LongTermMemory()
|
||||
|
||||
next_is_output = False
|
||||
for thought in INITIAL_THOUGHTS:
|
||||
thought = thought.replace("$TASK", self.instruction)
|
||||
if next_is_output:
|
||||
d = {"action": "output", "args": {"output": thought}}
|
||||
next_is_output = False
|
||||
else:
|
||||
if thought.startswith("RUN"):
|
||||
command = thought.split("RUN ")[1]
|
||||
d = {"action": "run", "args": {"command": command}}
|
||||
next_is_output = True
|
||||
|
||||
elif thought.startswith("RECALL"):
|
||||
query = thought.split("RECALL ")[1]
|
||||
d = {"action": "recall", "args": {"query": query}}
|
||||
next_is_output = True
|
||||
|
||||
elif thought.startswith("BROWSE"):
|
||||
url = thought.split("BROWSE ")[1]
|
||||
d = {"action": "browse", "args": {"url": url}}
|
||||
next_is_output = True
|
||||
else:
|
||||
d = {"action": "think", "args": {"thought": thought}}
|
||||
|
||||
self._add_event(d)
|
||||
self._initialized = True
|
||||
|
||||
def step(self, state: State) -> Action:
|
||||
self._initialize()
|
||||
# TODO: make langchains agent use Action & Observation
|
||||
# completly from ground up
|
||||
|
||||
# Translate state to action_dict
|
||||
for prev_action, obs in state.updated_info:
|
||||
d = None
|
||||
if isinstance(obs, CmdOutputObservation):
|
||||
if obs.error:
|
||||
d = {"action": "error", "args": {"output": obs.content}}
|
||||
else:
|
||||
d = {"action": "output", "args": {"output": obs.content}}
|
||||
else:
|
||||
d = {"action": "output", "args": {"output": obs.content}}
|
||||
if d is not None:
|
||||
self._add_event(d)
|
||||
|
||||
d = None
|
||||
if isinstance(prev_action, CmdRunAction):
|
||||
d = {"action": "run", "args": {"command": prev_action.command}}
|
||||
elif isinstance(prev_action, CmdKillAction):
|
||||
d = {"action": "kill", "args": {"id": prev_action.id}}
|
||||
elif isinstance(prev_action, BrowseURLAction):
|
||||
d = {"action": "browse", "args": {"url": prev_action.url}}
|
||||
elif isinstance(prev_action, FileReadAction):
|
||||
d = {"action": "read", "args": {"file": prev_action.path}}
|
||||
elif isinstance(prev_action, FileWriteAction):
|
||||
d = {"action": "write", "args": {"file": prev_action.path, "content": prev_action.contents}}
|
||||
elif isinstance(prev_action, AgentRecallAction):
|
||||
d = {"action": "recall", "args": {"query": prev_action.query}}
|
||||
elif isinstance(prev_action, AgentThinkAction):
|
||||
d = {"action": "think", "args": {"thought": prev_action.thought}}
|
||||
elif isinstance(prev_action, AgentFinishAction):
|
||||
d = {"action": "finish"}
|
||||
elif isinstance(prev_action, NullAction):
|
||||
d = None
|
||||
else:
|
||||
raise ValueError(f"Unknown action type: {prev_action}")
|
||||
if d is not None:
|
||||
self._add_event(d)
|
||||
|
||||
state.updated_info = []
|
||||
|
||||
prompt = prompts.get_request_action_prompt(
|
||||
self.instruction,
|
||||
self.monologue.get_thoughts(),
|
||||
state.background_commands_obs,
|
||||
)
|
||||
messages = [{"content": prompt,"role": "user"}]
|
||||
resp = self.llm.completion(messages=messages)
|
||||
action_resp = resp['choices'][0]['message']['content']
|
||||
action = prompts.parse_action_response(action_resp)
|
||||
self.latest_action = action
|
||||
return action
|
||||
|
||||
def search_memory(self, query: str) -> List[str]:
|
||||
return self.memory.search(query)
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user