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swarms
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pull/546/head
Kye Gomez 6 months ago
parent ad47858cb2
commit 99fb4b17f6
5 changed files with 225 additions and 70 deletions
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1
.gitignore vendored
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@ -17,6 +17,7 @@ chroma
Accounting Assistant_state.json
Unit Testing Agent_state.json
Devin_state.json
hire_researchers
json_logs
Medical Image Diagnostic Agent_state.json
flight agent_state.json

143
swarms/schemas/agent_input_schema.py
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@ -0,0 +1,143 @@
from typing import Any, Callable, Dict, List, Optional
from pydantic import BaseModel, Field
from pydantic.v1 import validator
class AgentSchema(BaseModel):
llm: Any = Field(..., description="The language model to use")
max_tokens: int = Field(
..., description="The maximum number of tokens", ge=1
)
context_window: int = Field(
..., description="The context window size", ge=1
)
user_name: str = Field(..., description="The user name")
agent_name: str = Field(..., description="The name of the agent")
system_prompt: str = Field(..., description="The system prompt")
template: Optional[str] = Field(default=None)
max_loops: Optional[int] = Field(default=1, ge=1)
stopping_condition: Optional[Callable[[str], bool]] = Field(
default=None
)
loop_interval: Optional[int] = Field(default=0, ge=0)
retry_attempts: Optional[int] = Field(default=3, ge=0)
retry_interval: Optional[int] = Field(default=1, ge=0)
return_history: Optional[bool] = Field(default=False)
stopping_token: Optional[str] = Field(default=None)
dynamic_loops: Optional[bool] = Field(default=False)
interactive: Optional[bool] = Field(default=False)
dashboard: Optional[bool] = Field(default=False)
agent_description: Optional[str] = Field(default=None)
tools: Optional[List[Callable]] = Field(default=None)
dynamic_temperature_enabled: Optional[bool] = Field(default=False)
sop: Optional[str] = Field(default=None)
sop_list: Optional[List[str]] = Field(default=None)
saved_state_path: Optional[str] = Field(default=None)
autosave: Optional[bool] = Field(default=False)
self_healing_enabled: Optional[bool] = Field(default=False)
code_interpreter: Optional[bool] = Field(default=False)
multi_modal: Optional[bool] = Field(default=False)
pdf_path: Optional[str] = Field(default=None)
list_of_pdf: Optional[str] = Field(default=None)
tokenizer: Optional[Any] = Field(default=None)
long_term_memory: Optional[Any] = Field(default=None)
preset_stopping_token: Optional[bool] = Field(default=False)
traceback: Optional[Any] = Field(default=None)
traceback_handlers: Optional[Any] = Field(default=None)
streaming_on: Optional[bool] = Field(default=False)
docs: Optional[List[str]] = Field(default=None)
docs_folder: Optional[str] = Field(default=None)
verbose: Optional[bool] = Field(default=False)
parser: Optional[Callable] = Field(default=None)
best_of_n: Optional[int] = Field(default=None)
callback: Optional[Callable] = Field(default=None)
metadata: Optional[Dict[str, Any]] = Field(default=None)
callbacks: Optional[List[Callable]] = Field(default=None)
logger_handler: Optional[Any] = Field(default=None)
search_algorithm: Optional[Callable] = Field(default=None)
logs_to_filename: Optional[str] = Field(default=None)
evaluator: Optional[Callable] = Field(default=None)
output_json: Optional[bool] = Field(default=False)
stopping_func: Optional[Callable] = Field(default=None)
custom_loop_condition: Optional[Callable] = Field(default=None)
sentiment_threshold: Optional[float] = Field(default=None)
custom_exit_command: Optional[str] = Field(default="exit")
sentiment_analyzer: Optional[Callable] = Field(default=None)
limit_tokens_from_string: Optional[Callable] = Field(default=None)
custom_tools_prompt: Optional[Callable] = Field(default=None)
tool_schema: Optional[Any] = Field(default=None)
output_type: Optional[Any] = Field(default=None)
function_calling_type: Optional[str] = Field(default="json")
output_cleaner: Optional[Callable] = Field(default=None)
function_calling_format_type: Optional[str] = Field(default="OpenAI")
list_base_models: Optional[List[Any]] = Field(default=None)
metadata_output_type: Optional[str] = Field(default="json")
state_save_file_type: Optional[str] = Field(default="json")
chain_of_thoughts: Optional[bool] = Field(default=False)
algorithm_of_thoughts: Optional[bool] = Field(default=False)
tree_of_thoughts: Optional[bool] = Field(default=False)
tool_choice: Optional[str] = Field(default="auto")
execute_tool: Optional[bool] = Field(default=False)
rules: Optional[str] = Field(default=None)
planning: Optional[bool] = Field(default=False)
planning_prompt: Optional[str] = Field(default=None)
device: Optional[str] = Field(default=None)
custom_planning_prompt: Optional[str] = Field(default=None)
memory_chunk_size: Optional[int] = Field(default=2000, ge=0)
agent_ops_on: Optional[bool] = Field(default=False)
log_directory: Optional[str] = Field(default=None)
project_path: Optional[str] = Field(default=None)
tool_system_prompt: Optional[str] = Field(default="tool_sop_prompt()")
top_p: Optional[float] = Field(default=0.9, ge=0, le=1)
top_k: Optional[int] = Field(default=None)
frequency_penalty: Optional[float] = Field(default=0.0, ge=0, le=1)
presence_penalty: Optional[float] = Field(default=0.0, ge=0, le=1)
temperature: Optional[float] = Field(default=0.1, ge=0, le=1)
@validator(
"tools",
"docs",
"sop_list",
"callbacks",
"list_base_models",
each_item=True,
)
def check_list_items_not_none(cls, v):
if v is None:
raise ValueError("List items must not be None")
return v
@validator(
"tokenizer",
"memory",
"traceback",
"traceback_handlers",
"parser",
"callback",
"search_algorithm",
"evaluator",
"stopping_func",
"custom_loop_condition",
"sentiment_analyzer",
"limit_tokens_from_string",
"custom_tools_prompt",
"output_cleaner",
)
def check_optional_callable_not_none(cls, v):
if v is not None and not callable(v):
raise ValueError(f"{v} must be a callable")
return v
# # Example of how to use the schema
# agent_data = {
# "llm": "OpenAIChat",
# "max_tokens": 4096,
# "context_window": 8192,
# "user_name": "Human",
# "agent_name": "test-agent",
# "system_prompt": "Custom system prompt",
# }
# agent = AgentSchema(**agent_data)
# print(agent)

63
swarms/schemas/schemas.py
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@ -16,35 +16,6 @@ class TaskInput(BaseModel):
)
class Artifact(BaseModel):
"""
Represents an artifact.
Attributes:
artifact_id (str): Id of the artifact.
file_name (str): Filename of the artifact.
relative_path (str, optional): Relative path of the artifact in the agent's workspace.
"""
artifact_id: str = Field(
...,
description="Id of the artifact",
examples=["b225e278-8b4c-4f99-a696-8facf19f0e56"],
)
file_name: str = Field(
...,
description="Filename of the artifact",
examples=["main.py"],
)
relative_path: str | None = Field(
None,
description=(
"Relative path of the artifact in the agent's workspace"
),
examples=["python/code/"],
)
class ArtifactUpload(BaseModel):
file: bytes = Field(..., description="File to upload")
relative_path: str | None = Field(
@ -86,22 +57,22 @@ class TaskRequestBody(BaseModel):
additional_input: TaskInput | None = None
class Task(TaskRequestBody):
task_id: str = Field(
...,
description="The ID of the task.",
examples=["50da533e-3904-4401-8a07-c49adf88b5eb"],
)
artifacts: list[Artifact] = Field(
[],
description="A list of artifacts that the task has produced.",
examples=[
[
"7a49f31c-f9c6-4346-a22c-e32bc5af4d8e",
"ab7b4091-2560-4692-a4fe-d831ea3ca7d6",
]
],
)
# class Task(TaskRequestBody):
# task_id: str = Field(
# ...,
# description="The ID of the task.",
# examples=["50da533e-3904-4401-8a07-c49adf88b5eb"],
# )
# artifacts: list[Artifact] = Field(
# [],
# description="A list of artifacts that the task has produced.",
# examples=[
# [
# "7a49f31c-f9c6-4346-a22c-e32bc5af4d8e",
# "ab7b4091-2560-4692-a4fe-d831ea3ca7d6",
# ]
# ],
# )
class StepRequestBody(BaseModel):
@ -144,7 +115,7 @@ class Step(BaseModel):
" <write_to_file('output.txt', 'Washington')"
],
)
artifacts: list[Artifact] = Field(
artifacts: list[Any] = Field(
[],
description="A list of artifacts that the step has produced.",
)

70
swarms/structs/agent.py
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@ -266,6 +266,7 @@ class Agent(BaseStructure):
top_k: int = None,
frequency_penalty: float = 0.0,
presence_penalty: float = 0.0,
temperature: float = 0.1,
*args,
**kwargs,
):
@ -356,6 +357,7 @@ class Agent(BaseStructure):
self.top_k = top_k
self.frequency_penalty = frequency_penalty
self.presence_penalty = presence_penalty
self.temperature
# Name
self.name = agent_name
@ -472,6 +474,7 @@ class Agent(BaseStructure):
self.short_memory.add(role=self.user_name, content=self.sop)
# If agent_ops is on => activate agentops
if agent_ops_on is True:
self.activate_agentops()
def set_system_prompt(self, system_prompt: str):
@ -483,19 +486,19 @@ class Agent(BaseStructure):
self.feedback.append(feedback)
logging.info(f"Feedback received: {feedback}")
def initialize_llm(self, llm: Any) -> None:
return llm(
system_prompt=self.system_prompt,
max_tokens=self.max_tokens,
context_length=self.context_length,
temperature=self.temperature,
top_p=self.top_p,
top_k=self.top_k,
frequency_penalty=self.frequency_penalty,
presence_penalty=self.presence_penalty,
stop=self.stopping_token,
engine=self.engine,
)
# TODO: Implement the function
# def initialize_llm(self, llm: Any) -> None:
# return llm(
# system_prompt=self.system_prompt,
# max_tokens=self.max_tokens,
# context_length=self.context_length,
# temperature=self.temperature,
# top_p=self.top_p,
# top_k=self.top_k,
# frequency_penalty=self.frequency_penalty,
# presence_penalty=self.presence_penalty,
# stop=self.stopping_token,
# )
def agent_initialization(self):
try:
@ -718,8 +721,8 @@ class Agent(BaseStructure):
response = None
all_responses = []
if self.tokenizer is not None:
self.check_available_tokens()
# if self.tokenizer is not None:
# self.check_available_tokens()
while self.max_loops == "auto" or loop_count < self.max_loops:
loop_count += 1
@ -733,6 +736,7 @@ class Agent(BaseStructure):
# Task prompt
task_prompt = self.short_memory.return_history_as_string()
# Parameters
attempt = 0
success = False
while attempt < self.retry_attempts and not success:
@ -743,6 +747,14 @@ class Agent(BaseStructure):
task, *args, **kwargs
)
)
if exists(self.tokenizer):
task_prompt = (
self.count_and_shorten_context_window(
memory_retrieval
)
)
# Merge the task prompt with the memory retrieval
task_prompt = f"{task_prompt} Documents: Available {memory_retrieval}"
@ -758,14 +770,6 @@ class Agent(BaseStructure):
all_responses.append(response)
else:
if exists(self.tokenizer):
task_prompt = (
self.count_and_shorten_context_window(
task_prompt
)
)
response_args = (
(task_prompt, *args)
if img is None
@ -1996,3 +2000,23 @@ class Agent(BaseStructure):
f"Error with the base models, check the base model types and make sure they are initialized {error}"
)
raise error
async def count_tokens_and_subtract_from_context_window(
self, response: str, *args, **kwargs
):
"""
Count the number of tokens in the response and subtract it from the context window.
Args:
response (str): The response to count the tokens from.
Returns:
str: The response after counting the tokens and subtracting it from the context window.
"""
# Count the number of tokens in the response
tokens = self.tokenizer.count_tokens(response)
# Subtract the number of tokens from the context window
self.context_length -= len(tokens)
return response

16
test.py
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@ -0,0 +1,16 @@
import requests
url = "https://linkedin-api8.p.rapidapi.com/linkedin-to-email"
querystring = {
"url": "https://www.linkedin.com/in/nicolas-nahas-3ba227170/"
}
headers = {
"x-rapidapi-key": "8c6cd073d2msh9fc7d37c26ce73bp1dea6ajsn81819935da85",
"x-rapidapi-host": "linkedin-api8.p.rapidapi.com",
}
response = requests.get(url, headers=headers, params=querystring)
print(response.json())
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