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Update agent.py

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nullonesix 1 year ago committed by GitHub
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swarms/structs/agent.py
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@ -5,24 +5,30 @@ import logging
import random import random
import re import re
import time import time
import uuid
from typing import Any, Callable, Dict, List, Optional, Tuple from typing import Any, Callable, Dict, List, Optional, Tuple
from termcolor import colored from termcolor import colored
from swarms.prompts.agent_system_prompts import FLOW_SYSTEM_PROMPT from swarms.memory.qdrant import Qdrant
# from langchain.document_loaders import CSVLoader
from langchain.text_splitter import CharacterTextSplitter
# from langchain.schema.document import Document
from langchain.docstore.document import Document
from swarms.tools.tool import BaseTool
from swarms.utils.code_interpreter import SubprocessCodeInterpreter
from swarms.utils.parse_code import extract_code_in_backticks_in_string
from swarms.prompts.multi_modal_autonomous_instruction_prompt import ( from swarms.prompts.multi_modal_autonomous_instruction_prompt import (
MULTI_MODAL_AUTO_AGENT_SYSTEM_PROMPT_1, MULTI_MODAL_AUTO_AGENT_SYSTEM_PROMPT_1,
) )
from swarms.utils.pdf_to_text import pdf_to_text
from swarms.prompts.tools import ( from swarms.prompts.tools import (
DYNAMIC_STOP_PROMPT, DYNAMIC_STOP_PROMPT,
DYNAMICAL_TOOL_USAGE, DYNAMICAL_TOOL_USAGE,
SCENARIOS, SCENARIOS,
) )
from swarms.tools.tool import BaseTool from swarms.prompts.agent_system_prompts import FLOW_SYSTEM_PROMPT
from swarms.utils.code_interpreter import SubprocessCodeInterpreter
from swarms.utils.parse_code import extract_code_in_backticks_in_string
from swarms.utils.pdf_to_text import pdf_to_text
def autonomous_agent_prompt( def autonomous_agent_prompt(
@ -70,11 +76,6 @@ def parse_done_token(response: str) -> bool:
return "<DONE>" in response return "<DONE>" in response
def agent_id():
"""Generate an agent id"""
return str(uuid.uuid4())
class Agent: class Agent:
""" """
Agent is the structure that provides autonomy to any llm in a reliable and effective fashion. Agent is the structure that provides autonomy to any llm in a reliable and effective fashion.
@ -100,47 +101,52 @@ class Agent:
**kwargs (Any): Any additional keyword arguments **kwargs (Any): Any additional keyword arguments
Methods: Methods:
run(task: str, **kwargs: Any): Run the agent on a task run: Run the autonomous agent loop
run_concurrent(tasks: List[str], **kwargs: Any): Run the agent on a list of tasks concurrently run_concurrent: Run the autonomous agent loop concurrently
bulk_run(inputs: List[Dict[str, Any]]): Run the agent on a list of inputs bulk_run: Run the autonomous agent loop in bulk
from_llm_and_template(llm: Any, template: str): Create AgentStream from LLM and a string template. save: Save the agent history to a file
from_llm_and_template_file(llm: Any, template_file: str): Create AgentStream from LLM and a template file. load: Load the agent history from a file
save(file_path): Save the agent history to a file validate_response: Validate the response based on certain criteria
load(file_path): Load the agent history from a file print_history_and_memory: Print the history and memory of the agent
validate_response(response: str): Validate the response based on certain criteria step: Execute a single step in the agent interaction
print_history_and_memory(): Print the entire history and memory of the agent graceful_shutdown: Gracefully shutdown the system saving the state
step(task: str, **kwargs): Executes a single step in the agent interaction, generating a response from the language model based on the given input text. run_with_timeout: Run the loop but stop if it takes longer than the timeout
graceful_shutdown(): Gracefully shutdown the system saving the state analyze_feedback: Analyze the feedback for issues
run_with_timeout(task: str, timeout: int): Run the loop but stop if it takes longer than the timeout undo_last: Response the last response and return the previous state
analyze_feedback(): Analyze the feedback for issues add_response_filter: Add a response filter to filter out certain words from the response
undo_last(): Response the last response and return the previous state apply_reponse_filters: Apply the response filters to the response
add_response_filter(filter_word: str): Add a response filter to filter out certain words from the response filtered_run: Filter the response
apply_reponse_filters(response: str): Apply the response filters to the response interactive_run: Interactive run mode
filtered_run(task: str): Filtered run streamed_generation: Stream the generation of the response
interactive_run(max_loops: int): Interactive run mode get_llm_params: Extracts and returns the parameters of the llm object for serialization.
streamed_generation(prompt: str): Stream the generation of the response agent_history_prompt: Generate the agent history prompt
get_llm_params(): Extracts and returns the parameters of the llm object for serialization. add_task_to_memory: Add the task to the memory
save_state(file_path: str): Saves the current state of the agent to a JSON file, including the llm parameters. add_message_to_memory: Add the message to the memory
load_state(file_path: str): Loads the state of the agent from a json file and restores the configuration and memory. add_message_to_memory_and_truncate: Add the message to the memory and truncate
retry_on_failure(function, retries: int = 3, retry_delay: int = 1): Retry wrapper for LLM calls. print_dashboard: Print dashboard
run_code(response: str): Run the code in the response activate_autonomous_agent: Print the autonomous agent activation message
construct_dynamic_prompt(): Construct the dynamic prompt _check_stopping_condition: Check if the stopping condition is met
extract_tool_commands(text: str): Extract the tool commands from the text format_prompt: Format the prompt
parse_and_execute_tools(response: str): Parse and execute the tools get_llm_init_params: Get the llm init params
execute_tools(tool_name, params): Execute the tool with the provided params provide_feedback: Allow users to provide feedback on the responses
truncate_history(): Take the history and truncate it to fit into the model context length truncate_history: Take the history and truncate it to fit into the model context length
add_task_to_memory(task: str): Add the task to the memory agent_history_prompt: Generate the agent history prompt
add_message_to_memory(message: str): Add the message to the memory extract_tool_commands: Extract the tool commands from the text
add_message_to_memory_and_truncate(message: str): Add the message to the memory and truncate parse_and_execute_tools: Parse and execute the tools
print_dashboard(task: str): Print dashboard execute_tools: Execute the tool with the provided parameters
activate_autonomous_agent(): Print the autonomous agent activation message construct_dynamic_prompt: Construct the dynamic prompt
dynamic_temperature(): Dynamically change the temperature get_tool_description: Get the tool description
_check_stopping_condition(response: str): Check if the stopping condition is met find_tool_by_name: Find a tool by name
format_prompt(template, **kwargs: Any): Format the template with the provided kwargs using f-string interpolation. parse_tool_command: Parse the text for tool usage
get_llm_init_params(): Get LLM init params dynamic_temperature: Dynamically change the temperature
get_tool_description(): Get the tool description _run: Generate a result using the provided keyword args.
find_tool_by_name(name: str): Find a tool by name from_llm_and_template: Create AgentStream from LLM and a string template.
from_llm_and_template_file: Create AgentStream from LLM and a template file.
save_state: Save the state of the agent
load_state: Load the state of the agent
run_async: Run the agent asynchronously
arun: Run the agent asynchronously
run_code: Run the code in the response
Example: Example:
>>> from swarms.models import OpenAIChat >>> from swarms.models import OpenAIChat
@ -160,8 +166,7 @@ class Agent:
def __init__( def __init__(
self, self,
id: str = agent_id, llm: Any,
llm: Any = None,
template: Optional[str] = None, template: Optional[str] = None,
max_loops=5, max_loops=5,
stopping_condition: Optional[Callable[[str], bool]] = None, stopping_condition: Optional[Callable[[str], bool]] = None,
@ -193,7 +198,6 @@ class Agent:
*args, *args,
**kwargs: Any, **kwargs: Any,
): ):
self.id = id
self.llm = llm self.llm = llm
self.template = template self.template = template
self.max_loops = max_loops self.max_loops = max_loops
@ -226,6 +230,8 @@ class Agent:
self.list_of_pdf = list_of_pdf self.list_of_pdf = list_of_pdf
self.tokenizer = tokenizer self.tokenizer = tokenizer
self.vector_database = Qdrant(api_key="BhG2_yINqNU-aKovSEBadn69Zszhbo5uaqdJ6G_qDkdySjAljvuPqQ", host="https://697ea26c-2881-4e17-8af4-817fcb5862e8.europe-west3-0.gcp.cloud.qdrant.io", collection_name="snake")
# The max_loops will be set dynamically if the dynamic_loop # The max_loops will be set dynamically if the dynamic_loop
if self.dynamic_loops: if self.dynamic_loops:
self.max_loops = "auto" self.max_loops = "auto"
@ -406,7 +412,6 @@ class Agent:
---------------------------------------- ----------------------------------------
Agent Configuration: Agent Configuration:
Agent ID: {self.id}
Name: {self.agent_name} Name: {self.agent_name}
Description: {self.agent_description} Description: {self.agent_description}
Standard Operating Procedure: {self.sop} Standard Operating Procedure: {self.sop}
@ -455,45 +460,6 @@ class Agent:
) )
) )
print(error) print(error)
def loop_count_print(self, loop_count, max_loops):
"""loop_count_print summary
Args:
loop_count (_type_): _description_
max_loops (_type_): _description_
"""
print(
colored(f"\nLoop {loop_count} of {max_loops}", "cyan")
)
print("\n")
def _history(self, user_name: str, task: str) -> str:
"""Generate the history for the history prompt
Args:
user_name (str): _description_
task (str): _description_
Returns:
str: _description_
"""
history = [f"{user_name}: {task}"]
return history
def _dynamic_prompt_setup(self, dynamic_prompt: str, task: str) -> str:
"""_dynamic_prompt_setup summary
Args:
dynamic_prompt (str): _description_
task (str): _description_
Returns:
str: _description_
"""
dynamic_prompt = dynamic_prompt or self.construct_dynamic_prompt()
combined_prompt = f"{dynamic_prompt}\n{task}"
return combined_prompt
def run(self, task: Optional[str], img: Optional[str] = None, **kwargs): def run(self, task: Optional[str], img: Optional[str] = None, **kwargs):
""" """
@ -511,11 +477,14 @@ class Agent:
""" """
try: try:
# dynamic_prompt = self.construct_dynamic_prompt()
# combined_prompt = f"{dynamic_prompt}\n{task}"
# Activate Autonomous agent message # Activate Autonomous agent message
self.activate_autonomous_agent() self.activate_autonomous_agent()
response = task # or combined_prompt response = task # or combined_prompt
history = self._history(self.user_name, task) history = [f"{self.user_name}: {task}"]
# If dashboard = True then print the dashboard # If dashboard = True then print the dashboard
if self.dashboard: if self.dashboard:
@ -527,7 +496,9 @@ class Agent:
while self.max_loops == "auto" or loop_count < self.max_loops: while self.max_loops == "auto" or loop_count < self.max_loops:
# Loop count # Loop count
loop_count += 1 loop_count += 1
self.loop_count_print(loop_count, self.max_loops) print(
colored(f"\nLoop {loop_count} of {self.max_loops}", "blue")
)
print("\n") print("\n")
# Check to see if stopping token is in the output to stop the loop # Check to see if stopping token is in the output to stop the loop
@ -544,6 +515,28 @@ class Agent:
# Preparing the prompt # Preparing the prompt
task = self.agent_history_prompt(FLOW_SYSTEM_PROMPT, response) task = self.agent_history_prompt(FLOW_SYSTEM_PROMPT, response)
# Search vector database
search_results = self.vector_database.search_vectors(query=response)
# Add search result to context
accumulated_search_result_texts = ''
for search_result in search_results:
# if search_result.score > 0.66:
if search_result.score > 0.0:
filtered_search_result_text = search_result.payload['content'] + '\n\n'
accumulated_search_result_texts += filtered_search_result_text
# print(search_result)
print()
print('***TASK***')
print(task)
print()
print('***TASK + VECTOR***')
# task = accumulated_search_result_texts + task
task = task + accumulated_search_result_texts
print(task)
print('***END TASK + VECTOR***')
print()
attempt = 0 attempt = 0
while attempt < self.retry_attempts: while attempt < self.retry_attempts:
try: try:
@ -587,6 +580,24 @@ class Agent:
# Add the response to the history # Add the response to the history
history.append(response) history.append(response)
# Add the response to vector database
def get_text_chunks_langchain(text):
text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=100)
docs = [Document(page_content=x) for x in text_splitter.split_text(text)]
return docs
text = response
docs = get_text_chunks_langchain(text)
# page_content = response
# doc = type('obj', (object,), {'page_content' : 'content'})()
# doc.page_content = page_content
# docs = [doc]
print('***DOCS***')
print(docs)
print('***END DOCS***')
self.vector_database.add_vectors(docs)
time.sleep(self.loop_interval) time.sleep(self.loop_interval)
# Add the history to the memory # Add the history to the memory
self.memory.append(history) self.memory.append(history)
@ -1167,14 +1178,14 @@ class Agent:
‘‘‘ ‘‘‘
""" """
# def self_healing(self, **kwargs): def self_healing(self, **kwargs):
# """ """
# Self healing by debugging errors and refactoring its own code Self healing by debugging errors and refactoring its own code
# Args: Args:
# **kwargs (Any): Any additional keyword arguments **kwargs (Any): Any additional keyword arguments
# """ """
# pass pass
# def refactor_code( # def refactor_code(
# self, # self,
@ -1199,29 +1210,29 @@ class Agent:
# # Sort the changes in reverse line order # # Sort the changes in reverse line order
# # explanations.sort(key=lambda x: x["line", reverse=True]) # # explanations.sort(key=lambda x: x["line", reverse=True])
# def error_prompt_inject( # # def error_prompt_inject(
# self, # # self,
# file_path: str, # # file_path: str,
# args: List, # # args: List,
# error: str, # # error: str,
# ): # # ):
# with open(file_path, "r") as f: # # with open(file_path, "r") as f:
# file_lines = f.readlines() # # file_lines = f.readlines()
# file_with_lines = [] # # file_with_lines = []
# for i, line in enumerate(file_lines): # # for i, line in enumerate(file_lines):
# file_with_lines.append(str(i + 1) + "" + line) # # file_with_lines.append(str(i + 1) + "" + line)
# file_with_lines = "".join(file_with_lines) # # file_with_lines = "".join(file_with_lines)
# prompt = f""" # # prompt = f"""
# Here is the script that needs fixing:\n\n # # Here is the script that needs fixing:\n\n
# {file_with_lines}\n\n # # {file_with_lines}\n\n
# Here are the arguments it was provided:\n\n # # Here are the arguments it was provided:\n\n
# {args}\n\n # # {args}\n\n
# Here is the error message:\n\n # # Here is the error message:\n\n
# {error}\n # # {error}\n
# "Please provide your suggested changes, and remember to stick to the " # # "Please provide your suggested changes, and remember to stick to the "
# exact format as described above. # # exact format as described above.
# """ # # """
# print(prompt) # # # Print(prompt)

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