From b4f923f1fbdf1ccab8be6da5486d3cc6aa5369c3 Mon Sep 17 00:00:00 2001 From: Kye Gomez Date: Fri, 24 May 2024 20:23:35 -0400 Subject: [PATCH] [CLEANUP] [New Tools System ] --- .gitignore | 1 + docs/requirements.txt | 22 -- new_agent_tool_system.py | 77 +++++ .../agentic_space_traffic_control/game.py | 2 +- .../agentic_space_traffic_control/tools.py | 39 --- .../demos/plant_biologist_swarm/prompts.py | 100 ++++++ .../swarm_workers_agents.py | 117 +++++++ .../demos/plant_biologist_swarm/tomato.jpg | Bin 0 -> 1977431 bytes .../structs/agent_with_tools_example.py | 4 - playground/structs/kyle_hackathon.py | 6 +- playground/structs/load_balancer_example.py | 34 ++ playground/structs/multi_modal_rag_agent.py | 2 +- playground/tools/agent_with_tools_example.py | 2 +- pyproject.toml | 2 +- requirements.txt | 29 -- scripts/{ => auto_tests_docs}/auto_docs.py | 0 scripts/cleanup/cleanup_entry.sh | 0 scripts/{ => cleanup}/code_quality.sh | 0 scripts/cleanup/del_pycache.sh | 25 ++ scripts/{ => cleanup}/json_log_cleanup.py | 6 +- scripts/{ => cleanup}/log_cleanup.py | 0 scripts/{ => cleanup}/log_cleanup.sh | 0 scripts/del_pycache.sh | 7 - .../{ => misc}/get_package_requirements.py | 0 scripts/{ => misc}/playground_to_examples.sh | 0 .../requirementstxt_to_pyproject.py | 0 scripts/{ => tests}/run_examples.sh | 0 scripts/{ => tests}/test_name.sh | 0 scripts/{ => tests}/tests.sh | 0 swarms/prompts/tools.py | 67 ++++ swarms/schemas/hass_agent_schema.py | 115 +++++++ swarms/structs/__init__.py | 5 +- swarms/structs/agent.py | 117 +++++-- swarms/structs/hiearchical_swarm.py | 137 +++++++- swarms/structs/swarm_load_balancer.py | 35 +- swarms/structs/tree_of_thoughts.py | 121 +++++++ swarms/tools/__init__.py | 48 +-- swarms/tools/base_tool.py | 39 ++- swarms/tools/exec_tool.py | 168 ---------- swarms/tools/func_calling_executor.py | 176 ++++++++++ swarms/tools/func_to_str.py | 40 +++ ...=> openai_func_calling_schema_pydantic.py} | 0 swarms/tools/openai_tool_creator_decorator.py | 316 +----------------- swarms/tools/prebuilt/__init__.py | 9 + .../tools/{ => prebuilt}/code_interpreter.py | 0 swarms/tools/{ => prebuilt}/math_eval.py | 0 swarms/tools/pydantic_to_json.py | 39 --- swarms/tools/tool.py | 8 - swarms/tools/tool_utils.py | 35 ++ swarms/utils/__init__.py | 6 +- .../utils/test_subprocess_code_interpreter.py | 2 +- 51 files changed, 1230 insertions(+), 728 deletions(-) delete mode 100644 docs/requirements.txt create mode 100644 new_agent_tool_system.py delete mode 100644 playground/demos/agentic_space_traffic_control/tools.py create mode 100644 playground/demos/plant_biologist_swarm/prompts.py create mode 100644 playground/demos/plant_biologist_swarm/swarm_workers_agents.py create mode 100644 playground/demos/plant_biologist_swarm/tomato.jpg create mode 100644 playground/structs/load_balancer_example.py delete mode 100644 requirements.txt rename scripts/{ => auto_tests_docs}/auto_docs.py (100%) create mode 100644 scripts/cleanup/cleanup_entry.sh rename scripts/{ => cleanup}/code_quality.sh (100%) create mode 100755 scripts/cleanup/del_pycache.sh rename scripts/{ => cleanup}/json_log_cleanup.py (89%) rename scripts/{ => cleanup}/log_cleanup.py (100%) rename scripts/{ => cleanup}/log_cleanup.sh (100%) delete mode 100755 scripts/del_pycache.sh rename scripts/{ => misc}/get_package_requirements.py (100%) rename scripts/{ => misc}/playground_to_examples.sh (100%) rename scripts/{ => misc}/requirementstxt_to_pyproject.py (100%) rename scripts/{ => tests}/run_examples.sh (100%) rename scripts/{ => tests}/test_name.sh (100%) rename scripts/{ => tests}/tests.sh (100%) create mode 100644 swarms/schemas/hass_agent_schema.py create mode 100644 swarms/structs/tree_of_thoughts.py delete mode 100644 swarms/tools/exec_tool.py create mode 100644 swarms/tools/func_calling_executor.py create mode 100644 swarms/tools/func_to_str.py rename swarms/tools/{openai_func_calling_schema.py => openai_func_calling_schema_pydantic.py} (100%) create mode 100644 swarms/tools/prebuilt/__init__.py rename swarms/tools/{ => prebuilt}/code_interpreter.py (100%) rename swarms/tools/{ => prebuilt}/math_eval.py (100%) delete mode 100644 swarms/tools/tool.py diff --git a/.gitignore b/.gitignore index 8e65f79f..0d8fa52d 100644 --- a/.gitignore +++ b/.gitignore @@ -14,6 +14,7 @@ runs chroma Unit Testing Agent_state.json Devin_state.json +json_logs Medical Image Diagnostic Agent_state.json D_state.json swarms/__pycache__ diff --git a/docs/requirements.txt b/docs/requirements.txt deleted file mode 100644 index c3b60fc7..00000000 --- a/docs/requirements.txt +++ /dev/null @@ -1,22 +0,0 @@ -mkdocs -mkdocs-material -mkdocs-glightbox -mkdocs-git-authors-plugin -mkdocs-git-revision-date-plugin -mkdocs-git-committers-plugin -mkdocstrings -mike -mkdocs-jupyter -mkdocs-git-committers-plugin-2 -mkdocs-git-revision-date-localized-plugin -mkdocs-redirects -mkdocs-material-extensions -mkdocs-simple-hooks -mkdocs-awesome-pages-plugin -mkdocs-versioning -mkdocs-mermaid2-plugin -mkdocs-include-markdown-plugin -mkdocs-enumerate-headings-plugin -mkdocs-autolinks-plugin -mkdocs-minify-html-plugin -mkdocs-autolinks-plugin \ No newline at end of file diff --git a/new_agent_tool_system.py b/new_agent_tool_system.py new file mode 100644 index 00000000..1745cf58 --- /dev/null +++ b/new_agent_tool_system.py @@ -0,0 +1,77 @@ +""" + + +tool decorated func [search_api] -> agent which parses the docs of the tool func +-> injected into prompt -> agent will output json containing tool usage -> agent output will be parsed -> tool executed +-> terminal response can be returned to agent for self-healing + + +""" + +import os + +from dotenv import load_dotenv + +# Import the OpenAIChat model and the Agent struct +from swarms import Agent, llama3Hosted + +# Load the environment variables +load_dotenv() + + +# Define a tool +def search_api(query: str, description: str): + """Search the web for the query + + Args: + query (str): _description_ + + Returns: + _type_: _description_ + """ + return f"Search results for {query}" + + +def weather_api( + query: str, +): + """_summary_ + + Args: + query (str): _description_ + """ + print(f"Getting the weather for {query}") + + +def rapid_api(query: str): + """_summary_ + + Args: + query (str): _description_ + """ + print(f"Getting the weather for {query}") + + +# Get the API key from the environment +api_key = os.environ.get("OPENAI_API_KEY") + +# Initialize the language model +llm = llama3Hosted( + temperature=0.5, +) + + +## Initialize the workflow +agent = Agent( + agent_name="Research Agent", + llm=llm, + max_loops=3, + dashboard=True, + tools=[search_api, weather_api, rapid_api], + interactive=True, + execute_tool=True, +) + +# Run the workflow on a task +out = agent.run("Use the weather tool in Miami") +print(out) diff --git a/playground/demos/agentic_space_traffic_control/game.py b/playground/demos/agentic_space_traffic_control/game.py index aa927f58..ea374231 100644 --- a/playground/demos/agentic_space_traffic_control/game.py +++ b/playground/demos/agentic_space_traffic_control/game.py @@ -7,7 +7,7 @@ from playground.demos.agentic_space_traffic_control.prompts import ( WEATHER_ANALYST_SYSTEM_PROMPT, SPACE_TRAFFIC_CONTROLLER_SYS_PROMPT, ) -from playground.demos.agentic_space_traffic_control.tools import ( +from tools import ( fetch_weather_data, ) from swarms.tools import get_openai_function_schema_from_func diff --git a/playground/demos/agentic_space_traffic_control/tools.py b/playground/demos/agentic_space_traffic_control/tools.py deleted file mode 100644 index a5c29c2a..00000000 --- a/playground/demos/agentic_space_traffic_control/tools.py +++ /dev/null @@ -1,39 +0,0 @@ -import json -import requests -from typing import Dict, Any - - -def fetch_weather_data(city: str) -> Dict[str, Any]: - """ - Fetch near real-time weather data for a city using wttr.in. - - Args: - city (str): The name of the city (e.g., "Austin, Tx"). - - Returns: - Dict[str, Any]: Weather data for the specified city. - - Raises: - Exception: If the request fails or the response is invalid. - """ - url = f"http://wttr.in/{city}" - params = {"format": "j1"} # JSON format - try: - response = requests.get(url, params=params) - response.raise_for_status() - response = json.dumps(response.json(), indent=2) - return response - except requests.RequestException as e: - raise Exception(f"Failed to fetch weather data: {e}") - except ValueError: - raise Exception("Invalid response format.") - - -# # Example usage -# city = "Huntsville, AL" - -# try: -# weather_data = fetch_weather_data(city) -# print("Weather Data:", weather_data) -# except Exception as e: -# print(e) diff --git a/playground/demos/plant_biologist_swarm/prompts.py b/playground/demos/plant_biologist_swarm/prompts.py new file mode 100644 index 00000000..759e02d0 --- /dev/null +++ b/playground/demos/plant_biologist_swarm/prompts.py @@ -0,0 +1,100 @@ +""" +5 agent swarm + +image of plant -> diagnoser [what plant is it?] -> disease detector [is it healthy?] -> treatment recommender [what should I do?] -> growth predictor [how will it grow?] -> planter [where should I plant it?] / Harvester [when should I harvest it?] + + + +""" + + +def diagnoser_agent() -> str: + prompt = """ + You are a Plant Diagnoser Agent. Your task is to accurately identify the plant species from the provided image. + You will receive an image of a plant, and you need to determine the specific type of plant it is. + + Steps: + 1. Analyze the given image. + 2. Identify distinguishing features such as leaf shape, color, size, and any other relevant characteristics. + 3. Use your plant identification database or model to match these features with a known plant species. + 4. Provide a clear and specific identification of the plant species. + + Output: + - Plant species identified with a high degree of accuracy. + - Provide any relevant information or characteristics that support your identification through a rigorous analysis of the image. + - Identify any potential challenges or ambiguities in the identification process and address them accordingly. + """ + return prompt + + +def disease_detector_agent() -> str: + prompt = """ + You are the Disease Detector Agent. + Your task is to determine the health status of the identified plant. + You will receive an image of the plant and its identified species from the Diagnoser Agent. + + Steps: + 1. Analyze the given image with a focus on signs of disease or health issues. + 2. Look for symptoms such as discoloration, spots, wilting, or any other abnormalities. + 3. Cross-reference these symptoms with known diseases for the identified plant species. + 4. Determine if the plant is healthy or diseased, and if diseased, identify the specific disease. + + Output: + - Health status of the plant (Healthy or Diseased). + - If diseased, specify the disease and provide relevant confidence scores or supporting information. + - Provide a rigorous analysis of the image to support your diagnosis. + """ + return prompt + + +def treatment_recommender_agent() -> str: + prompt = """ + You are the Treatment Recommender Agent. + Your task is to recommend appropriate treatments based on the plant's health status provided by the Disease Detector Agent. + You will receive the plant species, health status, and disease information. + + Steps: + 1. Analyze the health status and, if applicable, the specific disease affecting the plant. + 2. Refer to your database or model of treatment options suitable for the identified plant species and its specific condition. + 3. Determine the most effective treatment methods, considering factors such as severity of the disease, plant species, and environmental conditions. + 4. Provide detailed treatment recommendations. + + Output: + - Detailed treatment plan, including methods, materials, and steps. + - Any additional tips or considerations for optimal treatment. + """ + return prompt + + +def growth_predictor_agent() -> str: + prompt = """ + You are the Growth Predictor Agent. Your task is to predict the future growth of the plant based on the current health status and treatment recommendations. You will receive the plant species, health status, and treatment plan. + + Steps: + 1. Analyze the current health status and the proposed treatment plan. + 2. Use growth prediction models to forecast the plant’s growth trajectory. + 3. Consider factors such as plant species, health improvements from treatment, environmental conditions, and typical growth patterns. + 4. Provide a growth prediction timeline. + + Output: + - Growth prediction, including key milestones and timeframes. + - Any assumptions or conditions that may affect the growth prediction. + """ + return prompt + + +def harvester_agent() -> str: + prompt = """ + You are the Harvester Agent. + Your task is to recommend the optimal harvesting time based on the plant’s growth prediction. + You will receive the plant species and growth prediction timeline. + + Steps: + 1. Analyze the growth prediction and determine the optimal harvesting time. + 2. Consider factors such as maturity, peak nutritional value, and market conditions. + 3. Recommend the best time to harvest to ensure optimal quality and yield. + + Output: + - Detailed harvesting time recommendation with justification. + """ + return prompt diff --git a/playground/demos/plant_biologist_swarm/swarm_workers_agents.py b/playground/demos/plant_biologist_swarm/swarm_workers_agents.py new file mode 100644 index 00000000..d389edd5 --- /dev/null +++ b/playground/demos/plant_biologist_swarm/swarm_workers_agents.py @@ -0,0 +1,117 @@ +import os + +from dotenv import load_dotenv +from playground.demos.plant_biologist_swarm.prompts import ( + diagnoser_agent, + disease_detector_agent, + growth_predictor_agent, + harvester_agent, + treatment_recommender_agent, +) + +from swarms import Agent, GPT4VisionAPI + + +# Load the OpenAI API key from the .env file +load_dotenv() + +# Initialize the OpenAI API key +api_key = os.environ.get("OPENAI_API_KEY") + + +# llm = llm, +llm = GPT4VisionAPI( + max_tokens=4000, +) + +# Initialize Diagnoser Agent +diagnoser_agent = Agent( + agent_name="Diagnoser Agent", + system_prompt=diagnoser_agent(), + llm=llm, + max_loops=1, + dashboard=False, + streaming_on=True, + verbose=True, + # saved_state_path="diagnoser.json", + multi_modal=True, + autosave=True, +) + +# Initialize Harvester Agent +harvester_agent = Agent( + agent_name="Harvester Agent", + system_prompt=harvester_agent(), + llm=llm, + max_loops=1, + dashboard=False, + streaming_on=True, + verbose=True, + # saved_state_path="harvester.json", + multi_modal=True, + autosave=True, +) + +# Initialize Growth Predictor Agent +growth_predictor_agent = Agent( + agent_name="Growth Predictor Agent", + system_prompt=growth_predictor_agent(), + llm=llm, + max_loops=1, + dashboard=False, + streaming_on=True, + verbose=True, + # saved_state_path="growth_predictor.json", + multi_modal=True, + autosave=True, +) + +# Initialize Treatment Recommender Agent +treatment_recommender_agent = Agent( + agent_name="Treatment Recommender Agent", + system_prompt=treatment_recommender_agent(), + llm=llm, + max_loops=1, + dashboard=False, + streaming_on=True, + verbose=True, + # saved_state_path="treatment_recommender.json", + multi_modal=True, + autosave=True, +) + +# Initialize Disease Detector Agent +disease_detector_agent = Agent( + agent_name="Disease Detector Agent", + system_prompt=disease_detector_agent(), + llm=llm, + max_loops=1, + dashboard=False, + streaming_on=True, + verbose=True, + # saved_state_path="disease_detector.json", + multi_modal=True, + autosave=True, +) +agents = [ + diagnoser_agent, + disease_detector_agent, + treatment_recommender_agent, + growth_predictor_agent, + harvester_agent, +] + +task = "Conduct a diagnosis on the patient's symptoms." +img = "tomato.jpg" + +loop = 0 +for i in range(len(agents)): + if i == 0: + output = agents[i].run(task, img) + + else: + output = agents[i].run(output, img) + + # Add extensive logging for each agent + print(f"Agent {i+1} - {agents[i].agent_name}") + 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