From 69bfa4c242f5575132b013fa2d79b48869fd3f95 Mon Sep 17 00:00:00 2001 From: Eternal Reclaimer <98760976+kyegomez@users.noreply.github.com> Date: Sun, 11 Feb 2024 09:24:37 -0800 Subject: [PATCH 001/105] Update README.md --- README.md | 12 +++--------- 1 file changed, 3 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 1b22bc10..5803a41e 100644 --- a/README.md +++ b/README.md @@ -28,7 +28,7 @@ Run example in Collab: Date: Mon, 12 Feb 2024 10:05:35 +0000 Subject: [PATCH 002/105] Bump codacy/codacy-analysis-cli-action from 4.3.0 to 4.4.0 Bumps [codacy/codacy-analysis-cli-action](https://github.com/codacy/codacy-analysis-cli-action) from 4.3.0 to 4.4.0. - [Release notes](https://github.com/codacy/codacy-analysis-cli-action/releases) - [Commits](https://github.com/codacy/codacy-analysis-cli-action/compare/5cc54a75f9ad88159bb54046196d920e40e367a5...33d455949345bddfdb845fba76b57b70cc83754b) --- updated-dependencies: - dependency-name: codacy/codacy-analysis-cli-action dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] --- .github/workflows/codacy.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/codacy.yml b/.github/workflows/codacy.yml index 55e4b49f..5f58b11d 100644 --- a/.github/workflows/codacy.yml +++ b/.github/workflows/codacy.yml @@ -40,7 +40,7 @@ jobs: # Execute Codacy Analysis CLI and generate a SARIF output with the security issues identified during the analysis - name: Run Codacy Analysis CLI - uses: codacy/codacy-analysis-cli-action@5cc54a75f9ad88159bb54046196d920e40e367a5 + uses: codacy/codacy-analysis-cli-action@33d455949345bddfdb845fba76b57b70cc83754b with: # Check https://github.com/codacy/codacy-analysis-cli#project-token to get your project token from your Codacy repository # You can also omit the token and run the tools that support default configurations From 641b466c6736b7debb624ab53ff5860d1a1ca59c Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 14 Feb 2024 22:33:30 -0800 Subject: [PATCH 003/105] [FEATS][Majority Voting] [BUGF][Conversatopn] [ARCHITECTURE][Rust backend] --- Cargo.toml | 17 + docs/corporate/data_room.md | 12 + example.py | 4 +- .../demos/education/education_example.py | 13 +- .../demos/simple_rag/simple_rag_text.py | 24 -- .../structs/agent_with_tools_example.py | 9 +- playground/structs/majority_voting.py | 21 + .../structs/multi_modal_rag_agent.py | 0 .../structs/stackoverflow_swarm_example.py | 0 runtime/concurrent_exec.rs | 93 +++++ runtime/cuda_wrapper.rs | 71 ++++ runtime/file_utils.rs | 37 ++ runtime/multi_threading.rs | 110 ++++++ swarms/__init__.py | 5 + swarms/memory/chroma_db.py | 4 +- swarms/models/clipq.py | 17 +- swarms/structs/__init__.py | 5 +- swarms/structs/agent.py | 10 +- swarms/structs/base_multiagent_structure.py | 197 ++++++++++ swarms/structs/conversation.py | 3 +- swarms/structs/debate.py | 366 ++++++++++++++++++ swarms/structs/majority_voting.py | 140 +++++++ swarms/structs/stackoverflow_swarm.py | 93 +++++ tests/engines/test_cuda_wrapper.rs | 26 ++ tests/engines/test_multithreading.rs | 62 +++ 25 files changed, 1285 insertions(+), 54 deletions(-) create mode 100644 Cargo.toml delete mode 100644 playground/demos/simple_rag/simple_rag_text.py create mode 100644 playground/structs/majority_voting.py rename multi_modal_rag_agent.py => playground/structs/multi_modal_rag_agent.py (100%) create mode 100644 playground/structs/stackoverflow_swarm_example.py create mode 100644 runtime/concurrent_exec.rs create mode 100644 runtime/cuda_wrapper.rs create mode 100644 runtime/file_utils.rs create mode 100644 runtime/multi_threading.rs create mode 100644 swarms/structs/base_multiagent_structure.py create mode 100644 swarms/structs/debate.py create mode 100644 swarms/structs/stackoverflow_swarm.py create mode 100644 tests/engines/test_cuda_wrapper.rs create mode 100644 tests/engines/test_multithreading.rs diff --git a/Cargo.toml b/Cargo.toml new file mode 100644 index 00000000..a4fd0c06 --- /dev/null +++ b/Cargo.toml @@ -0,0 +1,17 @@ +[package] +name = "engine" +version = "0.1.0" +edition = "2018" + +[lib] +name = "engine" +path = "src/my_lib.rs" +crate-type = ["cdylib"] + +[dependencies] +pyo3 = { version = "0.15", features = ["extension-module"] } +rayon = "1.5.1" +log = "0.4.14" +rustcuda = "0.1.0" +rustcuda_derive = "*" +rustcuda_core = "0.1" diff --git a/docs/corporate/data_room.md b/docs/corporate/data_room.md index 946f209f..8df244a4 100644 --- a/docs/corporate/data_room.md +++ b/docs/corporate/data_room.md @@ -70,6 +70,11 @@ The team has thousands of hours building and optimizing autonomous agents. Leade Key milestones: get 80K framework users in January 2024, start contracts in target verticals, introduce commercial products in 2025 with various pricing models. +### **Pre-Seed Pitch Deck** +- [Here is our pitch deck for our preseed round](https://www.figma.com/file/LlEMXZ48HTIG3S9VzdibaB/Swarm-Pitch-Deck?type=design&node-id=0%3A1& +mode=design&t=D3023hPOz27M9RGD-1) + + ### **The Swarm Corporation Memo** To learn more about our mission, vision, plans for GTM, and much more please refer to the [Swarm Memo here](https://docs.google.com/document/d/1hS_nv_lFjCqLfnJBoF6ULY9roTbSgSuCkvXvSUSc7Lo/edit?usp=sharing) @@ -93,3 +98,10 @@ Swarms is an open source framework for developers in python to enable seamless, | Github Traffic Metrics | Metrics related to traffic, such as views and clones on Github. | [Github Traffic Metrics](https://github.com/kyegomez/swarms/graphs/traffic) | | Issues with the framework | Current open issues for the product on Github. | [![GitHub issues](https://img.shields.io/github/issues/kyegomez/swarms)](https://github.com/kyegomez/swarms/issues) | + + +## **Corporate Documents** +This section is dedicated entirely for corporate documents. + +- [Cap Table](https://docs.google.com/spreadsheets/d/1wuTWbfhYaY5Xp6nSQ9R0wDtSpwSS9coHxsjKd0UbIDc/edit?usp=sharing) + diff --git a/example.py b/example.py index bd63c71c..d9ba8f1c 100644 --- a/example.py +++ b/example.py @@ -11,4 +11,6 @@ agent = Agent( ) # Run the workflow on a task -agent("Generate a 10,000 word blog on health and wellness.") +agent( + "Find a chick fil a equivalent in san francisco in hayes valley" +) diff --git a/playground/demos/education/education_example.py b/playground/demos/education/education_example.py index 77f16f1b..266cede9 100644 --- a/playground/demos/education/education_example.py +++ b/playground/demos/education/education_example.py @@ -1,8 +1,7 @@ import os from dotenv import load_dotenv from swarms.models import OpenAIChat -from swarms.models.stable_diffusion import StableDiffusion -from swarms.structs import Agent, SequentialWorkflow +from swarms import Agent, SequentialWorkflow import swarms.prompts.education as edu_prompts # Load environment variables @@ -15,9 +14,6 @@ llm = OpenAIChat( openai_api_key=api_key, temperature=0.5, max_tokens=3000 ) -# Initialize Stable Diffusion -sd_api = StableDiffusion(api_key=stability_api_key) - # User preferences (can be dynamically set in a real application) user_preferences = { "subjects": "Cognitive Architectures", @@ -60,8 +56,6 @@ workflow.add(sample_lesson_agent, "Generate a practice test") workflow.run() # Generate an image using Stable Diffusion -image_result = sd_api.run(image_prompt) - # Output results for each task for task in workflow.tasks: print( @@ -70,7 +64,4 @@ for task in workflow.tasks: ) # Output image result -print( - "Image Generation Task: Generate an image for the interactive" - f" lesson\nResult: {image_result}" -) +print("Image Generation Task: Generate an image for the interactive") diff --git a/playground/demos/simple_rag/simple_rag_text.py b/playground/demos/simple_rag/simple_rag_text.py deleted file mode 100644 index 8e8e3cb0..00000000 --- a/playground/demos/simple_rag/simple_rag_text.py +++ /dev/null @@ -1,24 +0,0 @@ -# Text embeddings, image embeddings, and multimodal embeddings -# Add text and image embeddings into postgresl database - -from swarms.models.jina_embeds import JinaEmbeddings -from swarms.models.gigabind import Gigabind - -# Model -model = JinaEmbeddings( - max_length=8192, - device="cuda", - quantize=True, - huggingface_api_key="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", -) - - -# Encode text - -embeddings = model("Encode this super long document text") - - -# Embed images or text -model = Gigabind() - -multi_modal_embeddings = model(text=[text], imgs=[img1, img2, img3]) diff --git a/playground/structs/agent_with_tools_example.py b/playground/structs/agent_with_tools_example.py index 99f21638..44dcd8f6 100644 --- a/playground/structs/agent_with_tools_example.py +++ b/playground/structs/agent_with_tools_example.py @@ -13,8 +13,7 @@ import os from dotenv import load_dotenv # Import the OpenAIChat model and the Agent struct -from swarms.models import OpenAIChat -from swarms.structs import Agent +from swarms import OpenAIChat, Agent from swarms.tools.tool import tool # Load the environment variables @@ -69,7 +68,11 @@ llm = OpenAIChat( ## Initialize the workflow agent = Agent( - llm=llm, max_loops=1, dashboard=True, tools=[search_api] + agent_name="Research Agent", + llm=llm, + max_loops=1, + dashboard=True, + tools=[search_api, weather_api, rapid_api], ) # Run the workflow on a task diff --git a/playground/structs/majority_voting.py b/playground/structs/majority_voting.py new file mode 100644 index 00000000..5eefb8ab --- /dev/null +++ b/playground/structs/majority_voting.py @@ -0,0 +1,21 @@ +from swarms import Agent, OpenAIChat, MajorityVoting + +# Initialize the llm +llm = OpenAIChat() + +# Initialize the agents +agent1 = Agent(llm=llm, max_loops=1) +agent2 = Agent(llm=llm, max_loops=1) +agent3 = Agent(llm=llm, max_loops=1) + + +# Initialize the majority voting +mv = MajorityVoting( + agents=[agent1, agent2, agent3], + concurrent=True, + multithreaded=True, +) + + +# Start the majority voting +mv.run("What is the capital of France?") diff --git a/multi_modal_rag_agent.py b/playground/structs/multi_modal_rag_agent.py similarity index 100% rename from multi_modal_rag_agent.py rename to playground/structs/multi_modal_rag_agent.py diff --git a/playground/structs/stackoverflow_swarm_example.py b/playground/structs/stackoverflow_swarm_example.py new file mode 100644 index 00000000..e69de29b diff --git a/runtime/concurrent_exec.rs b/runtime/concurrent_exec.rs new file mode 100644 index 00000000..2d3bb504 --- /dev/null +++ b/runtime/concurrent_exec.rs @@ -0,0 +1,93 @@ +use pyo3::prelude::*; +use pyo3::wrap_pyfunction; +use pyo3::types::IntoPyDict; +use rayon::{ThreadPool, ThreadPoolBuilder, prelude::*}; +use std::sync::{Arc, Mutex}; +use std::time::{Duration, Instant}; +use std::thread; + + +#[pymodule] +fn rust_module(py: Python, m: &PyModule) -> PyResult<()> { + m.add_function(wrap_pyfunction!(concurrent_exec, m)?)?; + Ok(()) +} + +#[pyfunction] +pub fn concurrent_exec( + py_codes: Vec<&str>, + timeout: Option, + num_threads: usize, + error_handler: F, + log_function: G, + result_handler: H, +) -> PyResult>> + +/// This function wraps Python code in Rust concurrency for ultra high performance. +/// +/// # Arguments +/// +/// * `py_codes` - A vector of string slices that holds the Python codes to be executed. +/// * `timeout` - An optional duration to specify a timeout for the Python code execution. +/// * `num_threads` - The number of threads to use for executing the Python code. +/// * `error_handler` - A function to handle errors during Python code execution. +/// * `log_function` - A function to log the execution of the Python code. +/// * `result_handler` - A function to handle the results of the Python code execution. +/// +/// # Example +/// +/// ``` +/// let py_codes = vec!["print('Hello, World!')", "print('Hello, Rust!')"]; +/// let timeout = Some(Duration::from_secs(5)); +/// let num_threads = 4; +/// let error_handler = |e| eprintln!("Error: {}", e); +/// let log_function = |s| println!("Log: {}", s); +/// let result_handler = |r| println!("Result: {:?}", r); +/// execute_python_codes(py_codes, timeout, num_threads, error_handler, log_function, result_handler); +/// ``` +where + F: Fn(&str), + G: Fn(&str), + H: Fn(&PyResult<()>), +{ + let gil = Python::acquire_gil(); + let py = gil.python(); + let py_codes = Arc::new(Mutex::new(py_codes)); + let results = Arc::new(Mutex::new(Vec::new())); + let pool = ThreadPool::new(num_threads); + + pool.install(|| { + py_codes.par_iter().for_each(|code| { + let locals = [("__name__", "__main__")].into_py_dict(py); + let globals = [("__name__", "__main__")].into_py_dict(py); + + log_function(&format!("Executing Python code: {}", code)); + let result = py.run(code, Some(globals), Some(locals)); + + match timeout { + Some(t) => { + let now = Instant::now(); + let timeout_thread = thread::spawn(move || { + while now.elapsed() < t { + if let Ok(_) = result { + break; + } + } + if now.elapsed() >= t { + error_handler(&format!("Python code execution timed out: {}", code)); + } + }); + + timeout_thread.join().unwrap(); + } + None => {} + } + + results.lock().unwrap().push(result.clone()); + result_handler(&result); + }); + }); + + pool.join(); + Ok(results.lock().unwrap().clone()) +} \ No newline at end of file diff --git a/runtime/cuda_wrapper.rs b/runtime/cuda_wrapper.rs new file mode 100644 index 00000000..7516088b --- /dev/null +++ b/runtime/cuda_wrapper.rs @@ -0,0 +1,71 @@ +use pyo3::prelude::*; +use rustacuda::prelude::*; +use rustacuda::memory::DeviceBox; +use std::error::Error; +use std::ffi::CString; + +#[pymodule] +fn rust_cuda(_py: Python, m: &PyModule) -> PyResult<()> { + #[pyfn(m, "execute_on_device")] + fn execute_on_device(py: Python, device_id: u32, a: f32, b: f32) -> PyResult { + /// The result of executing the CUDA operation. + let result = py.allow_threads(|| { + execute_cuda(device_id, a, b) + }); + match result { + Ok(res) => Ok(res), + Err(err) => Err(PyErr::new::(format!("{}", err))), + } + } + Ok(()) +} + +fn execute_cuda(device_id: u32, a: f32, b: f32) -> Result> { + rustacuda::init(CudaFlags::empty())?; + let device = Device::get_device(device_id)?; + /// Creates a new CUDA context and pushes it onto the current thread's stack. + /// + /// # Arguments + /// + /// * `flags` - The flags to be used when creating the context. + /// * `device` - The device on which the context will be created. + /// + /// # Returns + /// + /// The newly created CUDA context. + /// + /// # Errors + /// + /// Returns an error if the context creation fails. + /// + /// # Example + /// + /// ```rust + /// use swarms::cuda_wrapper::Context; + /// + /// let device = 0; + /// let context = Context::create_and_push(ContextFlags::MAP_HOST | ContextFlags::SCHED_AUTO, device)?; + /// ``` + pub fn create_and_push(flags: ContextFlags, device: i32) -> Result { + // implementation goes here + } + let context = Context::create_and_push(ContextFlags::MAP_HOST | ContextFlags::SCHED_AUTO, device)?; + let module_data = CString::new(include_str!("../resources/add.ptx"))?; + let module = Module::load_from_string(&module_data)?; + let stream = Stream::new(StreamFlags::NON_BLOCKING, None)?; + let mut x = DeviceBox::new(&a)?; + let mut y = DeviceBox::new(&b)?; + let mut result = DeviceBox::new(&0.0f32)?; + unsafe { + launch!(module.sum<<<1, 1, 0, stream>>>( + x.as_device_ptr(), + y.as_device_ptr(), + result.as_device_ptr(), + 1 + ))?; + } + stream.synchronize()?; + let mut result_host = 0.0f32; + result.copy_to(&mut result_host)?; + Ok(result_host) +} \ No newline at end of file diff --git a/runtime/file_utils.rs b/runtime/file_utils.rs new file mode 100644 index 00000000..62d8ccb0 --- /dev/null +++ b/runtime/file_utils.rs @@ -0,0 +1,37 @@ +use std::fs::File; +use std::io::prelude::*; +use std::time::Instant; +use std::io::{BufReader, io}; +use ranyon::prelude::{IntoParallelRefIterator, ParallelIterator}; + +fn read_file(path: &str) -> Vec { + /// Reads the contents of a file located at the specified path. + /// + /// # Arguments + /// + /// * `path` - The path to the file. + /// + /// # Returns + /// + /// A `Result` containing a vector of strings representing the lines of the file if the file was successfully read, + /// or an `io::Error` if there was an error reading the file. + /// + /// # Example + /// + /// ``` + /// use std::io; + /// use std::fs::File; + /// use std::io::BufReader; + /// + /// fn read_file(path: &str) -> io::Result> { + /// let contents: io::Result> = BufReader::new(File::open(path).expect("Could not open file")) + /// .lines() + /// .collect(); + /// contents + /// } + /// ``` + let contents: io::Result> = BufReader::new(File::open(path).expect("Could not open file")) + .lines() + .collect(); + return contents.expect("Could not read file"); +} \ No newline at end of file diff --git a/runtime/multi_threading.rs b/runtime/multi_threading.rs new file mode 100644 index 00000000..99346ed2 --- /dev/null +++ b/runtime/multi_threading.rs @@ -0,0 +1,110 @@ +/// This module provides a multi-threading processor for executing Python modules and functions in parallel. +/// It utilizes the `rayon` crate for parallel processing and the `pyo3` crate for interacting with the Python interpreter. +/// The `multithreading_processor` function takes a vector of `PythonModule` structs and the number of threads to use. +/// Each `PythonModule` struct contains the name of the Python module, the name of the function to call, and any arguments to pass to the function. +/// The function imports the Python module, calls the specified function, and sends any errors encountered back to the main thread. +/// If an import error occurs, a `PythonError::ImportError` is returned. +/// If a function call error occurs, a `PythonError::FunctionError` is returned. + +use pyo3::prelude::*; +use pyo3::wrap_pyfunction; +use rayon::prelude::*; +use std::sync::mpsc::{channel, Sender}; +use std::sync::{Arc, Mutex}; +use log::{info, error}; + +struct PythonModule<'a> { + name: &'a str, + function: &'a str, +} + +enum PythonError { + ImportError(String), + FunctionError(String), +} + +#[pyfunction] +fn my_module(py: Python, m: &PyModule) -> PyResult<()> { + m.add_function(wrap_pyfunction!(process_python_modules, m)?)?; + Ok(()) +} + +#[pyfunction] +fn process_python_modules(modules: Vec, num_threads: usize) -> Result<(), PythonError> { + /// The function returns `Ok(())` if all modules are processed successfully. + /// Note: This code assumes that the necessary dependencies (`pyo3`, `rayon`, `log`) are already imported and initialized. + /// + /// # Arguments + /// + /// * `modules` - A vector of `PythonModule` structs representing the Python modules and functions to execute. + /// * `num_threads` - The number of threads to use for parallel processing. + /// + /// # Examples + /// + /// ``` + /// use pyo3::types::PyModule; + /// use pyo3::types::PyResult; + /// use pyo3::prelude::*; + /// + /// struct PythonModule<'a> { + /// name: &'a str, + /// function: &'a str, + /// args: Vec<&'a str>, + /// } + /// + /// #[pymodule] + /// fn multithreading_processor(modules: Vec, num_threads: usize) -> Result<(), PythonError> { + /// // Function implementation + /// Ok(()) + /// } + /// ``` + /// + /// # Errors + /// + /// Returns a `PythonError` if an import error or a function call error occurs. + /// + /// # Panics + /// + /// This function does not panic. + /// + /// # Safety + /// + /// This function is safe to call, but it assumes that the necessary dependencies (`pyo3`, `rayon`, `log`) are already imported and initialized. + // Initialize Python interpreter + let gil = Python::acquire_gil(); + let py = gil.python(); + + // Set the global thread pool's configuration + rayon::ThreadPoolBuilder::new() + .num_threads(num_threads) + .build_global() + .unwrap(); + + // Create a channel to send errors from threads to the main thread + let (tx, rx) = channel(); + let tx = Arc::new(Mutex::new(tx)); + + // Process each Python module in parallel + modules.par_iter().for_each(|module| { + let result = PyModule::import(py, module.name) + .map_err(|_| PythonError::ImportError(module.name.to_string())) + .and_then(|m| m.call0(module.function) + .map_err(|_| PythonError::FunctionError(module.function.to_string()))); + + if let Err(e) = result { + let tx = tx.lock().unwrap(); + tx.send(e).unwrap(); + } + }); + + // Check for errors + drop(tx); // Close the sender + for error in rx { + match error { + PythonError::ImportError(module) => error!("Failed to import module {}", module), + PythonError::FunctionError(function) => error!("Failed to call function {}", function), + } + } + + Ok(()) +} \ No newline at end of file diff --git a/swarms/__init__.py b/swarms/__init__.py index d88d9bf8..bece68ef 100644 --- a/swarms/__init__.py +++ b/swarms/__init__.py @@ -14,3 +14,8 @@ from swarms.tokenizers import * # noqa: E402, F403 from swarms.loaders import * # noqa: E402, F403 from swarms.artifacts import * # noqa: E402, F403 from swarms.chunkers import * # noqa: E402, F403 +from swarms.structs import * # noqa: E402, F403 +from swarms.agents import * # noqa: E402, F403 +from swarms.models import * # noqa: E402, F403 +from swarms.tools import * # noqa: E402, F403 +from swarms.telemetry import * # noqa: E402, F403 diff --git a/swarms/memory/chroma_db.py b/swarms/memory/chroma_db.py index 4418e90d..8a5b6e91 100644 --- a/swarms/memory/chroma_db.py +++ b/swarms/memory/chroma_db.py @@ -47,8 +47,8 @@ class ChromaDB: def __init__( self, - metric: str, - output_dir: str, + metric: str = "cosine", + output_dir: str = "swarms", limit_tokens: Optional[int] = 1000, n_results: int = 2, embedding_function: Callable = None, diff --git a/swarms/models/clipq.py b/swarms/models/clipq.py index 7e49e74a..e6d587c9 100644 --- a/swarms/models/clipq.py +++ b/swarms/models/clipq.py @@ -8,19 +8,16 @@ from transformers import CLIPModel, CLIPProcessor class CLIPQ: - """ - ClipQ is an CLIQ based model that can be used to generate captions for images. - - - Attributes: - model_name (str): The name of the model to be used. - query_text (str): The query text to be used for the model. + """CLIPQ model for image and text retrieval Args: - model_name (str): The name of the model to be used. - query_text (str): The query text to be used for the model. - + model_name (str): The name of the CLIP model to use + query_text (str): The query text to use for the model + Example: + >>> clipq = CLIPQ() + >>> image = clipq.fetch_image_from_url() + >>> vectors = clipq.get_vectors(image) """ diff --git a/swarms/structs/__init__.py b/swarms/structs/__init__.py index ec75cb9f..c8da387a 100644 --- a/swarms/structs/__init__.py +++ b/swarms/structs/__init__.py @@ -62,8 +62,9 @@ from swarms.structs.majority_voting import ( most_frequent, parse_code_completion, majority_voting, + MajorityVoting, ) - +from swarms.structs.stackoverflow_swarm import StackOverflowSwarm __all__ = [ "Agent", @@ -123,4 +124,6 @@ __all__ = [ "most_frequent", "parse_code_completion", "majority_voting", + "MajorityVoting", + "StackOverflowSwarm", ] diff --git a/swarms/structs/agent.py b/swarms/structs/agent.py index 3b059617..4192aab3 100644 --- a/swarms/structs/agent.py +++ b/swarms/structs/agent.py @@ -145,7 +145,7 @@ class Agent: id: str = agent_id, llm: Any = None, template: Optional[str] = None, - max_loops: int = 1, + max_loops: Optional[int] = 1, stopping_condition: Optional[Callable[[str], bool]] = None, loop_interval: int = 1, retry_attempts: int = 3, @@ -181,6 +181,10 @@ class Agent: docs_folder: str = None, verbose: bool = False, parser: Optional[Callable] = None, + best_of_n: Optional[int] = None, + callback: Optional[Callable] = None, + metadata: Optional[Dict[str, Any]] = None, + callbacks: Optional[List[Callable]] = None, *args, **kwargs, ): @@ -226,6 +230,10 @@ class Agent: self.docs_folder = docs_folder self.verbose = verbose self.parser = parser + self.best_of_n = best_of_n + self.callback = callback + self.metadata = metadata + self.callbacks = callbacks # The max_loops will be set dynamically if the dynamic_loop if self.dynamic_loops: diff --git a/swarms/structs/base_multiagent_structure.py b/swarms/structs/base_multiagent_structure.py new file mode 100644 index 00000000..302f2902 --- /dev/null +++ b/swarms/structs/base_multiagent_structure.py @@ -0,0 +1,197 @@ +import json +from typing import List, Optional, Sequence + +import yaml + +from swarms.structs.agent import Agent +from swarms.structs.conversation import Conversation +from swarms.utils.logger import logger + + +class BaseMultiAgentStructure: + """ + Base class for a multi-agent structure. + + Args: + agents (List[Agent], optional): List of agents in the structure. Defaults to None. + callbacks (Optional[Sequence[callable]], optional): List of callbacks for the structure. Defaults to None. + autosave (bool, optional): Flag indicating whether to enable autosave. Defaults to False. + logging (bool, optional): Flag indicating whether to enable logging. Defaults to False. + *args: Variable length argument list. + **kwargs: Arbitrary keyword arguments. + + Attributes: + agents (List[Agent]): List of agents in the structure. + callbacks (Optional[Sequence[callable]]): List of callbacks for the structure. + autosave (bool): Flag indicating whether autosave is enabled. + logging (bool): Flag indicating whether logging is enabled. + conversation (Conversation): Conversation object for the structure. + + Methods: + metadata(): Get the metadata of the multi-agent structure. + save_to_json(filename: str): Save the current state of the multi-agent structure to a JSON file. + load_from_json(filename: str): Load the state of the multi-agent structure from a JSON file. + """ + + def __init__( + self, + agents: List[Agent] = None, + callbacks: Optional[Sequence[callable]] = None, + autosave: bool = False, + logging: bool = False, + return_metadata: bool = False, + metadata_filename: str = "multiagent_structure_metadata.json", + *args, + **kwargs, + ): + self.agents = agents + self.callbacks = callbacks + self.autosave = autosave + self.logging = logging + self.return_metadata = return_metadata + self.metadata_filename = metadata_filename + self.conversation = Conversation( + time_enabled=True, *args, **kwargs + ) + if self.logging: + self.logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(levelname)s - %(message)s", + ) + + # Handle the case where the agents are not provided + # Handle agents + for agent in self.agents: + if not isinstance(agent, Agent): + raise TypeError("Agents must be of type Agent.") + + if self.agents is None: + self.agents = [] + + # Handle the case where the callbacks are not provided + if self.callbacks is None: + self.callbacks = [] + + # Handle the case where the autosave is not provided + if self.autosave is None: + self.autosave = False + + # Handle the case where the logging is not provided + if self.logging is None: + self.logging = False + + # Handle callbacks + if callbacks is not None: + for callback in self.callbacks: + if not callable(callback): + raise TypeError("Callback must be callable.") + + # Handle autosave + if autosave: + self.save_to_json(metadata_filename) + + def metadata(self): + """ + Get the metadata of the multi-agent structure. + + Returns: + dict: The metadata of the multi-agent structure. + """ + return { + "agents": self.agents, + "callbacks": self.callbacks, + "autosave": self.autosave, + "logging": self.logging, + "conversation": self.conversation, + } + + def save_to_json(self, filename: str): + """ + Save the current state of the multi-agent structure to a JSON file. + + Args: + filename (str): The name of the file to save the multi-agent structure to. + + Returns: + None + """ + try: + with open(filename, "w") as f: + json.dump(self.__dict__, f) + except Exception as e: + logger.error(e) + + def load_from_json(self, filename: str): + """ + Load the state of the multi-agent structure from a JSON file. + + Args: + filename (str): The name of the file to load the multi-agent structure from. + + Returns: + None + """ + try: + with open(filename, "r") as f: + self.__dict__ = json.load(f) + except Exception as e: + logger.error(e) + + def save_to_yaml(self, filename: str): + """ + Save the current state of the multi-agent structure to a YAML file. + + Args: + filename (str): The name of the file to save the multi-agent structure to. + + Returns: + None + """ + try: + with open(filename, "w") as f: + yaml.dump(self.__dict__, f) + except Exception as e: + logger.error(e) + + def load_from_yaml(self, filename: str): + """ + Load the state of the multi-agent structure from a YAML file. + + Args: + filename (str): The name of the file to load the multi-agent structure from. + + Returns: + None + """ + try: + with open(filename, "r") as f: + self.__dict__ = yaml.load(f) + except Exception as e: + logger.error(e) + + def __repr__(self): + return f"{self.__class__.__name__}({self.__dict__})" + + def __str__(self): + return f"{self.__class__.__name__}({self.__dict__})" + + def __len__(self): + return len(self.agents) + + def __getitem__(self, index): + return self.agents[index] + + def __setitem__(self, index, value): + self.agents[index] = value + + def __delitem__(self, index): + del self.agents[index] + + def __iter__(self): + return iter(self.agents) + + def __reversed__(self): + return reversed(self.agents) + + def __contains__(self, value): + return value in self.agents diff --git a/swarms/structs/conversation.py b/swarms/structs/conversation.py index c4bcdba5..8132de87 100644 --- a/swarms/structs/conversation.py +++ b/swarms/structs/conversation.py @@ -6,6 +6,7 @@ from termcolor import colored from swarms.memory.base_db import AbstractDatabase from swarms.structs.base import BaseStructure from swarms.tokenizers.base_tokenizer import BaseTokenizer +from typing import Optional class Conversation(BaseStructure): @@ -61,7 +62,7 @@ class Conversation(BaseStructure): def __init__( self, - system_prompt: str, + system_prompt: Optional[str] = None, time_enabled: bool = False, database: AbstractDatabase = None, autosave: bool = False, diff --git a/swarms/structs/debate.py b/swarms/structs/debate.py new file mode 100644 index 00000000..9db84f06 --- /dev/null +++ b/swarms/structs/debate.py @@ -0,0 +1,366 @@ +import os +import json + +from swarms.structs.agent import Agent +from datetime import datetime +from typing import List + +NAME_LIST = [ + "Affirmative side", + "Negative side", + "Moderator", +] + + +class DebatePlayer(Agent): + def __init__(self, llm, name: str, *args, **kwargs) -> None: + """Create a player in the debate + + Args: + model_name(str): model name + name (str): name of this player + temperature (float): higher values make the output more random, while lower values make it more focused and deterministic + openai_api_key (str): As the parameter name suggests + sleep_time (float): sleep because of rate limits + """ + super(DebatePlayer, self).__init__( + llm=llm, agent_name=name, *args, **kwargs + ) + + +class Debate: + """Create a debate + + Args: + model_name (str): openai model name + temperature (float): higher values make the output more random, while lower values make it more focused and deterministic + num_players (int): num of players + save_file_dir (str): dir path to json file + openai_api_key (str): As the parameter name suggests + prompts_path (str): prompts path (json file) + max_round (int): maximum Rounds of Debate + sleep_time (float): sleep because of rate limits + """ + + def __init__( + self, + debate_agents: List[DebatePlayer], + temperature: float = 0, + num_players: int = 3, + save_file_dir: str = None, + prompts_path: str = None, + max_round: int = 3, + sleep_time: float = 0, + ) -> None: + self.debate_agents = debate_agents + self.num_players = num_players + self.save_file_dir = save_file_dir + self.max_round = max_round + self.sleep_time = sleep_time + + # init save file + now = datetime.now() + current_time = now.strftime("%Y-%m-%d_%H:%M:%S") + self.save_file = { + "start_time": current_time, + "end_time": "", + "temperature": temperature, + "num_players": num_players, + "success": False, + "src_lng": "", + "tgt_lng": "", + "source": "", + "reference": "", + "base_translation": "", + "debate_translation": "", + "Reason": "", + "Supported Side": "", + "players": {}, + } + prompts = json.load(open(prompts_path)) + self.save_file.update(prompts) + self.init_prompt() + + if self.save_file["base_translation"] == "": + self.create_base() + + # creat&init agents + self.create_agents() + self.init_agents() + + def init_prompt(self): + def prompt_replace(key): + self.save_file[key] = ( + self.save_file[key] + .replace("##src_lng##", self.save_file["src_lng"]) + .replace("##tgt_lng##", self.save_file["tgt_lng"]) + .replace("##source##", self.save_file["source"]) + .replace( + "##base_translation##", + self.save_file["base_translation"], + ) + ) + + prompt_replace("base_prompt") + prompt_replace("player_meta_prompt") + prompt_replace("moderator_meta_prompt") + prompt_replace("judge_prompt_last2") + + def create_base(self): + print( + "\n===== Translation Task" + f" =====\n{self.save_file['base_prompt']}\n" + ) + agent = DebatePlayer( + name="Baseline", + openai_api_key=self.openai_api_key, + ) + agent.add_message_to_memory(self.save_file["base_prompt"]) + base_translation = agent.ask() + agent.add_message_to_memory(base_translation) + self.save_file["base_translation"] = base_translation + self.save_file["affirmative_prompt"] = self.save_file[ + "affirmative_prompt" + ].replace("##base_translation##", base_translation) + self.save_file["players"][agent.name] = agent.memory_lst + + def create_agents(self): + # creates players + self.players = [ + DebatePlayer( + model_name=self.model_name, + name=name, + ) + for name in NAME_LIST + ] + self.affirmative = self.players[0] + self.negative = self.players[1] + self.moderator = self.players[2] + + def init_agents(self): + # start: set meta prompt + self.affirmative.system_prompt( + self.save_file["player_meta_prompt"] + ) + self.negative.system_prompt( + self.save_file["player_meta_prompt"] + ) + self.moderator.system_prompt( + self.save_file["moderator_meta_prompt"] + ) + + # start: first round debate, state opinions + print("===== Debate Round-1 =====\n") + self.affirmative.add_message_to_memory( + self.save_file["affirmative_prompt"] + ) + self.aff_ans = self.affirmative.ask() + self.affirmative.add_message_to_memory(self.aff_ans) + + self.negative.add_message_to_memory( + self.save_file["negative_prompt"].replace( + "##aff_ans##", self.aff_ans + ) + ) + self.neg_ans = self.negative.ask() + self.negative.add_message_to_memory(self.neg_ans) + + self.moderator.add_message_to_memory( + self.save_file["moderator_prompt"] + .replace("##aff_ans##", self.aff_ans) + .replace("##neg_ans##", self.neg_ans) + .replace("##round##", "first") + ) + self.mod_ans = self.moderator.ask() + self.moderator.add_message_to_memory(self.mod_ans) + self.mod_ans = eval(self.mod_ans) + + def round_dct(self, num: int): + dct = { + 1: "first", + 2: "second", + 3: "third", + 4: "fourth", + 5: "fifth", + 6: "sixth", + 7: "seventh", + 8: "eighth", + 9: "ninth", + 10: "tenth", + } + return dct[num] + + def save_file_to_json(self, id): + now = datetime.now() + current_time = now.strftime("%Y-%m-%d_%H:%M:%S") + save_file_path = os.path.join( + self.save_file_dir, f"{id}.json" + ) + + self.save_file["end_time"] = current_time + json_str = json.dumps( + self.save_file, ensure_ascii=False, indent=4 + ) + with open(save_file_path, "w") as f: + f.write(json_str) + + def broadcast(self, msg: str): + """Broadcast a message to all players. + Typical use is for the host to announce public information + + Args: + msg (str): the message + """ + # print(msg) + for player in self.players: + player.add_message_to_memory(msg) + + def speak(self, speaker: str, msg: str): + """The speaker broadcast a message to all other players. + + Args: + speaker (str): name of the speaker + msg (str): the message + """ + if not msg.startswith(f"{speaker}: "): + msg = f"{speaker}: {msg}" + # print(msg) + for player in self.players: + if player.name != speaker: + player.add_message_to_memory(msg) + + def ask_and_speak(self, player: DebatePlayer): + ans = player.ask() + player.add_message_to_memory(ans) + self.speak(player.name, ans) + + def run(self): + for round in range(self.max_round - 1): + if self.mod_ans["debate_translation"] != "": + break + else: + print(f"===== Debate Round-{round+2} =====\n") + self.affirmative.add_message_to_memory( + self.save_file["debate_prompt"].replace( + "##oppo_ans##", self.neg_ans + ) + ) + self.aff_ans = self.affirmative.ask() + self.affirmative.add_message_to_memory(self.aff_ans) + + self.negative.add_message_to_memory( + self.save_file["debate_prompt"].replace( + "##oppo_ans##", self.aff_ans + ) + ) + self.neg_ans = self.negative.ask() + self.negative.add_message_to_memory(self.neg_ans) + + self.moderator.add_message_to_memory( + self.save_file["moderator_prompt"] + .replace("##aff_ans##", self.aff_ans) + .replace("##neg_ans##", self.neg_ans) + .replace("##round##", self.round_dct(round + 2)) + ) + self.mod_ans = self.moderator.ask() + self.moderator.add_message_to_memory(self.mod_ans) + self.mod_ans = eval(self.mod_ans) + + if self.mod_ans["debate_translation"] != "": + self.save_file.update(self.mod_ans) + self.save_file["success"] = True + + # ultimate deadly technique. + else: + judge_player = DebatePlayer( + model_name=self.model_name, + name="Judge", + temperature=self.temperature, + openai_api_key=self.openai_api_key, + sleep_time=self.sleep_time, + ) + aff_ans = self.affirmative.memory_lst[2]["content"] + neg_ans = self.negative.memory_lst[2]["content"] + + judge_player.system_prompt( + self.save_file["moderator_meta_prompt"] + ) + + # extract answer candidates + judge_player.add_message_to_memory( + self.save_file["judge_prompt_last1"] + .replace("##aff_ans##", aff_ans) + .replace("##neg_ans##", neg_ans) + ) + ans = judge_player.ask() + judge_player.add_message_to_memory(ans) + + # select one from the candidates + judge_player.add_message_to_memory( + self.save_file["judge_prompt_last2"] + ) + ans = judge_player.ask() + judge_player.add_message_to_memory(ans) + + ans = eval(ans) + if ans["debate_translation"] != "": + self.save_file["success"] = True + # save file + self.save_file.update(ans) + self.players.append(judge_player) + + for player in self.players: + self.save_file["players"][player.name] = player.memory_lst + + +# def parse_args(): +# parser = argparse.ArgumentParser("", formatter_class=argparse.ArgumentDefaultsHelpFormatter) + +# parser.add_argument("-i", "--input-file", type=str, required=True, help="Input file path") +# parser.add_argument("-o", "--output-dir", type=str, required=True, help="Output file dir") +# parser.add_argument("-lp", "--lang-pair", type=str, required=True, help="Language pair") +# parser.add_argument("-k", "--api-key", type=str, required=True, help="OpenAI api key") +# parser.add_argument("-m", "--model-name", type=str, default="gpt-3.5-turbo", help="Model name") +# parser.add_argument("-t", "--temperature", type=float, default=0, help="Sampling temperature") + +# return parser.parse_args() + + +# if __name__ == "__main__": +# args = parse_args() +# openai_api_key = args.api_key + +# current_script_path = os.path.abspath(__file__) +# MAD_path = current_script_path.rsplit("/", 2)[0] + +# src_lng, tgt_lng = args.lang_pair.split('-') +# src_full = Language.make(language=src_lng).display_name() +# tgt_full = Language.make(language=tgt_lng).display_name() + +# config = json.load(open(f"{MAD_path}/code/utils/config4tran.json", "r")) + +# inputs = open(args.input_file, "r").readlines() +# inputs = [l.strip() for l in inputs] + +# save_file_dir = args.output_dir +# if not os.path.exists(save_file_dir): +# os.mkdir(save_file_dir) + +# for id, input in enumerate(tqdm(inputs)): +# # files = os.listdir(save_file_dir) +# # if f"{id}.json" in files: +# # continue + +# prompts_path = f"{save_file_dir}/{id}-config.json" + +# config['source'] = input.split('\t')[0] +# config['reference'] = input.split('\t')[1] +# config['src_lng'] = src_full +# config['tgt_lng'] = tgt_full + +# with open(prompts_path, 'w') as file: +# json.dump(config, file, ensure_ascii=False, indent=4) + +# debate = Debate(save_file_dir=save_file_dir, num_players=3, openai_api_key=openai_api_key, prompts_path=prompts_path, temperature=0, sleep_time=0) +# debate.run() +# debate.save_file_to_json(id) diff --git a/swarms/structs/majority_voting.py b/swarms/structs/majority_voting.py index c911b9f0..05539ecf 100644 --- a/swarms/structs/majority_voting.py +++ b/swarms/structs/majority_voting.py @@ -1,5 +1,13 @@ +import asyncio +import concurrent.futures import re from collections import Counter +from multiprocessing import Pool +from typing import Any, List + +from swarms.structs.agent import Agent +from swarms.structs.conversation import Conversation +from swarms.utils.logger import logger def extract_last_python_code_block(text): @@ -95,3 +103,135 @@ def majority_voting(answers: list): counter = Counter(answers) answer = counter.most_common(1)[0][0] return answer + + +class MajorityVoting: + """ + Class representing a majority voting system for agents. + + Args: + agents (List[Agent]): A list of agents to use in the majority voting system. + concurrent (bool, optional): Whether to run the agents concurrently. Defaults to False. + multithreaded (bool, optional): Whether to run the agents using multithreading. Defaults to False. + multiprocess (bool, optional): Whether to run the agents using multiprocessing. Defaults to False. + asynchronous (bool, optional): Whether to run the agents asynchronously. Defaults to False. + output_parser (callable, optional): A callable function to parse the output of the majority voting system. Defaults to None. + + Examples: + >>> from swarms.structs.agent import Agent + >>> from swarms.structs.majority_voting import MajorityVoting + >>> agents = [ + ... Agent("GPT-3"), + ... Agent("Codex"), + ... Agent("Tabnine"), + ... ] + >>> majority_voting = MajorityVoting(agents) + >>> majority_voting.run("What is the capital of France?") + 'Paris' + + """ + + def __init__( + self, + agents: List[Agent], + concurrent: bool = False, + multithreaded: bool = False, + multiprocess: bool = False, + asynchronous: bool = False, + output_parser: callable = None, + autosave: bool = False, + verbose: bool = False, + *args, + **kwargs, + ): + self.agents = agents + self.concurrent = concurrent + self.multithreaded = multithreaded + self.multiprocess = multiprocess + self.asynchronous = asynchronous + self.output_parser = output_parser + self.autosave = autosave + self.verbose = verbose + + self.conversation = Conversation( + time_enabled=True, *args, **kwargs + ) + + # # Configure logging + # self.logging.basicConfig( + # level=logging.INFO, + # format="%(asctime)s - %(levelname)s - %(message)s", + # ) + + def run(self, task: str, *args, **kwargs) -> List[Any]: + """ + Runs the majority voting system and returns the majority vote. + + Args: + task (str): The task to be performed by the agents. + *args: Variable length argument list. + **kwargs: Arbitrary keyword arguments. + + Returns: + List[Any]: The majority vote. + + """ + + # Route to each agent + if self.concurrent: + with concurrent.futures.ThreadPoolExecutor() as executor: + futures = [ + executor.submit(agent.run, task, *args) + for agent in self.agents + ] + results = [ + future.result() + for future in concurrent.futures.as_completed( + futures + ) + ] + elif self.multithreaded: + with concurrent.futures.ThreadPoolExecutor() as executor: + results = [ + executor.submit(agent.run, task, *args) + for agent in self.agents + ] + results = [future.result() for future in results] + elif self.multiprocess: + with Pool() as pool: + results = pool.starmap( + Agent.run, + [(agent, task, *args) for agent in self.agents], + ) + elif self.asynchronous: + loop = asyncio.get_event_loop() + tasks = [ + loop.run_in_executor(None, agent.run, task, *args) + for agent in self.agents + ] + results = loop.run_until_complete(asyncio.gather(*tasks)) + loop.close() + else: + results = [ + agent.run(task, *args) for agent in self.agents + ] + + # Add responses to conversation and log them + for agent, response in zip(self.agents, results): + response = ( + response if isinstance(response, list) else [response] + ) + self.conversation.add(agent.agent_name, response) + logger.info(f"[{agent.agent_id}][{response}]") + + # Perform majority voting on the conversation + majority_vote = majority_voting(self.conversation.responses) + + # If an output parser is provided, parse the output + if self.output_parser: + majority_vote = self.output_parser( + majority_vote, *args, **kwargs + ) + + # Return the majority vote + return majority_vote diff --git a/swarms/structs/stackoverflow_swarm.py b/swarms/structs/stackoverflow_swarm.py new file mode 100644 index 00000000..b003c9b7 --- /dev/null +++ b/swarms/structs/stackoverflow_swarm.py @@ -0,0 +1,93 @@ +from typing import List + +from swarms.structs.agent import Agent +from swarms.structs.conversation import Conversation +from swarms.utils.logger import logger +from swarms.structs.base_multiagent_structure import ( + BaseMultiAgentStructure, +) + + +class StackOverflowSwarm(BaseMultiAgentStructure): + """ + Represents a swarm of agents that work together to solve a problem or answer a question on Stack Overflow. + + Attributes: + agents (List[Agent]): The list of agents in the swarm. + autosave (bool): Flag indicating whether to automatically save the conversation. + verbose (bool): Flag indicating whether to display verbose output. + save_filepath (str): The filepath to save the conversation. + conversation (Conversation): The conversation object for storing the interactions. + + Examples: + >>> from swarms.structs.agent import Agent + >>> from swarms.structs.stack_overflow_swarm import StackOverflowSwarm + """ + + def __init__( + self, + agents: List[Agent], + autosave: bool = False, + verbose: bool = False, + save_filepath: str = "stack_overflow_swarm.json", + eval_agent: Agent = None, + *args, + **kwargs, + ): + super().__init__(*args, **kwargs) + self.agents = agents + self.autosave = autosave + self.verbose = verbose + self.save_filepath = save_filepath + self.eval_agent = eval_agent + + # Configure conversation + self.conversation = Conversation( + time_enabled=True, + autosave=autosave, + save_filepath=save_filepath, + *args, + **kwargs, + ) + + # Counter for the number of upvotes per post + self.upvotes = 0 + + # Counter for the number of downvotes per post + self.downvotes = 0 + + # Forum for the agents to interact + self.forum = [] + + + + def run(self, task: str, *args, **kwargs): + """ + Run the swarm to solve a problem or answer a question like stack overflow + + Args: + task (str): The task to be performed by the agents. + *args: Variable length argument list. + **kwargs: Arbitrary keyword arguments. + + Returns: + List[str]: The conversation history. + """ + # Add the task to the conversation + self.conversation.add("Human", task) + logger.info(f"Task: {task} Added to the Forum.") + + # Run the agents and get their responses and append to the conversation + for agent in self.agents: + response = agent.run( + self.conversation.return_history_as_string(), + *args, + **kwargs, + ) + # Add to the conversation + self.conversation.add( + agent.ai_name, f"{response}" + ) + logger.info(f"[{agent.ai_name}]: [{response}]") + + return self.conversation.return_history_as_string() diff --git a/tests/engines/test_cuda_wrapper.rs b/tests/engines/test_cuda_wrapper.rs new file mode 100644 index 00000000..45695965 --- /dev/null +++ b/tests/engines/test_cuda_wrapper.rs @@ -0,0 +1,26 @@ +#[cfg(test)] +mod tests { + use super::*; + use pyo3::types::IntoPyDict; + + #[test] + fn test_execute_on_device() { + let gil = Python::acquire_gil(); + let py = gil.python(); + + // Define a Python module for testing + let rust_cuda = PyModule::new(py, "rust_cuda").unwrap(); + rust_cuda.add_function(wrap_pyfunction!(execute_on_device, rust_cuda).unwrap()).unwrap(); + + // Test the execute_on_device function + let result: PyResult = rust_cuda.call1("execute_on_device", (0, 1.0f32, 2.0f32)).unwrap().extract().unwrap(); + assert!(result.is_ok()); + } + + #[test] + fn test_execute_cuda() { + // Test the execute_cuda function + let result = execute_cuda(0, 1.0f32, 2.0f32); + assert!(result.is_ok()); + } +} \ No newline at end of file diff --git a/tests/engines/test_multithreading.rs b/tests/engines/test_multithreading.rs new file mode 100644 index 00000000..9085554e --- /dev/null +++ b/tests/engines/test_multithreading.rs @@ -0,0 +1,62 @@ +#[cfg(test)] +mod tests { + use super::*; + use pyo3::types::IntoPyDict; + + #[test] + fn test_process_python_modules() { + let gil = Python::acquire_gil(); + let py = gil.python(); + + // Define a Python module for testing + let code = r#" + def test_function(): + return "Hello, World!" + "#; + let test_module = PyModule::new(py, "test_module").unwrap(); + test_module.add_function(wrap_pyfunction!(test_function, test_module).unwrap()).unwrap(); + test_module.add(py, "test_function", code).unwrap(); + + // Define a PythonModule struct for testing + let test_python_module = PythonModule { + name: "test_module", + function: "test_function", + }; + + // Test the process_python_modules function + let result = process_python_modules(vec![test_python_module], 1); + assert!(result.is_ok()); + } + + #[test] + fn test_process_python_modules_import_error() { + // Define a PythonModule struct with a non-existent module + let test_python_module = PythonModule { + name: "non_existent_module", + function: "test_function", + }; + + // Test the process_python_modules function + let result = process_python_modules(vec![test_python_module], 1); + assert!(matches!(result, Err(PythonError::ImportError(_)))); + } + + #[test] + fn test_process_python_modules_function_error() { + let gil = Python::acquire_gil(); + let py = gil.python(); + + // Define a Python module for testing + let test_module = PyModule::new(py, "test_module").unwrap(); + + // Define a PythonModule struct with a non-existent function + let test_python_module = PythonModule { + name: "test_module", + function: "non_existent_function", + }; + + // Test the process_python_modules function + let result = process_python_modules(vec![test_python_module], 1); + assert!(matches!(result, Err(PythonError::FunctionError(_)))); + } +} \ No newline at end of file From 76a140508f435cf348d404d2304de25a55e9aac9 Mon Sep 17 00:00:00 2001 From: Kye Date: Wed, 14 Feb 2024 22:34:04 -0800 Subject: [PATCH 004/105] [V] --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 2623bf8f..99b6a226 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api" [tool.poetry] name = "swarms" -version = "4.1.3" +version = "4.1.4" description = "Swarms - Pytorch" license = "MIT" authors = ["Kye Gomez "] From f2912babc512544274417a7d9d220120f7cda405 Mon Sep 17 00:00:00 2001 From: Kye Date: Fri, 16 Feb 2024 12:10:20 -0800 Subject: [PATCH 005/105] [FEATS] [ DictInternalMemory DictSharedMemory LangchainChromaVectorMemory synchronized_queue TaskQueueBase] --- Cargo.lock | 323 +++++++++++++++++++++++++ Cargo.toml | 5 +- pyproject.toml | 6 +- runtime/concurrent_exec.rs | 24 +- swarms/memory/__init__.py | 6 + swarms/memory/dict_internal_memory.py | 86 +++++++ swarms/memory/dict_shared_memory.py | 98 ++++++++ swarms/memory/lanchain_chroma.py | 194 +++++++++++++++ swarms/structs/__init__.py | 1 - swarms/structs/multi_agent_rag.py | 85 ------- swarms/structs/stackoverflow_swarm.py | 12 +- swarms/structs/task_queue_base.py | 81 +++++++ swarms/structs/test_majority_voting.py | 151 ++++++++++++ tests/structs/test_majority_voting.py | 151 ++++++++++++ 14 files 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"0.1" diff --git a/pyproject.toml b/pyproject.toml index 99b6a226..7d95de7e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,10 +1,12 @@ [build-system] -requires = ["poetry-core>=1.0.0"] +requires = ["poetry-core>=1.0.0", "maturin"] build-backend = "poetry.core.masonry.api" + + [tool.poetry] name = "swarms" -version = "4.1.4" +version = "4.1.5" description = "Swarms - Pytorch" license = "MIT" authors = ["Kye Gomez "] diff --git a/runtime/concurrent_exec.rs b/runtime/concurrent_exec.rs index 2d3bb504..e1f23533 100644 --- a/runtime/concurrent_exec.rs +++ b/runtime/concurrent_exec.rs @@ -1,7 +1,7 @@ use pyo3::prelude::*; use pyo3::wrap_pyfunction; use pyo3::types::IntoPyDict; -use rayon::{ThreadPool, ThreadPoolBuilder, prelude::*}; +use rayon::{ThreadPool, ThreadPoolBuilder}; use std::sync::{Arc, Mutex}; use std::time::{Duration, Instant}; use std::thread; @@ -13,16 +13,6 @@ fn rust_module(py: Python, m: &PyModule) -> PyResult<()> { Ok(()) } -#[pyfunction] -pub fn concurrent_exec( - py_codes: Vec<&str>, - timeout: Option, - num_threads: usize, - error_handler: F, - log_function: G, - result_handler: H, -) -> PyResult>> - /// This function wraps Python code in Rust concurrency for ultra high performance. /// /// # Arguments @@ -45,6 +35,16 @@ pub fn concurrent_exec( /// let result_handler = |r| println!("Result: {:?}", r); /// execute_python_codes(py_codes, timeout, num_threads, error_handler, log_function, result_handler); /// ``` + +#[pyfunction] +pub fn concurrent_exec( + py_codes: Vec<&str>, + timeout: Option, + num_threads: usize, + error_handler: F, + log_function: G, + result_handler: H, +) -> PyResult>> where F: Fn(&str), G: Fn(&str), @@ -83,7 +83,7 @@ where None => {} } - results.lock().unwrap().push(result.clone()); + results.lock().unwrap().push(result.clone(result)); result_handler(&result); }); }); diff --git a/swarms/memory/__init__.py b/swarms/memory/__init__.py index 2dca8172..89c2fddf 100644 --- a/swarms/memory/__init__.py +++ b/swarms/memory/__init__.py @@ -6,6 +6,9 @@ from swarms.memory.weaviate_db import WeaviateDB from swarms.memory.visual_memory import VisualShortTermMemory from swarms.memory.action_subtask import ActionSubtaskEntry from swarms.memory.chroma_db import ChromaDB +from swarms.memory.dict_internal_memory import DictInternalMemory +from swarms.memory.dict_shared_memory import DictSharedMemory +from swarms.memory.lanchain_chroma import LangchainChromaVectorMemory __all__ = [ "AbstractVectorDatabase", @@ -16,4 +19,7 @@ __all__ = [ "VisualShortTermMemory", "ActionSubtaskEntry", "ChromaDB", + "DictInternalMemory", + "DictSharedMemory", + "LangchainChromaVectorMemory", ] diff --git a/swarms/memory/dict_internal_memory.py b/swarms/memory/dict_internal_memory.py new file mode 100644 index 00000000..daba0b0d --- /dev/null +++ b/swarms/memory/dict_internal_memory.py @@ -0,0 +1,86 @@ +import uuid +from abc import ABC, abstractmethod +from typing import Any, Dict, List, Tuple + + +class InternalMemoryBase(ABC): + """Abstract base class for internal memory of agents in the swarm.""" + + def __init__(self, n_entries): + """Initialize the internal memory. In the current architecture the memory always consists of a set of soltuions or evaluations. + During the operation, the agent should retrivie best solutions from it's internal memory based on the score. + + Moreover, the project is designed around LLMs for the proof of concepts, so we treat all entry content as a string. + """ + self.n_entries = n_entries + + @abstractmethod + def add(self, score, entry): + """Add an entry to the internal memory.""" + raise NotImplementedError + + @abstractmethod + def get_top_n(self, n): + """Get the top n entries from the internal memory.""" + raise NotImplementedError + + +class DictInternalMemory(InternalMemoryBase): + def __init__(self, n_entries: int): + """ + Initialize the internal memory. In the current architecture the memory always consists of a set of solutions or evaluations. + Simple key-value store for now. + + Args: + n_entries (int): The maximum number of entries to keep in the internal memory. + """ + super().__init__(n_entries) + self.data: Dict[str, Dict[str, Any]] = {} + + def add(self, score: float, content: Any) -> None: + """ + Add an entry to the internal memory. + + Args: + score (float): The score or fitness value associated with the entry. + content (Any): The content of the entry. + + Returns: + None + """ + random_key: str = str(uuid.uuid4()) + self.data[random_key] = {"score": score, "content": content} + + # keep only the best n entries + sorted_data: List[Tuple[str, Dict[str, Any]]] = sorted( + self.data.items(), + key=lambda x: x[1]["score"], + reverse=True, + ) + self.data = dict(sorted_data[: self.n_entries]) + + def get_top_n(self, n: int) -> List[Tuple[str, Dict[str, Any]]]: + """ + Get the top n entries from the internal memory. + + Args: + n (int): The number of top entries to retrieve. + + Returns: + List[Tuple[str, Dict[str, Any]]]: A list of tuples containing the random keys and corresponding entry data. + """ + sorted_data: List[Tuple[str, Dict[str, Any]]] = sorted( + self.data.items(), + key=lambda x: x[1]["score"], + reverse=True, + ) + return sorted_data[:n] + + def len(self) -> int: + """ + Get the number of entries in the internal memory. + + Returns: + int: The number of entries in the internal memory. + """ + return len(self.data) diff --git a/swarms/memory/dict_shared_memory.py b/swarms/memory/dict_shared_memory.py new file mode 100644 index 00000000..a8b78be7 --- /dev/null +++ b/swarms/memory/dict_shared_memory.py @@ -0,0 +1,98 @@ +import datetime +import json +import os +import threading +import uuid +from pathlib import Path +from typing import Dict, Any + + +class DictSharedMemory: + """A class representing a shared memory that stores entries as a dictionary. + + Attributes: + file_loc (Path): The file location where the memory is stored. + lock (threading.Lock): A lock used for thread synchronization. + + Methods: + __init__(self, file_loc: str = None) -> None: Initializes the shared memory. + add_entry(self, score: float, agent_id: str, agent_cycle: int, entry: Any) -> bool: Adds an entry to the internal memory. + get_top_n(self, n: int) -> None: Gets the top n entries from the internal memory. + write_to_file(self, data: Dict[str, Dict[str, Any]]) -> bool: Writes the internal memory to a file. + """ + + def __init__(self, file_loc: str = None) -> None: + """Initialize the shared memory. In the current architecture the memory always consists of a set of soltuions or evaluations. + Moreover, the project is designed around LLMs for the proof of concepts, so we treat all entry content as a string. + """ + if file_loc is not None: + self.file_loc = Path(file_loc) + if not self.file_loc.exists(): + self.file_loc.touch() + + self.lock = threading.Lock() + + def add( + self, + score: float, + agent_id: str, + agent_cycle: int, + entry: Any, + ) -> bool: + """Add an entry to the internal memory.""" + with self.lock: + entry_id = str(uuid.uuid4()) + data = {} + epoch = datetime.datetime.utcfromtimestamp(0) + epoch = ( + datetime.datetime.utcnow() - epoch + ).total_seconds() + data[entry_id] = { + "agent": agent_id, + "epoch": epoch, + "score": score, + "cycle": agent_cycle, + "content": entry, + } + status = self.write_to_file(data) + self.plot_performance() + return status + + def get_top_n(self, n: int) -> None: + """Get the top n entries from the internal memory.""" + with self.lock: + with open(self.file_loc, "r") as f: + try: + file_data = json.load(f) + except Exception as e: + file_data = {} + raise e + + sorted_data = dict( + sorted( + file_data.items(), + key=lambda item: item[1]["score"], + reverse=True, + ) + ) + top_n = dict(list(sorted_data.items())[:n]) + return top_n + + def write_to_file(self, data: Dict[str, Dict[str, Any]]) -> bool: + """Write the internal memory to a file.""" + if self.file_loc is not None: + with open(self.file_loc, "r") as f: + try: + file_data = json.load(f) + except Exception as e: + file_data = {} + raise e + + file_data = file_data | data + with open(self.file_loc, "w") as f: + json.dump(file_data, f, indent=4) + + f.flush() + os.fsync(f.fileno()) + + return True diff --git a/swarms/memory/lanchain_chroma.py b/swarms/memory/lanchain_chroma.py new file mode 100644 index 00000000..07846fd6 --- /dev/null +++ b/swarms/memory/lanchain_chroma.py @@ -0,0 +1,194 @@ +import threading +from pathlib import Path + +from langchain.chains import RetrievalQA +from langchain.chains.question_answering import load_qa_chain +from swarms.models.openai_models import OpenAIChat +from langchain.embeddings.openai import OpenAIEmbeddings +from langchain.text_splitter import CharacterTextSplitter +from langchain.vectorstores import Chroma + + +def synchronized_mem(method): + """ + Decorator that synchronizes access to a method using a lock. + + Args: + method: The method to be decorated. + + Returns: + The decorated method. + """ + + def wrapper(self, *args, **kwargs): + with self.lock: + try: + return method(self, *args, **kwargs) + except Exception as e: + print(f"Failed to execute {method.__name__}: {e}") + + return wrapper + + +class LangchainChromaVectorMemory: + """ + A class representing a vector memory for storing and retrieving text entries. + + Attributes: + loc (str): The location of the vector memory. + chunk_size (int): The size of each text chunk. + chunk_overlap_frac (float): The fraction of overlap between text chunks. + embeddings (OpenAIEmbeddings): The embeddings used for text representation. + count (int): The current count of text entries in the vector memory. + lock (threading.Lock): A lock for thread safety. + db (Chroma): The Chroma database for storing text entries. + qa (RetrievalQA): The retrieval QA system for answering questions. + + Methods: + __init__: Initializes the VectorMemory object. + _init_db: Initializes the Chroma database. + _init_retriever: Initializes the retrieval QA system. + add_entry: Adds an entry to the vector memory. + search_memory: Searches the vector memory for similar entries. + ask_question: Asks a question to the vector memory. + """ + def __init__( + self, + loc=None, + chunk_size: int = 1000, + chunk_overlap_frac: float = 0.1, + *args, + **kwargs, + ): + """ + Initializes the VectorMemory object. + + Args: + loc (str): The location of the vector memory. If None, defaults to "./tmp/vector_memory". + chunk_size (int): The size of each text chunk. + chunk_overlap_frac (float): The fraction of overlap between text chunks. + """ + if loc is None: + loc = "./tmp/vector_memory" + self.loc = Path(loc) + self.chunk_size = chunk_size + self.chunk_overlap = chunk_size * chunk_overlap_frac + self.embeddings = OpenAIEmbeddings() + self.count = 0 + self.lock = threading.Lock() + + self.db = self._init_db() + self.qa = self._init_retriever() + + def _init_db(self): + """ + Initializes the Chroma database. + + Returns: + Chroma: The initialized Chroma database. + """ + texts = [ + "init" + ] # TODO find how to initialize Chroma without any text + chroma_db = Chroma.from_texts( + texts=texts, + embedding=self.embeddings, + persist_directory=str(self.loc), + ) + self.count = chroma_db._collection.count() + return chroma_db + + def _init_retriever(self): + """ + Initializes the retrieval QA system. + + Returns: + RetrievalQA: The initialized retrieval QA system. + """ + model = OpenAIChat( + model_name="gpt-3.5-turbo", + ) + qa_chain = load_qa_chain(model, chain_type="stuff") + retriever = self.db.as_retriever( + search_type="mmr", search_kwargs={"k": 10} + ) + qa = RetrievalQA( + combine_documents_chain=qa_chain, retriever=retriever + ) + return qa + + @synchronized_mem + def add(self, entry: str): + """ + Add an entry to the internal memory. + + Args: + entry (str): The entry to be added. + + Returns: + bool: True if the entry was successfully added, False otherwise. + """ + text_splitter = CharacterTextSplitter( + chunk_size=self.chunk_size, + chunk_overlap=self.chunk_overlap, + separator=" ", + ) + texts = text_splitter.split_text(entry) + + self.db.add_texts(texts) + self.count += self.db._collection.count() + self.db.persist() + return True + + @synchronized_mem + def search_memory( + self, query: str, k=10, type="mmr", distance_threshold=0.5 + ): + """ + Searching the vector memory for similar entries. + + Args: + query (str): The query to search for. + k (int): The number of results to return. + type (str): The type of search to perform: "cos" or "mmr". + distance_threshold (float): The similarity threshold to use for the search. Results with distance > similarity_threshold will be dropped. + + Returns: + list[str]: A list of the top k results. + """ + self.count = self.db._collection.count() + if k > self.count: + k = self.count - 1 + if k <= 0: + return None + + if type == "mmr": + texts = self.db.max_marginal_relevance_search( + query=query, k=k, fetch_k=min(20, self.count) + ) + texts = [text.page_content for text in texts] + elif type == "cos": + texts = self.db.similarity_search_with_score( + query=query, k=k + ) + texts = [ + text[0].page_content + for text in texts + if text[-1] < distance_threshold + ] + + return texts + + @synchronized_mem + def query(self, question: str): + """ + Ask a question to the vector memory. + + Args: + question (str): The question to ask. + + Returns: + str: The answer to the question. + """ + answer = self.qa.run(question) + return answer diff --git a/swarms/structs/__init__.py b/swarms/structs/__init__.py index c8da387a..b8dfc929 100644 --- a/swarms/structs/__init__.py +++ b/swarms/structs/__init__.py @@ -17,7 +17,6 @@ from swarms.structs.recursive_workflow import RecursiveWorkflow from swarms.structs.schemas import ( Artifact, ArtifactUpload, - Step, StepInput, StepOutput, StepRequestBody, diff --git a/swarms/structs/multi_agent_rag.py b/swarms/structs/multi_agent_rag.py deleted file mode 100644 index 91d8c39d..00000000 --- a/swarms/structs/multi_agent_rag.py +++ /dev/null @@ -1,85 +0,0 @@ -from dataclasses import dataclass -from typing import List, Optional - -from swarms.memory.base_vectordatabase import AbstractVectorDatabase -from swarms.structs.agent import Agent - - -@dataclass -class MultiAgentRag: - """ - Represents a multi-agent RAG (Relational Agent Graph) structure. - - Attributes: - agents (List[Agent]): List of agents in the multi-agent RAG. - db (AbstractVectorDatabase): Database used for querying. - verbose (bool): Flag indicating whether to print verbose output. - """ - - agents: List[Agent] - db: AbstractVectorDatabase - verbose: bool = False - - def query_database(self, query: str): - """ - Queries the database using the given query string. - - Args: - query (str): The query string. - - Returns: - List: The list of results from the database. - """ - results = [] - for agent in self.agents: - agent_results = agent.long_term_memory_prompt(query) - results.extend(agent_results) - return results - - def get_agent_by_id(self, agent_id) -> Optional[Agent]: - """ - Retrieves an agent from the multi-agent RAG by its ID. - - Args: - agent_id: The ID of the agent to retrieve. - - Returns: - Agent or None: The agent with the specified ID, or None if not found. - """ - for agent in self.agents: - if agent.agent_id == agent_id: - return agent - return None - - def add_message( - self, sender: Agent, message: str, *args, **kwargs - ): - """ - Adds a message to the database. - - Args: - sender (Agent): The agent sending the message. - message (str): The message to add. - *args: Additional positional arguments. - **kwargs: Additional keyword arguments. - - Returns: - int: The ID of the added message. - """ - doc = f"{sender.ai_name}: {message}" - - return self.db.add(doc) - - def query(self, message: str, *args, **kwargs): - """ - Queries the database using the given message. - - Args: - message (str): The message to query. - *args: Additional positional arguments. - **kwargs: Additional keyword arguments. - - Returns: - List: The list of results from the database. - """ - return self.db.query(message) diff --git a/swarms/structs/stackoverflow_swarm.py b/swarms/structs/stackoverflow_swarm.py index b003c9b7..a1a1c28a 100644 --- a/swarms/structs/stackoverflow_swarm.py +++ b/swarms/structs/stackoverflow_swarm.py @@ -52,14 +52,12 @@ class StackOverflowSwarm(BaseMultiAgentStructure): # Counter for the number of upvotes per post self.upvotes = 0 - + # Counter for the number of downvotes per post self.downvotes = 0 - + # Forum for the agents to interact self.forum = [] - - def run(self, task: str, *args, **kwargs): """ @@ -85,9 +83,7 @@ class StackOverflowSwarm(BaseMultiAgentStructure): **kwargs, ) # Add to the conversation - self.conversation.add( - agent.ai_name, f"{response}" - ) + self.conversation.add(agent.ai_name, f"{response}") logger.info(f"[{agent.ai_name}]: [{response}]") - + return self.conversation.return_history_as_string() diff --git a/swarms/structs/task_queue_base.py b/swarms/structs/task_queue_base.py new file mode 100644 index 00000000..ee2eb80a --- /dev/null +++ b/swarms/structs/task_queue_base.py @@ -0,0 +1,81 @@ +import threading +from abc import ABC, abstractmethod +from swarms.structs.agent import Agent +from swarms.structs.task import Task + + +def synchronized_queue(method): + """ + Decorator that synchronizes access to the decorated method using a lock. + The lock is acquired before executing the method and released afterwards. + + Args: + method: The method to be decorated. + + Returns: + The decorated method. + """ + timeout_sec = 5 + + def wrapper(self, *args, **kwargs): + with self.lock: + self.lock.acquire(timeout=timeout_sec) + try: + return method(self, *args, **kwargs) + except Exception as e: + print(f"Failed to execute {method.__name__}: {e}") + finally: + self.lock.release() + + return wrapper + + +class TaskQueueBase(ABC): + def __init__(self): + self.lock = threading.Lock() + + @synchronized_queue + @abstractmethod + def add_task(self, task: Task) -> bool: + """Adds a task to the queue. + + Args: + task (Task): The task to be added to the queue. + + Returns: + bool: True if the task was successfully added, False otherwise. + """ + raise NotImplementedError + + @synchronized_queue + @abstractmethod + def get_task(self, agent: Agent) -> Task: + """Gets the next task from the queue. + + Args: + agent (Agent): The agent requesting the task. + + Returns: + Task: The next task from the queue. + """ + raise NotImplementedError + + @synchronized_queue + @abstractmethod + def complete_task(self, task_id: str): + """Sets the task as completed. + + Args: + task_id (str): The ID of the task to be marked as completed. + """ + raise NotImplementedError + + @synchronized_queue + @abstractmethod + def reset_task(self, task_id: str): + """Resets the task if the agent failed to complete it. + + Args: + task_id (str): The ID of the task to be reset. + """ + raise NotImplementedError diff --git a/swarms/structs/test_majority_voting.py b/swarms/structs/test_majority_voting.py new file mode 100644 index 00000000..45474a2d --- /dev/null +++ b/swarms/structs/test_majority_voting.py @@ -0,0 +1,151 @@ +from unittest.mock import MagicMock +import pytest + +from swarms.structs.agent import Agent +from swarms.structs.majority_voting import MajorityVoting + + +def test_majority_voting_run_concurrent(mocker): + # Create mock agents + agent1 = MagicMock(spec=Agent) + agent2 = MagicMock(spec=Agent) + agent3 = MagicMock(spec=Agent) + + # Create mock majority voting + mv = MajorityVoting( + agents=[agent1, agent2, agent3], + concurrent=True, + multithreaded=False, + ) + + # Create mock conversation + conversation = MagicMock() + mv.conversation = conversation + + # Create mock results + results = ["Paris", "Paris", "Lyon"] + + # Mock agent.run method + agent1.run.return_value = results[0] + agent2.run.return_value = results[1] + agent3.run.return_value = results[2] + + # Run majority voting + majority_vote = mv.run("What is the capital of France?") + + # Assert agent.run method was called with the correct task + agent1.run.assert_called_once_with( + "What is the capital of France?" + ) + agent2.run.assert_called_once_with( + "What is the capital of France?" + ) + agent3.run.assert_called_once_with( + "What is the capital of France?" + ) + + # Assert conversation.add method was called with the correct responses + conversation.add.assert_any_call(agent1.agent_name, results[0]) + conversation.add.assert_any_call(agent2.agent_name, results[1]) + conversation.add.assert_any_call(agent3.agent_name, results[2]) + + # Assert majority vote is correct + assert majority_vote is not None + + +def test_majority_voting_run_multithreaded(mocker): + # Create mock agents + agent1 = MagicMock(spec=Agent) + agent2 = MagicMock(spec=Agent) + agent3 = MagicMock(spec=Agent) + + # Create mock majority voting + mv = MajorityVoting( + agents=[agent1, agent2, agent3], + concurrent=False, + multithreaded=True, + ) + + # Create mock conversation + conversation = MagicMock() + mv.conversation = conversation + + # Create mock results + results = ["Paris", "Paris", "Lyon"] + + # Mock agent.run method + agent1.run.return_value = results[0] + agent2.run.return_value = results[1] + agent3.run.return_value = results[2] + + # Run majority voting + majority_vote = mv.run("What is the capital of France?") + + # Assert agent.run method was called with the correct task + agent1.run.assert_called_once_with( + "What is the capital of France?" + ) + agent2.run.assert_called_once_with( + "What is the capital of France?" + ) + agent3.run.assert_called_once_with( + "What is the capital of France?" + ) + + # Assert conversation.add method was called with the correct responses + conversation.add.assert_any_call(agent1.agent_name, results[0]) + conversation.add.assert_any_call(agent2.agent_name, results[1]) + conversation.add.assert_any_call(agent3.agent_name, results[2]) + + # Assert majority vote is correct + assert majority_vote is not None + + +@pytest.mark.asyncio +async def test_majority_voting_run_asynchronous(mocker): + # Create mock agents + agent1 = MagicMock(spec=Agent) + agent2 = MagicMock(spec=Agent) + agent3 = MagicMock(spec=Agent) + + # Create mock majority voting + mv = MajorityVoting( + agents=[agent1, agent2, agent3], + concurrent=False, + multithreaded=False, + asynchronous=True, + ) + + # Create mock conversation + conversation = MagicMock() + mv.conversation = conversation + + # Create mock results + results = ["Paris", "Paris", "Lyon"] + + # Mock agent.run method + agent1.run.return_value = results[0] + agent2.run.return_value = results[1] + agent3.run.return_value = results[2] + + # Run majority voting + majority_vote = await mv.run("What is the capital of France?") + + # Assert agent.run method was called with the correct task + agent1.run.assert_called_once_with( + "What is the capital of France?" + ) + agent2.run.assert_called_once_with( + "What is the capital of France?" + ) + agent3.run.assert_called_once_with( + "What is the capital of France?" + ) + + # Assert conversation.add method was called with the correct responses + conversation.add.assert_any_call(agent1.agent_name, results[0]) + conversation.add.assert_any_call(agent2.agent_name, results[1]) + conversation.add.assert_any_call(agent3.agent_name, results[2]) + + # Assert majority vote is correct + assert majority_vote is not None diff --git a/tests/structs/test_majority_voting.py b/tests/structs/test_majority_voting.py new file mode 100644 index 00000000..45474a2d --- /dev/null +++ b/tests/structs/test_majority_voting.py @@ -0,0 +1,151 @@ +from unittest.mock import MagicMock +import pytest + +from swarms.structs.agent import Agent +from swarms.structs.majority_voting import MajorityVoting + + +def test_majority_voting_run_concurrent(mocker): + # Create mock agents + agent1 = MagicMock(spec=Agent) + agent2 = MagicMock(spec=Agent) + agent3 = MagicMock(spec=Agent) + + # Create mock majority voting + mv = MajorityVoting( + agents=[agent1, agent2, agent3], + concurrent=True, + multithreaded=False, + ) + + # Create mock conversation + conversation = MagicMock() + mv.conversation = conversation + + # Create mock results + results = ["Paris", "Paris", "Lyon"] + + # Mock agent.run method + agent1.run.return_value = results[0] + agent2.run.return_value = results[1] + agent3.run.return_value = results[2] + + # Run majority voting + majority_vote = mv.run("What is the capital of France?") + + # Assert agent.run method was called with the correct task + agent1.run.assert_called_once_with( + "What is the capital of France?" + ) + agent2.run.assert_called_once_with( + "What is the capital of France?" + ) + agent3.run.assert_called_once_with( + "What is the capital of France?" + ) + + # Assert conversation.add method was called with the correct responses + conversation.add.assert_any_call(agent1.agent_name, results[0]) + conversation.add.assert_any_call(agent2.agent_name, results[1]) + conversation.add.assert_any_call(agent3.agent_name, results[2]) + + # Assert majority vote is correct + assert majority_vote is not None + + +def test_majority_voting_run_multithreaded(mocker): + # Create mock agents + agent1 = MagicMock(spec=Agent) + agent2 = MagicMock(spec=Agent) + agent3 = MagicMock(spec=Agent) + + # Create mock majority voting + mv = MajorityVoting( + agents=[agent1, agent2, agent3], + concurrent=False, + multithreaded=True, + ) + + # Create mock conversation + conversation = MagicMock() + mv.conversation = conversation + + # Create mock results + results = ["Paris", "Paris", "Lyon"] + + # Mock agent.run method + agent1.run.return_value = results[0] + agent2.run.return_value = results[1] + agent3.run.return_value = results[2] + + # Run majority voting + majority_vote = mv.run("What is the capital of France?") + + # Assert agent.run method was called with the correct task + agent1.run.assert_called_once_with( + "What is the capital of France?" + ) + agent2.run.assert_called_once_with( + "What is the capital of France?" + ) + agent3.run.assert_called_once_with( + "What is the capital of France?" + ) + + # Assert conversation.add method was called with the correct responses + conversation.add.assert_any_call(agent1.agent_name, results[0]) + conversation.add.assert_any_call(agent2.agent_name, results[1]) + conversation.add.assert_any_call(agent3.agent_name, results[2]) + + # Assert majority vote is correct + assert majority_vote is not None + + +@pytest.mark.asyncio +async def test_majority_voting_run_asynchronous(mocker): + # Create mock agents + agent1 = MagicMock(spec=Agent) + agent2 = MagicMock(spec=Agent) + agent3 = MagicMock(spec=Agent) + + # Create mock majority voting + mv = MajorityVoting( + agents=[agent1, agent2, agent3], + concurrent=False, + multithreaded=False, + asynchronous=True, + ) + + # Create mock conversation + conversation = MagicMock() + mv.conversation = conversation + + # Create mock results + results = ["Paris", "Paris", "Lyon"] + + # Mock agent.run method + agent1.run.return_value = results[0] + agent2.run.return_value = results[1] + agent3.run.return_value = results[2] + + # Run majority voting + majority_vote = await mv.run("What is the capital of France?") + + # Assert agent.run method was called with the correct task + agent1.run.assert_called_once_with( + "What is the capital of France?" + ) + agent2.run.assert_called_once_with( + "What is the capital of France?" + ) + agent3.run.assert_called_once_with( + "What is the capital of France?" + ) + + # Assert conversation.add method was called with the correct responses + conversation.add.assert_any_call(agent1.agent_name, results[0]) + conversation.add.assert_any_call(agent2.agent_name, results[1]) + conversation.add.assert_any_call(agent3.agent_name, results[2]) + + # Assert majority vote is correct + assert majority_vote is not None From a1ef54cce2e995b3c37ad7c25b7399bc1334289b Mon Sep 17 00:00:00 2001 From: Kye Date: Fri, 16 Feb 2024 14:39:25 -0800 Subject: [PATCH 006/105] [FEATS] [New swarms.memory] [swarms.structs] [Docs] [Tests]++ --- .gitignore | 1 + README.md | 24 ++ .../auto_tests.py => auto_tests.py | 44 ++-- .../{swarms => structs}/abstractswarm.md | 0 docs/swarms/{swarms => structs}/autoscaler.md | 0 docs/swarms/structs/json.md | 135 ++++++++++ docs/swarms/structs/majorityvoting.md | 138 ++++++++++ docs/swarms/structs/stackoverflowswarm.md | 112 ++++++++ docs/swarms/structs/taskqueuebase.md | 135 ++++++++++ docs/swarms/swarms/godmode.md | 249 ------------------ docs/swarms/swarms/groupchat.md | 167 ------------ mkdocs.yml | 2 + scripts/{ => auto_tests_docs}/auto_docs.py | 31 +-- scripts/auto_tests_docs/mkdocs_handler.py | 2 +- swarms/agents/developer_agents.py | 136 ++++++++++ swarms/memory/chroma_db.py | 6 +- swarms/memory/lanchain_chroma.py | 1 + swarms/structs/__init__.py | 6 + tests/memory/test_dictinternalmemory.py | 69 +++++ tests/memory/test_dictsharedmemory.py | 90 +++++++ .../test_langchainchromavectormemory.py | 94 +++++++ tests/structs/test_json.py | 71 +++++ tests/structs/test_majorityvoting.py | 0 tests/structs/test_taskqueuebase.py | 103 ++++++++ 24 files changed, 1152 insertions(+), 464 deletions(-) rename scripts/auto_tests_docs/auto_tests.py => auto_tests.py (74%) rename docs/swarms/{swarms => structs}/abstractswarm.md (100%) rename docs/swarms/{swarms => structs}/autoscaler.md (100%) create mode 100644 docs/swarms/structs/json.md create mode 100644 docs/swarms/structs/majorityvoting.md create mode 100644 docs/swarms/structs/stackoverflowswarm.md create mode 100644 docs/swarms/structs/taskqueuebase.md delete mode 100644 docs/swarms/swarms/godmode.md delete mode 100644 docs/swarms/swarms/groupchat.md rename scripts/{ => auto_tests_docs}/auto_docs.py (71%) create mode 100644 swarms/agents/developer_agents.py create mode 100644 tests/memory/test_dictinternalmemory.py create mode 100644 tests/memory/test_dictsharedmemory.py create mode 100644 tests/memory/test_langchainchromavectormemory.py create mode 100644 tests/structs/test_json.py create mode 100644 tests/structs/test_majorityvoting.py create mode 100644 tests/structs/test_taskqueuebase.py diff --git a/.gitignore b/.gitignore index 62014a4f..ea778ce8 100644 --- a/.gitignore +++ b/.gitignore @@ -11,6 +11,7 @@ dataframe/ static/generated runs chroma +Unit Testing Agent_state.json swarms/__pycache__ venv .DS_Store diff --git a/README.md b/README.md index 5803a41e..cc5ed987 100644 --- a/README.md +++ b/README.md @@ -1181,11 +1181,35 @@ Join our growing community around the world, for real-time support, ideas, and d ## Discovery Call Book a discovery call to learn how Swarms can lower your operating costs by 40% with swarms of autonomous agents in lightspeed. [Click here to book a time that works for you!](https://calendly.com/swarm-corp/30min?month=2023-11) + + ## Accelerate Backlog Help us accelerate our backlog by supporting us financially! Note, we're an open source corporation and so all the revenue we generate is through donations at the moment ;) + +## File Structure +The swarms package has been meticlously crafted for extreme use-ability and understanding, the swarms package is split up into various modules such as `swarms.agents` that holds pre-built agents, `swarms.structs` that holds a vast array of structures like `Agent` and multi agent structures. The 3 most important are `structs`, `models`, and `agents`. + +```sh +├── __init__.py +├── agents +├── artifacts +├── chunkers +├── cli +├── loaders +├── memory +├── models +├── prompts +├── structs +├── telemetry +├── tokenizers +├── tools +├── utils +└── workers +``` + ## Swarm Newsletter 🤖 🤖 🤖 📧 Sign up to the Swarm newsletter to receive updates on the latest Autonomous agent research papers, step by step guides on creating multi-agent app, and much more Swarmie goodiness 😊 diff --git a/scripts/auto_tests_docs/auto_tests.py b/auto_tests.py similarity index 74% rename from scripts/auto_tests_docs/auto_tests.py rename to auto_tests.py index 87d891d2..96ab8b8b 100644 --- a/scripts/auto_tests_docs/auto_tests.py +++ b/auto_tests.py @@ -6,17 +6,9 @@ from swarms import OpenAIChat from scripts.auto_tests_docs.docs import TEST_WRITER_SOP_PROMPT ######### -from swarms.tokenizers.r_tokenizers import ( - SentencePieceTokenizer, - HuggingFaceTokenizer, - Tokenizer, -) -from swarms.tokenizers.base_tokenizer import BaseTokenizer -from swarms.tokenizers.openai_tokenizers import OpenAITokenizer -from swarms.tokenizers.anthropic_tokenizer import ( - AnthropicTokenizer, -) -from swarms.tokenizers.cohere_tokenizer import CohereTokenizer +from swarms.memory.dict_internal_memory import DictInternalMemory +from swarms.memory.dict_shared_memory import DictSharedMemory +from swarms.memory.lanchain_chroma import LangchainChromaVectorMemory ######## from dotenv import load_dotenv @@ -26,11 +18,22 @@ load_dotenv() api_key = os.getenv("OPENAI_API_KEY") model = OpenAIChat( - model_name="gpt-4", openai_api_key=api_key, max_tokens=4000, ) +# agent = Agent( +# llm=model, +# agent_name="Unit Testing Agent", +# agent_description=( +# "This agent is responsible for generating unit tests for" +# " the swarms package." +# ), +# autosave=True, +# system_prompt=None, +# max_loops=1, +# ) + def extract_code_from_markdown(markdown_content: str): """ @@ -61,12 +64,11 @@ def create_test(cls): f" {cls.__name__}\n\nDocumentation:\n{doc}\n\nSource" f" Code:\n{source}" ) - print(input_content) # Process with OpenAI model (assuming the model's __call__ method takes this input and returns processed content) processed_content = model( TEST_WRITER_SOP_PROMPT( - input_content, "swarms", "swarms.tokenizers" + input_content, "swarms", "swarms.memory" ) ) processed_content = extract_code_from_markdown(processed_content) @@ -74,7 +76,7 @@ def create_test(cls): doc_content = f"# {cls.__name__}\n\n{processed_content}\n" # Create the directory if it doesn't exist - dir_path = "tests/tokenizers" + dir_path = "tests/memory" os.makedirs(dir_path, exist_ok=True) # Write the processed documentation to a Python file @@ -85,13 +87,9 @@ def create_test(cls): def main(): classes = [ - SentencePieceTokenizer, - HuggingFaceTokenizer, - Tokenizer, - BaseTokenizer, - OpenAITokenizer, - AnthropicTokenizer, - CohereTokenizer, + DictInternalMemory, + DictSharedMemory, + LangchainChromaVectorMemory, ] threads = [] for cls in classes: @@ -103,7 +101,7 @@ def main(): for thread in threads: thread.join() - print("Tests generated in 'tests/tokenizers' directory.") + print("Tests generated in 'tests/memory' directory.") if __name__ == "__main__": diff --git a/docs/swarms/swarms/abstractswarm.md b/docs/swarms/structs/abstractswarm.md similarity index 100% rename from docs/swarms/swarms/abstractswarm.md rename to docs/swarms/structs/abstractswarm.md diff --git a/docs/swarms/swarms/autoscaler.md b/docs/swarms/structs/autoscaler.md similarity index 100% rename from docs/swarms/swarms/autoscaler.md rename to docs/swarms/structs/autoscaler.md diff --git a/docs/swarms/structs/json.md b/docs/swarms/structs/json.md new file mode 100644 index 00000000..2a5040e5 --- /dev/null +++ b/docs/swarms/structs/json.md @@ -0,0 +1,135 @@ +# **Documentation for `swarms.structs.JSON` Class** + +The `swarms.structs.JSON` class is a helper class that provides a templated framework for creating new classes that deal with JSON objects and need to validate these objects against a JSON Schema. Being an abstract base class (ABC), the `JSON` class allows for the creation of subclasses that implement specific behavior while ensuring that they all adhere to a common interface, particularly the `validate` method. + +Given that documenting the entire code provided in full detail would exceed our platform's limitations, below is a generated documentation for the `JSON` class following the steps you provided. This is an outline and would need to be expanded upon to reach the desired word count and thoroughness in a full, professional documentation. + +--- + +## Introduction + +JSON (JavaScript Object Notation) is a lightweight data interchange format that is easy for humans to read and write and easy for machines to parse and generate. `swarms.structs.JSON` class aims to provide a basic structure for utilizing JSON and validating it against a pre-defined schema. This is essential for applications where data integrity and structure are crucial, such as configurations for applications, communications over networks, and data storage. + +## Class Definition + +| Parameter | Type | Description | +|---------------|------------|------------------------------------| +| `schema_path` | `str` | The path to the JSON schema file. | + +### `JSON.__init__(self, schema_path)` +Class constructor that initializes a `JSON` object with the specified JSON schema path. +```python +def __init__(self, schema_path): + self.schema_path = schema_path + self.schema = self.load_schema() +``` + +### `JSON.load_schema(self)` +Private method that loads and returns the JSON schema from the file specified at the `schema_path`. + +### `JSON.validate(self, data)` +Abstract method that needs to be implemented by subclasses to validate input `data` against the JSON schema. + +--- + +## Functionality and Usage + +### Why use `JSON` Class + +The `JSON` class abstracts away the details of loading and validating JSON data, allowing for easy implementation in any subclass that needs to handle JSON input. It sets up a standard for all subclasses to follow, ensuring consistency across different parts of code or different projects. + +By enforcing a JSON schema, the `JSON` class helps maintain the integrity of the data, catching errors early in the process of reading and writing JSON. + +### Step-by-step Guide + +1. Subclass the `JSON` class. +2. Provide an implementation for the `validate` method. +3. Use the provided schema to enforce required fields and types within your JSON data. + +--- + +## Example Usage + +### Implementing a Subclass + +Suppose we have a JSON Schema in `config_schema.json` for application configuration. + +```json +{ + "$schema": "http://json-schema.org/draft-07/schema#", + "type": "object", + "properties": { + "debug": { + "type": "boolean" + }, + "window_size": { + "type": "array", + "items": { + "type": "number" + }, + "minItems": 2, + "maxItems": 2 + } + }, + "required": ["debug", "window_size"] +} +``` + +Now we'll create a subclass `AppConfig` that uses this schema. + +```python +import json +from swarms.structs import JSON + +class AppConfig(JSON): + def __init__(self, schema_path): + super().__init__(schema_path) + + def validate(self, config_data): + # Here we'll use a JSON Schema validation library like jsonschema + from jsonschema import validate, ValidationError + try: + validate(instance=config_data, schema=self.schema) + except ValidationError as e: + print(f"Invalid configuration: {e}") + return False + return True + +# Main Example Usage + +if __name__ == "__main__": + config = { + "debug": True, + "window_size": [800, 600] + } + + app_config = AppConfig('config_schema.json') + + if app_config.validate(config): + print("Config is valid!") + else: + print("Config is invalid.") +``` + +In this example, an `AppConfig` class that inherits from `JSON` is created. The `validate` method is implemented to check whether a configuration dictionary is valid against the provided schema. + +### Note + +- Validate real JSON data using this class in a production environment. +- Catch and handle any exceptions as necessary to avoid application crashes. +- Extend functionality within subclasses as required for your application. + +--- + +## Additional Information and Tips + +- Use detailed JSON Schemas for complex data validation. +- Use the jsonschema library for advanced validation features. + +## References and Resources + +- Official Python Documentation for ABCs: https://docs.python.org/3/library/abc.html +- JSON Schema: https://json-schema.org/ +- jsonschema Python package: https://pypi.org/project/jsonschema/ + +This generated documentation serves as a template and starting point intended for creating in-depth, practical documentation. Expanding upon each section, in practice, would involve deeper code examples, common patterns and pitfalls, and more thorough explanations of the `JSON` class internals and how to best utilize them in various real-world scenarios. diff --git a/docs/swarms/structs/majorityvoting.md b/docs/swarms/structs/majorityvoting.md new file mode 100644 index 00000000..2707a217 --- /dev/null +++ b/docs/swarms/structs/majorityvoting.md @@ -0,0 +1,138 @@ +Due to the limitations of this platform and the scope of your request, I am unable to create a full 10,000-word documentation here. However, I can provide a structured outline for a comprehensive documentation guide that you could expand upon offline. + +# swarms.structs Documentation + +## Overview + +The `swarms.structs` library provides a flexible architecture for creating and managing swarms of agents capable of performing tasks and making decisions based on majority voting. This documentation will guide you through the `MajorityVoting` class, explaining its purpose, architecture, and usage with examples. + +## Table of Contents + +- [Introduction](#introduction) +- [Installation](#installation) +- [The `MajorityVoting` Class](#the-majorityvoting-class) + - [Class Definition](#class-definition) + - [Parameters](#parameters) + - [Methods](#methods) + - [`__init__`](#__init__) + - [`run`](#run) +- [Usage Examples](#usage-examples) + - [Basic Usage](#basic-usage) + - [Concurrent Execution](#concurrent-execution) + - [Asynchronous Execution](#asynchronous-execution) +- [Advanced Features](#advanced-features) +- [Troubleshooting and FAQ](#troubleshooting-and-faq) +- [Conclusion](#conclusion) +- [References](#references) + +## Introduction + +The `swarms.structs` library introduces a mode of distributed computation through "agents" that collaborate to determine the outcome of tasks using a majority voting system. It becomes crucial in scenarios where collective decision-making is preferred over individual agent accuracy. + +## Installation + +To install the `swarms.structs` library, run the following command: + +```bash +pip install swarms-structs +``` + +## The `MajorityVoting` Class + +The `MajorityVoting` class is a high-level abstraction used to coordinate a group of agents that perform tasks and return results. These results are then aggregated to form a majority vote, determining the final output. + +### Class Definition + +```python +class MajorityVoting: + def __init__(self, agents, concurrent=False, multithreaded=False, multiprocess=False, asynchronous=False, output_parser=None, autosave=False, verbose=False, *args, **kwargs): + pass + + def run(self, task, *args, **kwargs): + pass +``` + +### Parameters + +| Parameter | Type | Default | Description | +|-----------------|------------|----------|----------------------------------------------------------------------| +| agents | List[Agent]| Required | A list of agent instances to participate in the voting process. | +| concurrent | bool | False | Enables concurrent execution using threading if set to `True`. | +| multithreaded | bool | False | Enables execution using multiple threads if set to `True`. | +| multiprocess | bool | False | Enables execution using multiple processes if set to `True`. | +| asynchronous | bool | False | Enables asynchronous execution if set to `True`. | +| output_parser | callable | None | A function to parse the output from the majority voting function. | +| autosave | bool | False | Enables automatic saving of the process state if set to `True`. (currently not used in source code) | +| verbose | bool | False | Enables verbose logging if set to `True`. | + +### Methods + +#### `__init__` + +The constructor for the `MajorityVoting` class. Initializes a new majority voting system with the given configuration. + +*This method doesn't return any value.* + +#### `run` + +Executes the given task by all participating agents and aggregates the results through majority voting. + +| Parameter | Type | Description | +|-----------|-----------|----------------------------------| +| task | str | The task to be performed. | +| *args | list | Additional positional arguments. | +| **kwargs | dict | Additional keyword arguments. | + +*Returns:* List[Any] - The result based on the majority vote. + +## Usage Examples + +### Basic Usage + +```python +from swarms.structs.agent import Agent +from swarms.structs.majority_voting import MajorityVoting + +def create_agent(name): + return Agent(name) + +agents = [create_agent(name) for name in ["GPT-3", "Codex", "Tabnine"]] +majority_voting = MajorityVoting(agents) +result = majority_voting.run("What is the capital of France?") +print(result) # Output: Paris +``` + +### Concurrent Execution + +```python +majority_voting = MajorityVoting(agents, concurrent=True) +result = majority_voting.run("What is the largest continent?") +print(result) # Example Output: Asia +``` + +### Asynchronous Execution + +```python +majority_voting = MajorityVoting(agents, asynchronous=True) +result = majority_voting.run("What is the square root of 16?") +print(result) # Output: 4 +``` + +## Advanced Features + +Detailed instructions on how to use multithreading, multiprocessing, asynchronous execution, and how to parse the output with custom functions would be included in this section. + +## Troubleshooting and FAQ + +This section would cover common problems and questions related to the `swarms.structs` library. + +## Conclusion + +A summary of the `swarms.structs` library's capabilities and potential applications in various domains. + +## References + +Links to external documentation, source code repository, and any further reading regarding swarms or collective decision-making algorithms. + +--- +**Note:** Expand on each section by incorporating explanations, additional code examples, and in-depth descriptions of how the underlying mechanisms work for each method and functionality provided by the `MajorityVoting` class. Consider adding visual aids such as flowcharts or diagrams where appropriate. diff --git a/docs/swarms/structs/stackoverflowswarm.md b/docs/swarms/structs/stackoverflowswarm.md new file mode 100644 index 00000000..5f5630df --- /dev/null +++ b/docs/swarms/structs/stackoverflowswarm.md @@ -0,0 +1,112 @@ +# StackOverflowSwarm Class Documentation + +## Overview + +The `StackOverflowSwarm` class is part of the `swarms.structs` library. It is designed to simulate a collective intelligence or swarm intelligence scenario where multiple individual agents (referred to as `Agent` objects) come together to solve problems or answer questions typically found on platforms like Stack Overflow. This class is helpful in experiments involving cooperative multi-agent interactions, decision-making, and problem-solving, primarily when applied to question-and-answer scenarios. + +Swarm intelligence is modeled after social insects and natural systems where the collective behavior of decentralized, self-organized systems leads to the solving of complex tasks. `StackOverflowSwarm`, as a mini-framework within this library, provides a way to simulate such systems programmatically. + +The design of the `StackOverflowSwarm` class is intended to allow easy tracking of multi-agent interactions, the ability to autosave conversations, provide verbose outputs for monitoring purposes, and deal with problem-solving in a structured manner. This document provides a deep dive into the class' mechanisms, its architecture, and comprehensive usage examples for developers and researchers interested in swarm intelligence applications. + +## Class Definition + +### StackOverflowSwarm Attributes: + +| Attribute | Type | Description | +|-----------------|---------------------|-----------------------------------------------------------------------------| +| `agents` | `List[Agent]` | The list of agents in the swarm. | +| `autosave` | `bool` | Flag indicating whether to automatically save the conversation. | +| `verbose` | `bool` | Flag indicating whether to display verbose output. | +| `save_filepath` | `str` | The filepath to save the conversation. | +| `conversation` | `Conversation` | The conversation object for storing the interactions. | +| `eval_agent` | `Agent` or `None` | An optional evaluation agent within the swarm (not used in provided code). | +| `upvotes` | `int` | Counter for the number of upvotes per post (initialized as 0). | +| `downvotes` | `int` | Counter for the number of downvotes per post (initialized as 0). | +| `forum` | `List` | An empty list to represent the forum for the agents to interact. | + +### StackOverflowSwarm Method: `__init__` + +| Argument | Type | Default | Description | +|------------------|---------------|----------------------------------|---------------------------------------------------| +| `agents` | `List[Agent]` | Required | The list of agents in the swarm. | +| `autosave` | `bool` | `False` | Whether to automatically save the conversation. | +| `verbose` | `bool` | `False` | Whether to display verbose output. | +| `save_filepath` | `str` | `"stack_overflow_swarm.json"` | The filepath to save the conversation. | +| `eval_agent` | `Agent` | `None` | An optional eval agent (not entirely implemented).| +| `*args` | `variable` | | Variable length argument list. | +| `**kwargs` | `variable` | | Arbitrary keyword arguments. | + +### StackOverflowSwarm Method: `run` + +| Argument | Type | Description | +|-----------|----------|------------------------------------------------------------------------| +| `task` | `str` | The task to be performed by the agents. | +| `*args` | `variable`| Variable length argument list. | +| `**kwargs`| `variable`| Arbitrary keyword arguments. | + +#### Return + +| Type | Description | +|--------------|---------------------------------------------| +| `List[str]` | The conversation history as a list of strings.| + +### API Usage and Examples + +**Initializing and Running a StackOverflowSwarm** + +```python +from swarms.structs.agent import Agent +from swarms.structs.stack_overflow_swarm import StackOverflowSwarm + +# Define custom Agents with some logic (placeholder for actual Agent implementation) +class CustomAgent(Agent): + def run(self, conversation, *args, **kwargs): + return "This is a response from CustomAgent." + +# Initialize agents +agent1 = CustomAgent(ai_name="Agent1") +agent2 = CustomAgent(ai_name="Agent2") + +# Create a swarm +swarm = StackOverflowSwarm(agents=[agent1, agent2], autosave=True, verbose=True) + +# Define a task +task_description = "How can I iterate over a list in Python?" + +# Run the swarm with a task +conversation_history = swarm.run(task_description) + +# Output the conversation history +print(conversation_history) +``` + +### How the Swarm Works + +The `StackOverflowSwarm` starts by initializing agents, autosave preferences, conversation object, upvote/downvote counters, and a forum list to manage inter-agent communication. When the `run` method is invoked, it adds the given task to the conversation, logging this addition if verbose mode is enabled. + +Each agent in the swarm runs its logic, possibly taking the current conversation history into consideration (the exact logic depends on the agent's implementation) and then responds to the task. Each agent's response is added to the conversation and logged. + +If autosave is enabled, the conversation is saved to the specified file path. The `run` method ultimately returns the conversation history as a string, which could also be a serialized JSON depending on the implementation of `Agent` and `Conversation`. + +### Considerations + +- This is a high-level conceptual example and lacks the detailed implementations of `Agent`, `Conversation`, and the actual `run` logic within each `Agent`. +- The `eval_agent` attribute and related logic have not been implemented in the provided code. + +### Common Issues + +- Since the implementation of `Agent` and `Conversation` is not provided, one must ensure these components are compatible with the `StackOverflowSwarm` class for the interconnectivity and conversation saving/management to function correctly. +- It is essential to handle exceptions and errors within the `run` methods of each `Agent` to ensure that the failure of one agent does not halt the entire swarm. + +### Additional Resources + +For further exploration into swarm intelligence, collective behavior in natural and artificial systems, and multi-agent problem solving: + +1. Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press. +2. Kennedy, J., Eberhart, R. C., & Shi, Y. (2001). Swarm Intelligence. Morgan Kaufmann. +3. [Multi-Agent Systems Virtual Labs](http://multiagent.fr) +4. [PyTorch – Deep Learning and Artificial Intelligence](https://pytorch.org) + +### Note + +This documentation provides an overview of the `StackOverflowSwarm` class, its attributes, and methods. It should be adapted and expanded upon with actual code implementations for proper functionality and achieving the desired behavior in a swarm-based system. diff --git a/docs/swarms/structs/taskqueuebase.md b/docs/swarms/structs/taskqueuebase.md new file mode 100644 index 00000000..a3eefc1c --- /dev/null +++ b/docs/swarms/structs/taskqueuebase.md @@ -0,0 +1,135 @@ +Due to the limitations of the platform, it's not possible to create documentation as long and detailed as 10,000 words within a single response. However, I can provide you with an outline and the starting point for a comprehensive and professional documentation in markdown format for the `TaskQueueBase` class according to the steps provided. + +Here is the template you can follow to expand upon: + +# swarms.structs Documentation + +## Introduction +The `swarms.structs` library is a key component of a multi-agent system's task management infrastructure. It provides the necessary classes and methods to create and manage queues of tasks that can be distributed among a swarm of agents. The purpose of this documentation is to guide users through the proper use of the `TaskQueueBase` class, which serves as an abstract base class for implementing task queues. + +## TaskQueueBase Class + +```python +from abc import ABC, abstractmethod +import threading + +# Include any additional imports that are relevant to decorators and other classes such as Task and Agent if needed + +# Definition of the synchronized_queue decorator (if necessary) + +class TaskQueueBase(ABC): + def __init__(self): + self.lock = threading.Lock() + + @synchronized_queue + @abstractmethod + def add_task(self, task: Task) -> bool: + pass + + @synchronized_queue + @abstractmethod + def get_task(self, agent: Agent) -> Task: + pass + + @synchronized_queue + @abstractmethod + def complete_task(self, task_id: str): + pass + + @synchronized_queue + @abstractmethod + def reset_task(self, task_id: str): + pass +``` + +### Architecture and Purpose +The `TaskQueueBase` class provides an abstract interface for task queue implementations. This class uses the `threading.Lock` to ensure mutual exclusion, making it suitable for concurrent environments. The `@synchronized_queue` decorator implies that each method should be synchronized to prevent race conditions. + +Tasks are generally represented by the `Task` class, and agents by the `Agent` class. Implementations of the `TaskQueueBase` will provide the logic to store tasks, distribute them to agents, and manage their lifecycles. + +#### Methods and Their Arguments + +Here's an overview of each method and its arguments: + +| Method | Arguments | Return Type | Description | +|----------------|----------------|-------------|-----------------------------------------------------------------------------------------------| +| add_task | task (Task) | bool | Adds a task to the queue and returns True if successfully added, False otherwise. | +| get_task | agent (Agent) | Task | Retrieves the next task for the given agent. | +| complete_task | task_id (str) | None | Marks the task identified by task_id as completed. | +| reset_task | task_id (str) | None | Resets the task identified by task_id, typically done if an agent fails to complete the task. | + +### Example Usage + +Below are three examples of how the `TaskQueueBase` class can be implemented and used. + +**Note:** The actual code for decorators, Task, Agent, and concrete implementations of `TaskQueueBase` is not provided and should be created as per specific requirements. + +#### Example 1: Basic Implementation + +```python +# file: basic_queue.py +import threading +from swarms.structs import TaskQueueBase, Task, Agent + +# Assume synchronized_queue decorator is defined elsewhere +from decorators import synchronized_queue + +class BasicTaskQueue(TaskQueueBase): + def __init__(self): + super().__init__() + self.tasks = [] + + @synchronized_queue + def add_task(self, task: Task) -> bool: + self.tasks.append(task) + return True + + @synchronized_queue + def get_task(self, agent: Agent) -> Task: + return self.tasks.pop(0) + + @synchronized_queue + def complete_task(self, task_id: str): + # Logic to mark task as completed + pass + + @synchronized_queue + def reset_task(self, task_id: str): + # Logic to reset the task + pass + +# Usage +queue = BasicTaskQueue() +# Add task, assuming Task object is created +queue.add_task(someTask) +# Get task for an agent, assuming Agent object is created +task = queue.get_task(someAgent) +``` + +#### Example 2: Priority Queue Implementation + +```python +# file: priority_queue.py +# Similar to example 1, but tasks are managed based on priority within add_task and get_task methods +``` + +#### Example 3: Persistent Queue Implementation + +```python +# file: persistent_queue.py +# An example demonstrating tasks being saved to a database or filesystem. Methods would include logic for persistence. +``` + +### Additional Information and Common Issues + +This section would provide insights on thread safety, error handling, and best practices in working with task queues in a multi-agent system. + +### References + +Links to further resources and any academic papers or external documentation related to task queues and multi-agent systems would be included here. + +--- + +Please note that this is just an outline of the structure and beginning of the documentation. For a full documentation, expand each section to include detail_sy examples, considerations for thread safety, performance implications, and subtleties of the implementation. You can also add a FAQ section, troubleshooting guide, and any benchmarks if available. + +Remember, each method should be thoroughly explained with explicit examples that include handling successes and failures, as well as edge cases that might be encountered. The documentation should also consider various environments where the `TaskQueueBase` class may be used, such as different operating systems, and Python environments (i.e. CPython vs. PyPy). diff --git a/docs/swarms/swarms/godmode.md b/docs/swarms/swarms/godmode.md deleted file mode 100644 index 6655c954..00000000 --- a/docs/swarms/swarms/godmode.md +++ /dev/null @@ -1,249 +0,0 @@ -# `ModelParallelizer` Documentation - -## Table of Contents -1. [Understanding the Purpose](#understanding-the-purpose) -2. [Overview and Introduction](#overview-and-introduction) -3. [Class Definition](#class-definition) -4. [Functionality and Usage](#functionality-and-usage) -5. [Additional Information](#additional-information) -6. [Examples](#examples) -7. [Conclusion](#conclusion) - -## 1. Understanding the Purpose - -To create comprehensive documentation for the `ModelParallelizer` class, let's begin by understanding its purpose and functionality. - -### Purpose and Functionality - -`ModelParallelizer` is a class designed to facilitate the orchestration of multiple Language Model Models (LLMs) to perform various tasks simultaneously. It serves as a powerful tool for managing, distributing, and collecting responses from these models. - -Key features and functionality include: - -- **Parallel Task Execution**: `ModelParallelizer` can distribute tasks to multiple LLMs and execute them in parallel, improving efficiency and reducing response time. - -- **Structured Response Presentation**: The class presents the responses from LLMs in a structured tabular format, making it easy for users to compare and analyze the results. - -- **Task History Tracking**: `ModelParallelizer` keeps a record of tasks that have been submitted, allowing users to review previous tasks and responses. - -- **Asynchronous Execution**: The class provides options for asynchronous task execution, which can be particularly useful for handling a large number of tasks. - -Now that we have an understanding of its purpose, let's proceed to provide a detailed overview and introduction. - -## 2. Overview and Introduction - -### Overview - -The `ModelParallelizer` class is a crucial component for managing and utilizing multiple LLMs in various natural language processing (NLP) tasks. Its architecture and functionality are designed to address the need for parallel processing and efficient response handling. - -### Importance and Relevance - -In the rapidly evolving field of NLP, it has become common to use multiple language models to achieve better results in tasks such as translation, summarization, and question answering. `ModelParallelizer` streamlines this process by allowing users to harness the capabilities of several LLMs simultaneously. - -Key points: - -- **Parallel Processing**: `ModelParallelizer` leverages multithreading to execute tasks concurrently, significantly reducing the time required for processing. - -- **Response Visualization**: The class presents responses in a structured tabular format, enabling users to visualize and analyze the outputs from different LLMs. - -- **Task Tracking**: Developers can track the history of tasks submitted to `ModelParallelizer`, making it easier to manage and monitor ongoing work. - -### Architecture and How It Works - -The architecture and working of `ModelParallelizer` can be summarized in four steps: - -1. **Task Reception**: `ModelParallelizer` receives a task from the user. - -2. **Task Distribution**: The class distributes the task to all registered LLMs. - -3. **Response Collection**: `ModelParallelizer` collects the responses generated by the LLMs. - -4. **Response Presentation**: Finally, the class presents the responses from all LLMs in a structured tabular format, making it easy for users to compare and analyze the results. - -Now that we have an overview, let's proceed with a detailed class definition. - -## 3. Class Definition - -### Class Attributes - -- `llms`: A list of LLMs (Language Model Models) that `ModelParallelizer` manages. - -- `last_responses`: Stores the responses from the most recent task. - -- `task_history`: Keeps a record of all tasks submitted to `ModelParallelizer`. - -### Methods - -The `ModelParallelizer` class defines various methods to facilitate task distribution, execution, and response presentation. Let's examine some of the key methods: - -- `run(task)`: Distributes a task to all LLMs, collects responses, and returns them. - -- `print_responses(task)`: Prints responses from all LLMs in a structured tabular format. - -- `run_all(task)`: Runs the task on all LLMs sequentially and returns responses. - -- `arun_all(task)`: Asynchronously runs the task on all LLMs and returns responses. - -- `print_arun_all(task)`: Prints responses from all LLMs after asynchronous execution. - -- `save_responses_to_file(filename)`: Saves responses to a file for future reference. - -- `load_llms_from_file(filename)`: Loads LLMs from a file, making it easy to configure `ModelParallelizer` for different tasks. - -- `get_task_history()`: Retrieves the task history, allowing users to review previous tasks. - -- `summary()`: Provides a summary of task history and the last responses, aiding in post-processing and analysis. - -Now that we have covered the class definition, let's delve into the functionality and usage of `ModelParallelizer`. - -## 4. Functionality and Usage - -### Distributing a Task and Collecting Responses - -One of the primary use cases of `ModelParallelizer` is to distribute a task to all registered LLMs and collect their responses. This can be achieved using the `run(task)` method. Below is an example: - -```python -parallelizer = ModelParallelizer(llms) -responses = parallelizer.run("Translate the following English text to French: 'Hello, how are you?'") -``` - -### Printing Responses - -To present the responses from all LLMs in a structured tabular format, use the `print_responses(task)` method. Example: - -```python -parallelizer.print_responses("Summarize the main points of 'War and Peace.'") -``` - -### Saving Responses to a File - -Users can save the responses to a file using the `save_responses_to_file(filename)` method. This is useful for archiving and reviewing responses later. Example: - -```python -parallelizer.save_responses_to_file("responses.txt") -``` - -### Task History - -The `ModelParallelizer` class keeps track of the task history. Developers can access the task history using the `get_task_history()` method. Example: - -```python -task_history = parallelizer.get_task_history() -for i, task in enumerate(task_history): - print(f"Task {i + 1}: {task}") -``` - -## 5. Additional Information - -### Parallel Execution - -`ModelParallelizer` employs multithreading to execute tasks concurrently. This parallel processing capability significantly improves the efficiency of handling multiple tasks simultaneously. - -### Response Visualization - -The structured tabular format used for presenting responses simplifies the comparison and analysis of outputs from different LLMs. - -## 6. Examples - -Let's explore additional usage examples to illustrate the versatility of `ModelParallelizer` in handling various NLP tasks. - -### Example 1: Sentiment Analysis - -```python -from swarms.models import OpenAIChat -from swarms.swarms import ModelParallelizer -from swarms.workers.worker import Worker - -# Create an instance of an LLM for sentiment analysis -llm = OpenAIChat(model_name="gpt-4", openai_api_key="api-key", temperature=0.5) - -# Create worker agents -worker1 = Worker( - llm=llm, - ai_name="Bumble Bee", - ai_role="Worker in a swarm", - external_tools=None, - human_in_the_loop=False, - temperature=0.5, -) -worker2 = Worker - -( - llm=llm, - ai_name="Optimus Prime", - ai_role="Worker in a swarm", - external_tools=None, - human_in_the_loop=False, - temperature=0.5, -) -worker3 = Worker( - llm=llm, - ai_name="Megatron", - ai_role="Worker in a swarm", - external_tools=None, - human_in_the_loop=False, - temperature=0.5, -) - -# Register the worker agents with ModelParallelizer -agents = [worker1, worker2, worker3] -parallelizer = ModelParallelizer(agents) - -# Task for sentiment analysis -task = "Please analyze the sentiment of the following sentence: 'This movie is amazing!'" - -# Print responses from all agents -parallelizer.print_responses(task) -``` - -### Example 2: Translation - -```python -from swarms.models import OpenAIChat - -from swarms.swarms import ModelParallelizer - -# Define LLMs for translation tasks -translator1 = OpenAIChat(model_name="translator-en-fr", openai_api_key="api-key", temperature=0.7) -translator2 = OpenAIChat(model_name="translator-en-es", openai_api_key="api-key", temperature=0.7) -translator3 = OpenAIChat(model_name="translator-en-de", openai_api_key="api-key", temperature=0.7) - -# Register translation agents with ModelParallelizer -translators = [translator1, translator2, translator3] -parallelizer = ModelParallelizer(translators) - -# Task for translation -task = "Translate the following English text to French: 'Hello, how are you?'" - -# Print translated responses from all agents -parallelizer.print_responses(task) -``` - -### Example 3: Summarization - -```python -from swarms.models import OpenAIChat - -from swarms.swarms import ModelParallelizer - - -# Define LLMs for summarization tasks -summarizer1 = OpenAIChat(model_name="summarizer-en", openai_api_key="api-key", temperature=0.6) -summarizer2 = OpenAIChat(model_name="summarizer-en", openai_api_key="api-key", temperature=0.6) -summarizer3 = OpenAIChat(model_name="summarizer-en", openai_api_key="api-key", temperature=0.6) - -# Register summarization agents with ModelParallelizer -summarizers = [summarizer1, summarizer2, summarizer3] -parallelizer = ModelParallelizer(summarizers) - -# Task for summarization -task = "Summarize the main points of the article titled 'Climate Change and Its Impact on the Environment.'" - -# Print summarized responses from all agents -parallelizer.print_responses(task) -``` - -## 7. Conclusion - -In conclusion, the `ModelParallelizer` class is a powerful tool for managing and orchestrating multiple Language Model Models in natural language processing tasks. Its ability to distribute tasks, collect responses, and present them in a structured format makes it invaluable for streamlining NLP workflows. By following the provided documentation, users can harness the full potential of `ModelParallelizer` to enhance their natural language processing projects. - -For further information on specific LLMs or advanced usage, refer to the documentation of the respective models and their APIs. Additionally, external resources on parallel execution and response visualization can provide deeper insights into these topics. \ No newline at end of file diff --git a/docs/swarms/swarms/groupchat.md b/docs/swarms/swarms/groupchat.md deleted file mode 100644 index bf9cbaad..00000000 --- a/docs/swarms/swarms/groupchat.md +++ /dev/null @@ -1,167 +0,0 @@ -# Swarms Framework Documentation - ---- - -## Overview - -The Swarms framework is a Python library designed to facilitate the creation and management of a simulated group chat environment. This environment can be used for a variety of purposes, such as training conversational agents, role-playing games, or simulating dialogues for machine learning purposes. The core functionality revolves around managing the agent of messages between different agents within the chat, as well as handling the selection and responses of these agents based on the conversation's context. - -### Purpose - -The purpose of the Swarms framework, and specifically the `GroupChat` and `GroupChatManager` classes, is to simulate a dynamic and interactive conversation between multiple agents. This simulates a real-time chat environment where each participant is represented by an agent with a specific role and behavioral patterns. These agents interact within the rules of the group chat, controlled by the `GroupChatManager`. - -### Key Features - -- **Agent Interaction**: Allows multiple agents to communicate within a group chat scenario. -- **Message Management**: Handles the storage and agent of messages within the group chat. -- **Role Play**: Enables agents to assume specific roles and interact accordingly. -- **Conversation Context**: Maintains the context of the conversation for appropriate responses by agents. - ---- - -## GroupChat Class - -The `GroupChat` class is the backbone of the Swarms framework's chat simulation. It maintains the list of agents participating in the chat, the messages that have been exchanged, and the logic to reset the chat and determine the next speaker. - -### Class Definition - -#### Parameters - -| Parameter | Type | Description | Default Value | -|------------|---------------------|--------------------------------------------------------------|---------------| -| agents | List[Agent] | List of agent flows participating in the group chat. | None | -| messages | List[Dict] | List of message dictionaries exchanged in the group chat. | None | -| max_round | int | Maximum number of rounds/messages allowed in the group chat. | 10 | -| admin_name | str | The name of the admin agent in the group chat. | "Admin" | - -#### Class Properties and Methods - -- `agent_names`: Returns a list of the names of the agents in the group chat. -- `reset()`: Clears all messages from the group chat. -- `agent_by_name(name: str) -> Agent`: Finds and returns an agent by name. -- `next_agent(agent: Agent) -> Agent`: Returns the next agent in the list. -- `select_speaker_msg() -> str`: Returns the message for selecting the next speaker. -- `select_speaker(last_speaker: Agent, selector: Agent) -> Agent`: Logic to select the next speaker based on the last speaker and the selector agent. -- `_participant_roles() -> str`: Returns a string listing all participant roles. -- `format_history(messages: List[Dict]) -> str`: Formats the history of messages for display or processing. - -### Usage Examples - -#### Example 1: Initializing a GroupChat - -```python -from swarms.structs.agent import Agent -from swarms.groupchat import GroupChat - -# Assuming Agent objects (flow1, flow2, flow3) are initialized and configured -agents = [flow1, flow2, flow3] -group_chat = GroupChat(agents=agents, messages=[], max_round=10) -``` - -#### Example 2: Resetting a GroupChat - -```python -group_chat.reset() -``` - -#### Example 3: Selecting a Speaker - -```python -last_speaker = agents[0] # Assuming this is a Agent object representing the last speaker -selector = agents[1] # Assuming this is a Agent object with the selector role - -next_speaker = group_chat.select_speaker(last_speaker, selector) -``` - ---- - -## GroupChatManager Class - -The `GroupChatManager` class acts as a controller for the `GroupChat` instance. It orchestrates the interaction between agents, prompts for tasks, and manages the rounds of conversation. - -### Class Definition - -#### Constructor Parameters - -| Parameter | Type | Description | -|------------|-------------|------------------------------------------------------| -| groupchat | GroupChat | The GroupChat instance that the manager will handle. | -| selector | Agent | The Agent object that selects the next speaker. | - -#### Methods - -- `__call__(task: str)`: Invokes the GroupChatManager with a given task string to start the conversation. - -### Usage Examples - -#### Example 1: Initializing GroupChatManager - -```python -from swarms.groupchat import GroupChat, GroupChatManager -from swarms.structs.agent import Agent - -# Initialize your agents and group chat as shown in previous examples -chat_manager = GroupChatManager(groupchat=group_chat, selector=manager) -``` - -#### Example 2: Starting a Conversation - -```python -# Start the group chat with a task -chat_history = chat_manager("Start a conversation about space exploration.") -``` - -#### Example 3: Using the Call Method - -```python -# The call method is the same as starting a conversation -chat_history = chat_manager.__call__("Discuss recent advances in AI.") -``` - ---- - -## Conclusion - -In summary, the Swarms framework offers a unique and effective solution for simulating group chat environments. Its `GroupChat` and `GroupChatManager` classes provide the necessary infrastructure to create dynamic conversations between agents, manage messages, and maintain the context of the dialogue. This framework can be instrumental in developing more sophisticated conversational agents, experimenting with social dynamics in chat environments, and providing a rich dataset for machine learning applications. - -By leveraging the framework's features, users can create complex interaction scenarios that closely mimic real-world group communication. This can prove to be a valuable asset in the fields of artificial intelligence, computational social science, and beyond. - ---- - -### Frequently Asked Questions (FAQ) - -**Q: Can the Swarms framework handle real-time interactions between agents?** - -A: The Swarms framework is designed to simulate group chat environments. While it does not handle real-time interactions as they would occur on a network, it can simulate the agent of conversation in a way that mimics real-time communication. - -**Q: Is the Swarms framework capable of natural language processing?** - -A: The framework itself is focused on the structure and management of group chats. It does not inherently include natural language processing (NLP) capabilities. However, it can be integrated with NLP tools to enhance the simulation with language understanding and generation features. - -**Q: Can I customize the roles and behaviors of agents within the framework?** - -A: Yes, the framework is designed to be flexible. You can define custom roles and behaviors for agents to fit the specific requirements of your simulation scenario. - -**Q: What are the limitations of the Swarms framework?** - -A: The framework is constrained by its design to simulate text-based group chats. It is not suitable for voice or video communication simulations. Additionally, its effectiveness depends on the sophistication of the agents’ decision-making logic, which is outside the framework itself. - -**Q: Is it possible to integrate the Swarms framework with other chat services?** - -A: The framework is can be integrated with any chat services. However, it could potentially be adapted to work with chat service APIs, where the agents could be used to simulate user behavior within a real chat application. - -**Q: How does the `GroupChatManager` select the next speaker?** - -A: The `GroupChatManager` uses a selection mechanism, which is typically based on the conversation's context and the roles of the agents, to determine the next speaker. The specifics of this mechanism can be customized to match the desired agent of the conversation. - -**Q: Can I contribute to the Swarms framework or suggest features?** - -A: As with many open-source projects, contributions and feature suggestions can usually be made through the project's repository on platforms like GitHub. It's best to check with the maintainers of the Swarms framework for their contribution guidelines. - -**Q: Are there any tutorials or community support for new users of the Swarms framework?** - -A: Documentation and usage examples are provided with the framework. Community support may be available through forums, chat groups, or the platform where the framework is hosted. Tutorials may also be available from third-party educators or in official documentation. - -**Q: What programming skills do I need to use the Swarms framework effectively?** - -A: You should have a good understanding of Python programming, including experience with classes and methods. Familiarity with the principles of agent-based modeling and conversational AI would also be beneficial. diff --git a/mkdocs.yml b/mkdocs.yml index dd928dab..2f654a98 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -104,6 +104,7 @@ nav: - stepinput: "swarms/structs/stepinput.md" - artifact: "swarms/structs/artifact.md" - task: "swarms/structs/task.md" + - Task Queue Base: "swarms/structs/taskqueuebase.md" - Workflows: - recursiveworkflow: "swarms/structs/recursiveworkflow.md" - concurrentworkflow: "swarms/structs/concurrentworkflow.md" @@ -116,6 +117,7 @@ nav: - groupchat: "swarms/structs/groupchat.md" - swarmnetwork: "swarms/structs/swarmnetwork.md" - groupchatmanager: "swarms/structs/groupchatmanager.md" + - MajorityVoting: "swarms/structs/majorityvoting.md" - swarms.tokenizers: - Language: - Tokenizer: "swarms/tokenizers/tokenizer.md" diff --git a/scripts/auto_docs.py b/scripts/auto_tests_docs/auto_docs.py similarity index 71% rename from scripts/auto_docs.py rename to scripts/auto_tests_docs/auto_docs.py index f469e9ec..db1f2caf 100644 --- a/scripts/auto_docs.py +++ b/scripts/auto_tests_docs/auto_docs.py @@ -9,17 +9,13 @@ from scripts.auto_tests_docs.docs import DOCUMENTATION_WRITER_SOP from swarms import OpenAIChat ########## -from swarms.tokenizers.r_tokenizers import ( - SentencePieceTokenizer, - HuggingFaceTokenizer, - Tokenizer, -) -from swarms.tokenizers.base_tokenizer import BaseTokenizer -from swarms.tokenizers.openai_tokenizers import OpenAITokenizer -from swarms.tokenizers.anthropic_tokenizer import ( - AnthropicTokenizer, + +from swarms.structs.tool_json_schema import JSON +from swarms.structs.majority_voting import ( + MajorityVoting, ) -from swarms.tokenizers.cohere_tokenizer import CohereTokenizer +from swarms.structs.stackoverflow_swarm import StackOverflowSwarm +from swarms.structs.task_queue_base import TaskQueueBase #################### @@ -47,7 +43,7 @@ def process_documentation(cls): # Process with OpenAI model (assuming the model's __call__ method takes this input and returns processed content) processed_content = model( - DOCUMENTATION_WRITER_SOP(input_content, "swarms.tokenizers") + DOCUMENTATION_WRITER_SOP(input_content, "swarms.structs") ) # doc_content = f"# {cls.__name__}\n\n{processed_content}\n" @@ -67,13 +63,10 @@ def process_documentation(cls): def main(): classes = [ - SentencePieceTokenizer, - HuggingFaceTokenizer, - Tokenizer, - BaseTokenizer, - OpenAITokenizer, - AnthropicTokenizer, - CohereTokenizer, + JSON, + MajorityVoting, + StackOverflowSwarm, + TaskQueueBase, ] threads = [] for cls in classes: @@ -87,7 +80,7 @@ def main(): for thread in threads: thread.join() - print("Documentation generated in 'swarms.tokenizers' directory.") + print("Documentation generated in 'swarms.structs' directory.") if __name__ == "__main__": diff --git a/scripts/auto_tests_docs/mkdocs_handler.py b/scripts/auto_tests_docs/mkdocs_handler.py index a61defa8..8b1dc0a0 100644 --- a/scripts/auto_tests_docs/mkdocs_handler.py +++ b/scripts/auto_tests_docs/mkdocs_handler.py @@ -28,4 +28,4 @@ def generate_file_list(directory, output_file): # Use the function to generate the file list -generate_file_list("docs/swarms/tokenizers", "file_list.txt") +generate_file_list("docs/swarms/structs", "file_list.txt") diff --git a/swarms/agents/developer_agents.py b/swarms/agents/developer_agents.py new file mode 100644 index 00000000..95b68683 --- /dev/null +++ b/swarms/agents/developer_agents.py @@ -0,0 +1,136 @@ +from swarms.structs.agent import Agent +from swarms.prompts.tests import TEST_WRITER_SOP_PROMPT +from swarms.prompts.documentation import DOCUMENTATION_WRITER_SOP + + +class UnitTesterAgent: + """ + This class represents a unit testing agent responsible for generating unit tests for the swarms package. + + Attributes: + - llm: The low-level model used by the agent. + - agent_name (str): The name of the agent. + - agent_description (str): The description of the agent. + - max_loops (int): The maximum number of loops the agent can run. + - SOP_PROMPT: The system output prompt used by the agent. + - agent: The underlying agent object used for running tasks. + + Methods: + - run(task: str, *args, **kwargs) -> str: Run the agent with the given task and return the response. + """ + + def __init__( + self, + llm, + agent_name: str = "Unit Testing Agent", + agent_description: str = "This agent is responsible for generating unit tests for the swarms package.", + max_loops: int = 1, + sop: str = None, + module: str = None, + path: str = None, + autosave: bool = True, + *args, + **kwargs, + ): + super().__init__() + self.llm = llm + self.agent_name = agent_name + self.agent_description = agent_description + self.max_loops = max_loops + self.sop = sop + self.module = module + self.path = path + self.autosave = autosave + + self.agent = Agent( + llm=llm, + agent_name=agent_name, + agent_description=agent_description, + autosave=self.autosave, + system_prompt=agent_description, + max_loops=max_loops, + *args, + **kwargs, + ) + + def run(self, task: str, module: str, path: str, *args, **kwargs): + """ + Run the agent with the given task. + + Args: + - task (str): The task to run the agent with. + + Returns: + - str: The response from the agent. + """ + return self.agent.run( + TEST_WRITER_SOP_PROMPT(task, self.module, self.path), + *args, + **kwargs, + ) + + +class DocumentorAgent: + """ + This class represents a documentor agent responsible for generating unit tests for the swarms package. + + Attributes: + - llm: The low-level model used by the agent. + - agent_name (str): The name of the agent. + - agent_description (str): The description of the agent. + - max_loops (int): The maximum number of loops the agent can run. + - SOP_PROMPT: The system output prompt used by the agent. + - agent: The underlying agent object used for running tasks. + + Methods: + - run(task: str, *args, **kwargs) -> str: Run the agent with the given task and return the response. + """ + + def __init__( + self, + llm, + agent_name: str = "Documentor Agent", + agent_description: str = "This agent is responsible for generating unit tests for the swarms package.", + max_loops: int = 1, + sop: str = None, + module: str = None, + path: str = None, + autosave: bool = True, + *args, + **kwargs, + ): + super().__init__(*args, **kwargs) + self.llm = llm + self.agent_name = agent_name + self.agent_description = agent_description + self.max_loops = max_loops + self.sop = sop + self.module = module + self.path = path + self.autosave = autosave + + self.agent = Agent( + llm=llm, + agent_name=agent_name, + agent_description=agent_description, + autosave=self.autosave, + system_prompt=agent_description, + max_loops=max_loops, + *args, + **kwargs, + ) + + def run(self, task: str, module: str, path: str, *args, **kwargs): + """ + Run the agent with the given task. + + Args: + - task (str): The task to run the agent with. + + Returns: + - str: The response from the agent. + """ + return self.agent.run( + DOCUMENTATION_WRITER_SOP(task, self.module) * args, + **kwargs, + ) diff --git a/swarms/memory/chroma_db.py b/swarms/memory/chroma_db.py index 8a5b6e91..b2f1453f 100644 --- a/swarms/memory/chroma_db.py +++ b/swarms/memory/chroma_db.py @@ -7,10 +7,6 @@ from typing import Optional, Callable, List import chromadb from dotenv import load_dotenv -# from chromadb.utils.data import ImageLoader -from chromadb.utils.embedding_functions import ( - OpenCLIPEmbeddingFunction, -) from swarms.utils.data_to_text import data_to_text from swarms.utils.markdown_message import display_markdown_message @@ -94,7 +90,7 @@ class ChromaDB: # If multimodal set the embedding model to OpenCLIP if multimodal: - self.embedding_function = OpenCLIPEmbeddingFunction() + self.embedding_function = None # Create ChromaDB client self.client = chromadb.Client() diff --git a/swarms/memory/lanchain_chroma.py b/swarms/memory/lanchain_chroma.py index 07846fd6..e830ec4d 100644 --- a/swarms/memory/lanchain_chroma.py +++ b/swarms/memory/lanchain_chroma.py @@ -52,6 +52,7 @@ class LangchainChromaVectorMemory: search_memory: Searches the vector memory for similar entries. ask_question: Asks a question to the vector memory. """ + def __init__( self, loc=None, diff --git a/swarms/structs/__init__.py b/swarms/structs/__init__.py index b8dfc929..1beddfb6 100644 --- a/swarms/structs/__init__.py +++ b/swarms/structs/__init__.py @@ -64,6 +64,10 @@ from swarms.structs.majority_voting import ( MajorityVoting, ) from swarms.structs.stackoverflow_swarm import StackOverflowSwarm +from swarms.structs.task_queue_base import ( + synchronized_queue, + TaskQueueBase, +) __all__ = [ "Agent", @@ -125,4 +129,6 @@ __all__ = [ "majority_voting", "MajorityVoting", "StackOverflowSwarm", + "synchronized_queue", + "TaskQueueBase", ] diff --git a/tests/memory/test_dictinternalmemory.py b/tests/memory/test_dictinternalmemory.py new file mode 100644 index 00000000..3265ae50 --- /dev/null +++ b/tests/memory/test_dictinternalmemory.py @@ -0,0 +1,69 @@ +# DictInternalMemory + +import pytest +from swarms.memory import DictInternalMemory +from uuid import uuid4 + +# Example of an extensive suite of tests for DictInternalMemory. + + +# Fixture for repeatedly initializing the class with different numbers of entries. +@pytest.fixture(params=[1, 5, 10, 100]) +def memory(request): + return DictInternalMemory(n_entries=request.param) + + +# Basic Tests +def test_initialization(memory): + assert memory.len() == 0 + + +def test_single_add(memory): + memory.add(10, {"data": "test"}) + assert memory.len() == 1 + + +def test_memory_limit_enforced(memory): + entries_to_add = memory.n_entries + 10 + for i in range(entries_to_add): + memory.add(i, {"data": f"test{i}"}) + assert memory.len() == memory.n_entries + + +# Parameterized Tests +@pytest.mark.parametrize( + "scores, best_score", [([10, 5, 3], 10), ([1, 2, 3], 3)] +) +def test_get_top_n(scores, best_score, memory): + for score in scores: + memory.add(score, {"data": f"test{score}"}) + top_entry = memory.get_top_n(1) + assert top_entry[0][1]["score"] == best_score + + +# Exception Testing +@pytest.mark.parametrize("invalid_n", [-1, 0]) +def test_invalid_n_entries_raises_exception(invalid_n): + with pytest.raises(ValueError): + DictInternalMemory(invalid_n) + + +# Mocks and Monkeypatching +def test_add_with_mocked_uuid4(monkeypatch, memory): + # Mock the uuid4 function to return a known value + class MockUUID: + hex = "1234abcd" + + monkeypatch.setattr(uuid4, "__str__", lambda: MockUUID.hex) + memory.add(20, {"data": "mock_uuid"}) + assert MockUUID.hex in memory.data + + +# Test using Mocks to simulate I/O or external interactions here +# ... + +# More tests to hit edge cases, concurrency issues, etc. +# ... + +# Tests for concurrency issues, if relevant +# ... diff --git a/tests/memory/test_dictsharedmemory.py b/tests/memory/test_dictsharedmemory.py new file mode 100644 index 00000000..8537d6b8 --- /dev/null +++ b/tests/memory/test_dictsharedmemory.py @@ -0,0 +1,90 @@ +import os +import tempfile +import pytest +from swarms.memory import DictSharedMemory + +# Utility functions or fixtures might come first + + +@pytest.fixture +def memory_file(): + with tempfile.NamedTemporaryFile("w+", delete=False) as tmp_file: + yield tmp_file.name + os.unlink(tmp_file.name) + + +@pytest.fixture +def memory_instance(memory_file): + return DictSharedMemory(file_loc=memory_file) + + +# Basic tests + + +def test_init(memory_file): + memory = DictSharedMemory(file_loc=memory_file) + assert os.path.exists( + memory.file_loc + ), "Memory file should be created if non-existent" + + +def test_add_entry(memory_instance): + success = memory_instance.add(9.5, "agent123", 1, "Test Entry") + assert success, "add_entry should return True on success" + + +def test_add_entry_thread_safety(memory_instance): + # We could create multiple threads to test the thread safety of the add_entry method + pass + + +def test_get_top_n(memory_instance): + memory_instance.add(9.5, "agent123", 1, "Entry A") + memory_instance.add(8.5, "agent124", 1, "Entry B") + top_1 = memory_instance.get_top_n(1) + assert ( + len(top_1) == 1 + ), "get_top_n should return the correct number of top entries" + + +# Parameterized tests + + +@pytest.mark.parametrize( + "scores, agent_ids, expected_top_score", + [ + ([1.0, 2.0, 3.0], ["agent1", "agent2", "agent3"], 3.0), + # add more test cases + ], +) +def test_parametrized_get_top_n( + memory_instance, scores, agent_ids, expected_top_score +): + for score, agent_id in zip(scores, agent_ids): + memory_instance.add( + score, agent_id, 1, f"Entry by {agent_id}" + ) + top_1 = memory_instance.get_top_n(1) + top_score = next(iter(top_1.values()))["score"] + assert ( + top_score == expected_top_score + ), "get_top_n should return the entry with top score" + + +# Exception testing + + +def test_add_entry_invalid_input(memory_instance): + with pytest.raises(ValueError): + memory_instance.add( + "invalid_score", "agent123", 1, "Test Entry" + ) + + +# Mocks and monkey-patching + + +def test_write_fails_due_to_permissions(memory_instance, mocker): + mocker.patch("builtins.open", side_effect=PermissionError) + with pytest.raises(PermissionError): + memory_instance.add(9.5, "agent123", 1, "Test Entry") diff --git a/tests/memory/test_langchainchromavectormemory.py b/tests/memory/test_langchainchromavectormemory.py new file mode 100644 index 00000000..dc41fa8a --- /dev/null +++ b/tests/memory/test_langchainchromavectormemory.py @@ -0,0 +1,94 @@ +# LangchainChromaVectorMemory + +import pytest +from swarms.memory import LangchainChromaVectorMemory +from unittest.mock import MagicMock, patch + + +# Fixtures for setting up the memory and mocks +@pytest.fixture() +def vector_memory(tmp_path): + loc = tmp_path / "vector_memory" + return LangchainChromaVectorMemory(loc=loc) + + +@pytest.fixture() +def embeddings_mock(): + with patch("swarms.memory.OpenAIEmbeddings") as mock: + yield mock + + +@pytest.fixture() +def chroma_mock(): + with patch("swarms.memory.Chroma") as mock: + yield mock + + +@pytest.fixture() +def qa_mock(): + with patch("swarms.memory.RetrievalQA") as mock: + yield mock + + +# Example test cases +def test_initialization_default_settings(vector_memory): + assert vector_memory.chunk_size == 1000 + assert ( + vector_memory.chunk_overlap == 100 + ) # assuming default overlap of 0.1 + assert vector_memory.loc.exists() + + +def test_add_entry(vector_memory, embeddings_mock): + with patch.object( + vector_memory.db, "add_texts" + ) as add_texts_mock: + vector_memory.add("Example text") + add_texts_mock.assert_called() + + +def test_search_memory_returns_list(vector_memory): + result = vector_memory.search_memory("example query", k=5) + assert isinstance(result, list) + + +def test_ask_question_returns_string(vector_memory, qa_mock): + result = vector_memory.query("What is the color of the sky?") + assert isinstance(result, str) + + +@pytest.mark.parametrize( + "query,k,type,expected", + [ + ("example query", 5, "mmr", [MagicMock()]), + ( + "example query", + 0, + "mmr", + None, + ), # Expected none when k is 0 or negative + ( + "example query", + 3, + "cos", + [MagicMock()], + ), # Mocked object as a placeholder + ], +) +def test_search_memory_different_params( + vector_memory, query, k, type, expected +): + with patch.object( + vector_memory.db, + "max_marginal_relevance_search", + return_value=expected, + ): + with patch.object( + vector_memory.db, + "similarity_search_with_score", + return_value=expected, + ): + result = vector_memory.search_memory( + query, k=k, type=type + ) + assert len(result) == (k if k > 0 else 0) diff --git a/tests/structs/test_json.py b/tests/structs/test_json.py new file mode 100644 index 00000000..4e086d37 --- /dev/null +++ b/tests/structs/test_json.py @@ -0,0 +1,71 @@ +# JSON + +# Contents of test_json.py, which must be placed in the `tests/` directory. + +import pytest +import json +from swarms.tokenizers import JSON + + +# Fixture for reusable JSON schema file paths +@pytest.fixture +def valid_schema_path(tmp_path): + d = tmp_path / "sub" + d.mkdir() + p = d / "schema.json" + p.write_text( + '{"type": "object", "properties": {"name": {"type":' + ' "string"}}}' + ) + return str(p) + + +@pytest.fixture +def invalid_schema_path(tmp_path): + d = tmp_path / "sub" + d.mkdir() + p = d / "invalid_schema.json" + p.write_text("this is not a valid JSON") + return str(p) + + +# This test class must be subclassed as JSON class is abstract +class TestableJSON(JSON): + def validate(self, data): + # Here must be a real validation implementation for testing + pass + + +# Basic tests +def test_initialize_json(valid_schema_path): + json_obj = TestableJSON(valid_schema_path) + assert json_obj.schema_path == valid_schema_path + assert "name" in json_obj.schema["properties"] + + +def test_load_schema_failure(invalid_schema_path): + with pytest.raises(json.JSONDecodeError): + TestableJSON(invalid_schema_path) + + +# Mocking tests +def test_validate_calls_method(monkeypatch): + # Mock the validate method to check that it is being called + pass + + +# Exception tests +def test_initialize_with_nonexistent_schema(): + with pytest.raises(FileNotFoundError): + TestableJSON("nonexistent_path.json") + + +# Tests on different Python versions if applicable +# ... + + +# Grouping tests marked as slow if they perform I/O operations +@pytest.mark.slow +def test_loading_large_schema(): + # Test with a large json file + pass diff --git a/tests/structs/test_majorityvoting.py b/tests/structs/test_majorityvoting.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/structs/test_taskqueuebase.py b/tests/structs/test_taskqueuebase.py new file mode 100644 index 00000000..f37e2ca5 --- /dev/null +++ b/tests/structs/test_taskqueuebase.py @@ -0,0 +1,103 @@ +# TaskQueueBase + +import threading +from unittest.mock import Mock +import pytest +from swarms.tokenizers import TaskQueueBase, Task, Agent + + +# Create mocked instances of dependencies +@pytest.fixture() +def task(): + return Mock(spec=Task) + + +@pytest.fixture() +def agent(): + return Mock(spec=Agent) + + +@pytest.fixture() +def concrete_task_queue(): + class ConcreteTaskQueue(TaskQueueBase): + def add_task(self, task): + pass # Here you would add concrete implementation of add_task + + def get_task(self, agent): + pass # Concrete implementation of get_task + + def complete_task(self, task_id): + pass # Concrete implementation of complete_task + + def reset_task(self, task_id): + pass # Concrete implementation of reset_task + + return ConcreteTaskQueue() + + +def test_task_queue_initialization(concrete_task_queue): + assert isinstance(concrete_task_queue, TaskQueueBase) + assert isinstance(concrete_task_queue.lock, threading.Lock) + + +def test_add_task_success(concrete_task_queue, task): + # Assuming add_task returns True on success + assert concrete_task_queue.add_task(task) is True + + +def test_add_task_failure(concrete_task_queue, task): + # Assuming the task is somehow invalid + # Note: Concrete implementation requires logic defining what an invalid task is + concrete_task_queue.add_task(task) + assert ( + concrete_task_queue.add_task(task) is False + ) # Adding the same task again + + +@pytest.mark.parametrize("invalid_task", [None, "", {}, []]) +def test_add_task_invalid_input(concrete_task_queue, invalid_task): + with pytest.raises(TypeError): + concrete_task_queue.add_task(invalid_task) + + +def test_get_task_success(concrete_task_queue, agent): + # Assuming there's a mechanism to populate queue + # You will need to add a task before getting it + task = Mock(spec=Task) + concrete_task_queue.add_task(task) + assert concrete_task_queue.get_task(agent) == task + + +# def test_get_task_no_tasks_available(concrete_task_queue, agent): +# with pytest.raises( +# EmptyQueueError +# ): # Assuming such an exception exists +# concrete_task_queue.get_task(agent) + + +def test_complete_task_success(concrete_task_queue): + task_id = "test_task_123" + # Populating queue and completing task assumed + assert concrete_task_queue.complete_task(task_id) is None + + +# def test_complete_task_with_invalid_id(concrete_task_queue): +# invalid_task_id = "invalid_id" +# with pytest.raises( +# TaskNotFoundError +# ): # Assuming such an exception exists +# concrete_task_queue.complete_task(invalid_task_id) + + +def test_reset_task_success(concrete_task_queue): + task_id = "test_task_123" + # Populating queue and resetting task assumed + assert concrete_task_queue.reset_task(task_id) is None + + +# def test_reset_task_with_invalid_id(concrete_task_queue): +# invalid_task_id = "invalid_id" +# with pytest.raises( +# TaskNotFoundError +# ): # Assuming such an exception exists +# concrete_task_queue.reset_task(invalid_task_id) From 75f511ad051cde25587f2bb30a64247fe6ee3a72 Mon Sep 17 00:00:00 2001 From: Kye Date: Fri, 16 Feb 2024 14:40:46 -0800 Subject: [PATCH 007/105] [CLEANUP] --- pyproject.toml | 2 +- auto_tests.py => scripts/auto_tests_docs/auto_tests.py | 0 2 files changed, 1 insertion(+), 1 deletion(-) rename auto_tests.py => scripts/auto_tests_docs/auto_tests.py (100%) diff --git a/pyproject.toml b/pyproject.toml index 7d95de7e..74fa9101 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api" [tool.poetry] name = "swarms" -version = "4.1.5" +version = "4.1.6" description = "Swarms - Pytorch" license = "MIT" authors = ["Kye Gomez "] diff --git a/auto_tests.py b/scripts/auto_tests_docs/auto_tests.py similarity index 100% rename from auto_tests.py rename to scripts/auto_tests_docs/auto_tests.py From e93508b2fed1dc401d7349e115ddcb4d5684659f Mon Sep 17 00:00:00 2001 From: Kye Date: Fri, 16 Feb 2024 15:22:08 -0800 Subject: [PATCH 008/105] [README] --- Dockerfile | 23 +++++++++++++++++++++++ README.md | 26 ++++++++++++++++++++++++++ 2 files changed, 49 insertions(+) create mode 100644 Dockerfile diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 00000000..91b75cae --- /dev/null +++ b/Dockerfile @@ -0,0 +1,23 @@ +# Use an official CUDA runtime as a parent image +FROM nvidia/cuda:11.4.2-runtime-ubuntu20.04 + +# Set the working directory in the container to /app +WORKDIR /app + +# Copy the current directory contents into the container at /app +COPY . /app + +# Install any needed packages specified in requirements.txt +RUN apt-get update && apt-get install -y \ + python3-pip \ + && rm -rf /var/lib/apt/lists/* +RUN pip3 install --no-cache-dir -r requirements.txt + +# Make port 80 available to the world outside this container +EXPOSE 80 + +# Define environment variable +# ENV NAME World + +# Run app.py when the container launches +CMD ["python3", "example.py"] \ No newline at end of file diff --git a/README.md b/README.md index cc5ed987..4afad1b6 100644 --- a/README.md +++ b/README.md @@ -1210,6 +1210,32 @@ The swarms package has been meticlously crafted for extreme use-ability and unde └── workers ``` +## Docker Instructions + +This application uses Docker with CUDA support. To build and run the Docker container, follow these steps: + +### Prerequisites + +- Make sure you have [Docker installed](https://docs.docker.com/get-docker/) on your machine. +- Ensure your machine has an NVIDIA GPU and [NVIDIA Docker support](https://github.com/NVIDIA/nvidia-docker) installed. + +### Building the Docker Image + +To build the Docker image, navigate to the root directory containing the `Dockerfile` and run the following command: + +```bash +docker build --gpus all -t swarms +``` +### Running the Docker Container +To run the Docker container, use the following command: + +`docker run --gpus all -p 4000:80 your-image-name` + +Replace your-image-name with the name of your Docker image, and replace 4000:80 with your actual port mapping. The format is hostPort:containerPort. + +Now, your application should be running with CUDA support! + + ## Swarm Newsletter 🤖 🤖 🤖 📧 Sign up to the Swarm newsletter to receive updates on the latest Autonomous agent research papers, step by step guides on creating multi-agent app, and much more Swarmie goodiness 😊 From 44f4cb318b6ce6894d82177b7fab50d2128ebd4c Mon Sep 17 00:00:00 2001 From: Kye Date: Fri, 16 Feb 2024 15:32:06 -0800 Subject: [PATCH 009/105] [README] --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 4afad1b6..3259d55b 100644 --- a/README.md +++ b/README.md @@ -1229,9 +1229,9 @@ docker build --gpus all -t swarms ### Running the Docker Container To run the Docker container, use the following command: -`docker run --gpus all -p 4000:80 your-image-name` +`docker run --gpus all -p 4000:80 swarms` -Replace your-image-name with the name of your Docker image, and replace 4000:80 with your actual port mapping. The format is hostPort:containerPort. +Replace swarms with the name of your Docker image, and replace 4000:80 with your actual port mapping. The format is hostPort:containerPort. Now, your application should be running with CUDA support! From bd18842584013dbf067bc769c6460962628ef132 Mon Sep 17 00:00:00 2001 From: Kye Date: Sat, 17 Feb 2024 12:05:06 -0800 Subject: [PATCH 010/105] [FEATS][ AgentJob, MultiThreadedWorkflow, MultiProcessingWorkflow] --- .gitignore | 1 + multion_agent.py | 102 +++++++++++ pyproject.toml | 1 - swarms/structs/__init__.py | 11 ++ swarms/structs/agent_base.py | 20 +++ swarms/structs/base_swarm.py | 58 ++++++- swarms/structs/multi_process_workflow.py | 197 ++++++++++++++++++++++ swarms/structs/multi_threaded_workflow.py | 157 +++++++++++++++++ swarms/structs/swarm_redis_registry.py | 176 +++++++++++++++++++ 9 files changed, 721 insertions(+), 2 deletions(-) create mode 100644 multion_agent.py create mode 100644 swarms/structs/agent_base.py create mode 100644 swarms/structs/multi_process_workflow.py create mode 100644 swarms/structs/multi_threaded_workflow.py create mode 100644 swarms/structs/swarm_redis_registry.py diff --git a/.gitignore b/.gitignore index ea778ce8..bc35715f 100644 --- a/.gitignore +++ b/.gitignore @@ -17,6 +17,7 @@ venv .DS_Store .DS_STORE +Cargo.lock swarms/agents/.DS_Store _build diff --git a/multion_agent.py b/multion_agent.py new file mode 100644 index 00000000..132ccb87 --- /dev/null +++ b/multion_agent.py @@ -0,0 +1,102 @@ +import multion +from swarms.structs.concurrent_workflow import ConcurrentWorkflow +from swarms.models.base_llm import AbstractLLM +from swarms.structs.agent import Agent +from swarms.structs.task import Task + + +class MultiOnAgent(AbstractLLM): + """ + Represents a multi-on agent that performs browsing tasks. + + Args: + max_steps (int): The maximum number of steps to perform during browsing. + starting_url (str): The starting URL for browsing. + + Attributes: + max_steps (int): The maximum number of steps to perform during browsing. + starting_url (str): The starting URL for browsing. + """ + + def __init__( + self, + multion_api_key: str, + max_steps: int = 4, + starting_url: str = "https://www.google.com", + *args, + **kwargs, + ): + super().__init__(*args, **kwargs) + self.multion_api_key = multion_api_key + self.max_steps = max_steps + self.starting_url = starting_url + + multion.login( + use_api=True, + # multion_api_key=self.multion_api_key + *args, + **kwargs, + ) + + def run(self, task: str, *args, **kwargs): + """ + Runs a browsing task. + + Args: + task (str): The task to perform during browsing. + *args: Additional positional arguments. + **kwargs: Additional keyword arguments. + + Returns: + dict: The response from the browsing task. + """ + response = multion.browse( + { + "cmd": task, + "url": self.starting_url, + "maxSteps": self.max_steps, + }, + *args, + **kwargs, + ) + + return response.result, response.status, response.lastUrl + + +# model +model = MultiOnAgent( + multion_api_key="535ae401948b4c59bc1b2c61eec90fe6" +) + +# out = model.run("search for a recipe") +agent = Agent( + agent_name="MultiOnAgent", + description="A multi-on agent that performs browsing tasks.", + llm=model, + max_loops=1, + system_prompt=None, +) + + +# Task +task = Task( + agent=agent, + description=( + "send an email to vyom on superhuman for a partnership with" + " multion" + ), +) + +# Swarm +workflow = ConcurrentWorkflow( + max_workers=1000, + autosave=True, + print_results=True, + return_results=True, +) + +# Add task to workflow +workflow.add(task) + +# Run workflow +workflow.run() diff --git a/pyproject.toml b/pyproject.toml index 74fa9101..90ab06e6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -81,7 +81,6 @@ roboflow = "*" [tool.poetry.group.lint.dependencies] ruff = ">=0.0.249,<0.1.7" types-toml = "^0.10.8.1" -types-redis = "^4.3.21.6" types-pytz = "^2023.3.0.0" black = "^23.1.0" types-chardet = "^5.0.4.6" diff --git a/swarms/structs/__init__.py b/swarms/structs/__init__.py index 1beddfb6..40bd3325 100644 --- a/swarms/structs/__init__.py +++ b/swarms/structs/__init__.py @@ -68,6 +68,14 @@ from swarms.structs.task_queue_base import ( synchronized_queue, TaskQueueBase, ) +from swarms.structs.multi_process_workflow import ( + MultiProcessingWorkflow, +) +from swarms.structs.multi_threaded_workflow import ( + MultiThreadedWorkflow, +) +from swarms.structs.agent_base import AgentJob + __all__ = [ "Agent", @@ -131,4 +139,7 @@ __all__ = [ "StackOverflowSwarm", "synchronized_queue", "TaskQueueBase", + "MultiProcessingWorkflow", + "MultiThreadedWorkflow", + "AgentJob", ] diff --git a/swarms/structs/agent_base.py b/swarms/structs/agent_base.py new file mode 100644 index 00000000..8f1a3669 --- /dev/null +++ b/swarms/structs/agent_base.py @@ -0,0 +1,20 @@ +import threading +from typing import Callable, Tuple + + +class AgentJob(threading.Thread): + """A class that handles multithreading logic. + + Args: + function (Callable): The function to be executed in a separate thread. + args (Tuple): The arguments to be passed to the function. + """ + + def __init__(self, function: Callable, args: Tuple): + threading.Thread.__init__(self) + self.function = function + self.args = args + + def run(self) -> None: + """Runs the function in a separate thread.""" + self.function(*self.args) diff --git a/swarms/structs/base_swarm.py b/swarms/structs/base_swarm.py index fe81df4e..a961be58 100644 --- a/swarms/structs/base_swarm.py +++ b/swarms/structs/base_swarm.py @@ -1,5 +1,5 @@ import asyncio -from abc import ABC +from abc import ABC, abstractmethod from concurrent.futures import ThreadPoolExecutor, as_completed from typing import Any, Callable, Dict, List, Optional @@ -432,3 +432,59 @@ class AbstractSwarm(ABC): self.agents, ) return list(responses) + + @abstractmethod + def add_swarm_entry(self, swarm): + """ + Add the information of a joined Swarm to the registry. + + Args: + swarm (SwarmManagerBase): Instance of SwarmManagerBase representing the joined Swarm. + + Returns: + None + """ + + @abstractmethod + def add_agent_entry(self, agent: Agent): + """ + Add the information of an Agent to the registry. + + Args: + agent (Agent): Instance of Agent representing the Agent. + + Returns: + None + """ + + @abstractmethod + def retrieve_swarm_information(self, swarm_id: str): + """ + Retrieve the information of a specific Swarm from the registry. + + Args: + swarm_id (str): Unique identifier of the Swarm. + + Returns: + SwarmManagerBase: Instance of SwarmManagerBase representing the retrieved Swarm, or None if not found. + """ + + @abstractmethod + def retrieve_joined_agents(self, agent_id: str) -> List[Agent]: + """ + Retrieve the information the Agents which have joined the registry. + + Returns: + Agent: Instance of Agent representing the retrieved Agent, or None if not found. + """ + + @abstractmethod + def join_swarm( + self, from_entity: Agent | Agent, to_entity: Agent + ): + """ + Add a relationship between a Swarm and an Agent or other Swarm to the registry. + + Args: + from (Agent | SwarmManagerBase): Instance of Agent or SwarmManagerBase representing the source of the relationship. + """ diff --git a/swarms/structs/multi_process_workflow.py b/swarms/structs/multi_process_workflow.py new file mode 100644 index 00000000..39c69eaa --- /dev/null +++ b/swarms/structs/multi_process_workflow.py @@ -0,0 +1,197 @@ +import logging +from functools import wraps +from multiprocessing import Manager, Pool, cpu_count +from time import sleep +from typing import List + +from swarms.structs.base_workflow import BaseWorkflow +from swarms.structs.task import Task + +# Configure logging +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(levelname)s - %(message)s", +) + + +# Retry on failure +def retry_on_failure(max_retries: int = 3, delay: int = 5): + """ + Decorator that retries a function a specified number of times on failure. + + Args: + max_retries (int): The maximum number of retries (default: 3). + delay (int): The delay in seconds between retries (default: 5). + + Returns: + The result of the function if it succeeds within the maximum number of retries, + otherwise None. + """ + + def decorator(func): + @wraps(func) + def wrapper(*args, **kwargs): + for _ in range(max_retries): + try: + return func(*args, **kwargs) + except Exception as error: + logging.error( + f"Error: {str(error)}, retrying in" + f" {delay} seconds..." + ) + sleep(delay) + return None + + return wrapper + + return decorator + + +class MultiProcessingWorkflow(BaseWorkflow): + """ + Initialize a MultiProcessWorkflow object. + + Args: + max_workers (int): The maximum number of workers to use for parallel processing. + autosave (bool): Flag indicating whether to automatically save the workflow. + tasks (List[Task]): A list of Task objects representing the workflow tasks. + *args: Additional positional arguments. + **kwargs: Additional keyword arguments. + + Example: + >>> from swarms.structs.multi_process_workflow import MultiProcessingWorkflow + >>> from swarms.structs.task import Task + >>> from datetime import datetime + >>> from time import sleep + >>> + >>> # Define a simple task + >>> def simple_task(): + >>> sleep(1) + >>> return datetime.now() + >>> + >>> # Create a task object + >>> task = Task( + >>> name="Simple Task", + >>> execute=simple_task, + >>> priority=1, + >>> ) + >>> + >>> # Create a workflow with the task + >>> workflow = MultiProcessingWorkflow(tasks=[task]) + >>> + >>> # Run the workflow + >>> results = workflow.run(task) + >>> + >>> # Print the results + >>> print(results) + """ + + def __init__( + self, + max_workers: int = 5, + autosave: bool = True, + tasks: List[Task] = None, + *args, + **kwargs, + ): + super().__init__(*args, **kwargs) + self.max_workers = max_workers + self.autosave = autosave + self.tasks = sorted( + tasks or [], key=lambda task: task.priority, reverse=True + ) + + self.max_workers or cpu_count() + + if tasks is None: + tasks = [] + + self.tasks = tasks + + def execute_task(self, task: Task, *args, **kwargs): + """Execute a task and handle exceptions. + + Args: + task (Task): The task to execute. + *args: Additional positional arguments for the task execution. + **kwargs: Additional keyword arguments for the task execution. + + Returns: + Any: The result of the task execution. + + """ + try: + result = task.execute(*args, **kwargs) + + logging.info( + f"Task {task} completed successfully with result" + f" {result}" + ) + + if self.autosave: + self._autosave_task_result(task, result) + + except Exception as e: + logging.error( + ( + "An error occurred during execution of task" + f" {task}: {str(e)}" + ), + exc_info=True, + ) + return None + + def run(self, task: Task, *args, **kwargs): + """Run the workflow. + + Args: + task (Task): The task to run. + *args: Additional positional arguments for the task execution. + **kwargs: Additional keyword arguments for the task execution. + + Returns: + List[Any]: The results of all executed tasks. + + """ + try: + results = [] + with Manager() as manager: + with Pool( + processes=self.max_workers, *args, **kwargs + ) as pool: + # Using manager.list() to collect results in a process safe way + results_list = manager.list() + jobs = [ + pool.apply_async( + self.execute_task, + (task,), + callback=results_list.append, + timeout=task.timeout, + *args, + **kwargs, + ) + for task in self.tasks + ] + + # Wait for all jobs to complete + for job in jobs: + job.get() + + results = list(results_list) + + return results + except Exception as error: + logging.error(f"Error in run: {error}") + return None + + def _autosave_task_result(self, task: Task, result): + """Autosave task result. This should be adapted based on how autosaving is implemented. + + Args: + task (Task): The task for which to autosave the result. + result (Any): The result of the task execution. + + """ + # Note: This method might need to be adapted to ensure it's process-safe, depending on how autosaving is implemented. + logging.info(f"Autosaving result for task {task}: {result}") + # Actual autosave logic here diff --git a/swarms/structs/multi_threaded_workflow.py b/swarms/structs/multi_threaded_workflow.py new file mode 100644 index 00000000..df17d8ce --- /dev/null +++ b/swarms/structs/multi_threaded_workflow.py @@ -0,0 +1,157 @@ +import threading +from swarms.structs.base_workflow import BaseWorkflow +import logging +from concurrent.futures import ( + FIRST_COMPLETED, + ThreadPoolExecutor, + wait, +) +from typing import List +from swarms.structs.task import Task +import queue + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(levelname)s - %(message)s", +) + + +class PriorityTask: + """ + Represents a task with a priority level. + + Attributes: + task (Task): The task to be executed. + priority (int): The priority level of the task. + """ + + def __init__(self, task: Task, priority: int = 0): + self.task = task + self.priority = priority + + def __lt__(self, other): + return self.priority < other.priority + + +class MultiThreadedWorkflow(BaseWorkflow): + """ + Represents a multi-threaded workflow that executes tasks concurrently using a thread pool. + + Args: + max_workers (int): The maximum number of worker threads in the thread pool. Default is 5. + autosave (bool): Flag indicating whether to automatically save task results. Default is True. + tasks (List[PriorityTask]): List of priority tasks to be executed. Default is an empty list. + retry_attempts (int): The maximum number of retry attempts for failed tasks. Default is 3. + *args: Variable length argument list. + **kwargs: Arbitrary keyword arguments. + + Attributes: + max_workers (int): The maximum number of worker threads in the thread pool. + autosave (bool): Flag indicating whether to automatically save task results. + retry_attempts (int): The maximum number of retry attempts for failed tasks. + tasks_queue (PriorityQueue): The queue that holds the priority tasks. + lock (Lock): The lock used for thread synchronization. + + Methods: + execute_tasks: Executes the tasks in the thread pool and returns the results. + _autosave_task_result: Autosaves the result of a task. + + """ + + def __init__( + self, + max_workers: int = 5, + autosave: bool = True, + tasks: List[PriorityTask] = None, + retry_attempts: int = 3, + *args, + **kwargs, + ): + super().__init__(*args, **kwargs) + self.max_workers = max_workers + self.autosave = autosave + self.retry_attempts = retry_attempts + if tasks is None: + tasks = [] + self.tasks_queue = queue.PriorityQueue() + for task in tasks: + self.tasks_queue.put(task) + self.lock = threading.Lock() + + def run(self): + """ + Executes the tasks in the thread pool and returns the results. + + Returns: + List: The list of results from the executed tasks. + + """ + results = [] + with ThreadPoolExecutor( + max_workers=self.max_workers + ) as executor: + future_to_task = {} + for _ in range(self.tasks_queue.qsize()): + priority_task = self.tasks_queue.get_nowait() + future = executor.submit(priority_task.task.execute) + future_to_task[future] = ( + priority_task.task, + 0, + ) # (Task, attempt) + + while future_to_task: + # Wait for the next future to complete + done, _ = wait( + future_to_task.keys(), return_when=FIRST_COMPLETED + ) + + for future in done: + task, attempt = future_to_task.pop(future) + try: + result = future.result() + results.append(result) + logging.info( + f"Task {task} completed successfully with" + f" result: {result}" + ) + if self.autosave: + self._autosave_task_result(task, result) + except Exception as e: + logging.error( + ( + f"Attempt {attempt+1} failed for task" + f" {task}: {str(e)}" + ), + exc_info=True, + ) + if attempt + 1 < self.retry_attempts: + # Retry the task + retry_future = executor.submit( + task.execute + ) + future_to_task[retry_future] = ( + task, + attempt + 1, + ) + else: + logging.error( + f"Task {task} failed after" + f" {self.retry_attempts} attempts." + ) + + return results + + def _autosave_task_result(self, task: Task, result): + """ + Autosaves the result of a task. + + Args: + task (Task): The task whose result needs to be autosaved. + result: The result of the task. + + """ + with self.lock: + logging.info( + f"Autosaving result for task {task}: {result}" + ) + # Actual autosave logic goes here diff --git a/swarms/structs/swarm_redis_registry.py b/swarms/structs/swarm_redis_registry.py new file mode 100644 index 00000000..a17549cd --- /dev/null +++ b/swarms/structs/swarm_redis_registry.py @@ -0,0 +1,176 @@ +from dataclasses import asdict +from typing import List + +import networkx as nx +import redis +from redis.commands.graph import Graph, Node + +from swarms.structs.agent import Agent +from swarms.structs.base_swarm import AbstractSwarm + + +class SwarmRelationship: + JOINED = "joined" + + +class RedisSwarmRegistry(AbstractSwarm): + """ + Initialize the SwarmRedisRegistry object. + + Args: + host (str): The hostname or IP address of the Redis server. Default is "localhost". + port (int): The port number of the Redis server. Default is 6379. + db: The Redis database number. Default is 0. + graph_name (str): The name of the RedisGraph graph. Default is "swarm_registry". + """ + + def __init__( + self, + host: str = "localhost", + port: int = 6379, + db=0, + graph_name: str = "swarm_registry", + ): + self.redis = redis.StrictRedis( + host=host, port=port, db=db, decode_responses=True + ) + self.redis_graph = Graph(self.redis, graph_name) + self.graph = nx.DiGraph() + + def _entity_to_node(self, entity: Agent | Agent) -> Node: + """ + Converts an Agent or Swarm object to a Node object. + + Args: + entity (Agent | Agent): The Agent or Swarm object to convert. + + Returns: + Node: The converted Node object. + """ + return Node( + node_id=entity.id, + alias=entity.agent_name, + label=entity.agent_description, + properties=asdict(entity), + ) + + def _add_node(self, node: Agent | Agent): + """ + Adds a node to the graph. + + Args: + node (Agent | Agent): The Agent or Swarm node to add. + """ + self.graph.add_node(node.id) + if isinstance(node, Agent): + self.add_swarm_entry(node) + elif isinstance(node, Agent): + self.add_agent_entry(node) + + def _add_edge(self, from_node: Node, to_node: Node, relationship): + """ + Adds an edge between two nodes in the graph. + + Args: + from_node (Node): The source node of the edge. + to_node (Node): The target node of the edge. + relationship: The relationship type between the nodes. + """ + match_query = ( + f"MATCH (a:{from_node.label}),(b:{to_node.label}) WHERE" + f" a.id = {from_node.id} AND b.id = {to_node.id}" + ) + + query = f""" + {match_query} + CREATE (a)-[r:joined]->(b) RETURN r + """.replace("\n", "") + + self.redis_graph.query(query) + + def add_swarm_entry(self, swarm: Agent): + """ + Adds a swarm entry to the graph. + + Args: + swarm (Agent): The swarm object to add. + """ + node = self._entity_to_node(swarm) + self._persist_node(node) + + def add_agent_entry(self, agent: Agent): + """ + Adds an agent entry to the graph. + + Args: + agent (Agent): The agent object to add. + """ + node = self._entity_to_node(agent) + self._persist_node(node) + + def join_swarm( + self, + from_entity: Agent | Agent, + to_entity: Agent, + ): + """ + Adds an edge between two nodes in the graph. + + Args: + from_entity (Agent | Agent): The source entity of the edge. + to_entity (Agent): The target entity of the edge. + + Returns: + Any: The result of adding the edge. + """ + from_node = self._entity_to_node(from_entity) + to_node = self._entity_to_node(to_entity) + + return self._add_edge( + from_node, to_node, SwarmRelationship.JOINED + ) + + def _persist_node(self, node: Node): + """ + Persists a node in the graph. + + Args: + node (Node): The node to persist. + """ + query = f"CREATE {node}" + self.redis_graph.query(query) + + def retrieve_swarm_information(self, swarm_id: int) -> Agent: + """ + Retrieves swarm information from the registry. + + Args: + swarm_id (int): The ID of the swarm to retrieve. + + Returns: + Agent: The retrieved swarm information as an Agent object. + """ + swarm_key = f"swarm:{swarm_id}" + swarm_data = self.redis.hgetall(swarm_key) + if swarm_data: + # Parse the swarm_data and return an instance of AgentBase + # You can use the retrieved data to populate the AgentBase attributes + + return Agent(**swarm_data) + return None + + def retrieve_joined_agents(self) -> List[Agent]: + """ + Retrieves a list of joined agents from the registry. + + Returns: + List[Agent]: The retrieved joined agents as a list of Agent objects. + """ + agent_data = self.redis_graph.query( + "MATCH (a:agent)-[:joined]->(b:manager) RETURN a" + ) + if agent_data: + # Parse the agent_data and return an instance of AgentBase + # You can use the retrieved data to populate the AgentBase attributes + return [Agent(**agent_data) for agent_data in agent_data] + return None From 1c1dc3843aff18254a0c27958787657a8bb620b1 Mon Sep 17 00:00:00 2001 From: Kye Date: Sat, 17 Feb 2024 14:52:55 -0800 Subject: [PATCH 011/105] [CLEANUP] --- multion_agent.py => playground/agents/multion_agent.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename multion_agent.py => playground/agents/multion_agent.py (97%) diff --git a/multion_agent.py b/playground/agents/multion_agent.py similarity index 97% rename from multion_agent.py rename to playground/agents/multion_agent.py index 132ccb87..b3f10c8e 100644 --- a/multion_agent.py +++ b/playground/agents/multion_agent.py @@ -65,7 +65,7 @@ class MultiOnAgent(AbstractLLM): # model model = MultiOnAgent( - multion_api_key="535ae401948b4c59bc1b2c61eec90fe6" + multion_api_key="" ) # out = model.run("search for a recipe") From a4ac29553c2c98f05631aa457444e22292e150ad Mon Sep 17 00:00:00 2001 From: Wyatt Stanke Date: Sat, 17 Feb 2024 20:39:06 -0500 Subject: [PATCH 012/105] Update PDF library from PyPDF2 to pypdf --- docs/swarms/utils/pdf_to_text.md | 13 +++++++------ pyproject.toml | 3 +-- requirements.txt | 2 +- swarms/loaders/pdf_loader.py | 2 +- swarms/utils/pdf_to_text.py | 8 ++++---- tests/utils/test_pdf_to_text.py | 6 +++--- 6 files changed, 17 insertions(+), 17 deletions(-) diff --git a/docs/swarms/utils/pdf_to_text.md b/docs/swarms/utils/pdf_to_text.md index aecde1a9..246ac931 100644 --- a/docs/swarms/utils/pdf_to_text.md +++ b/docs/swarms/utils/pdf_to_text.md @@ -1,7 +1,8 @@ # pdf_to_text -## Introduction -The function `pdf_to_text` is a Python utility for converting a PDF file into a string of text content. It leverages the `PyPDF2` library, an excellent Python library for processing PDF files. The function takes in a PDF file's path and reads its content, subsequently returning the extracted textual data. +## Introduction + +The function `pdf_to_text` is a Python utility for converting a PDF file into a string of text content. It leverages the `pypdf` library, an excellent Python library for processing PDF files. The function takes in a PDF file's path and reads its content, subsequently returning the extracted textual data. This function can be very useful when you want to extract textual information from PDF files automatically. For instance, when processing a large number of documents, performing textual analysis, or when you're dealing with text data that is only available in PDF format. @@ -34,14 +35,14 @@ def pdf_to_text(pdf_path: str) -> str: ## Function Description -`pdf_to_text` utilises the `PdfReader` function from the `PyPDF2` library to read the PDF file. If the PDF file does not exist at the specified path or there was an error while reading the file, appropriate exceptions will be raised. It then iterates through each page in the PDF and uses the `extract_text` function to extract the text content from each page. These contents are then concatenated into a single variable and returned as the result. +`pdf_to_text` utilises the `PdfReader` function from the `pypdf` library to read the PDF file. If the PDF file does not exist at the specified path or there was an error while reading the file, appropriate exceptions will be raised. It then iterates through each page in the PDF and uses the `extract_text` function to extract the text content from each page. These contents are then concatenated into a single variable and returned as the result. ## Usage Examples -To use this function, you first need to install the `PyPDF2` library. It can be installed via pip: +To use this function, you first need to install the `pypdf` library. It can be installed via pip: ```python -!pip install pypdf2 +!pip install pypdf ``` Then, you should import the `pdf_to_text` function: @@ -68,4 +69,4 @@ print(text) - This function reads the text from the PDF. It does not handle images, graphical elements, or any non-text content. - If the PDF contains scanned images rather than textual data, the `extract_text` function may not be able to extract any text. In such cases, you would require OCR (Optical Character Recognition) tools to extract the text. - Be aware of the possibility that the output string might contain special characters or escape sequences because they were part of the PDF's content. You might need to clean the resulting text according to your requirements. -- The function uses the PyPDF2 library to facilitate the PDF reading and text extraction. For any issues related to PDF manipulation, consult the [PyPDF2 library documentation](https://pythonhosted.org/PyPDF2/). +- The function uses the pypdf library to facilitate the PDF reading and text extraction. For any issues related to PDF manipulation, consult the [pypdf library documentation](https://pythonhosted.org/pypdf/). diff --git a/pyproject.toml b/pyproject.toml index 90ab06e6..58ca7f59 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -43,7 +43,7 @@ datasets = "*" optimum = "1.15.0" diffusers = "*" toml = "*" -PyPDF2 = "3.0.1" +pypdf = "4.0.1" accelerate = "*" anthropic = "*" sentencepiece = "0.1.98" @@ -77,7 +77,6 @@ pinecone-client = "*" roboflow = "*" - [tool.poetry.group.lint.dependencies] ruff = ">=0.0.249,<0.1.7" types-toml = "^0.10.8.1" diff --git a/requirements.txt b/requirements.txt index e6496205..e582fa25 100644 --- a/requirements.txt +++ b/requirements.txt @@ -14,7 +14,7 @@ huggingface-hub google-generativeai==0.3.1 sentencepiece==0.1.98 requests_mock -PyPDF2==3.0.1 +pypdf==4.0.1 accelerate==0.22.0 chromadb tensorflow diff --git a/swarms/loaders/pdf_loader.py b/swarms/loaders/pdf_loader.py index f3db1448..17e0b465 100644 --- a/swarms/loaders/pdf_loader.py +++ b/swarms/loaders/pdf_loader.py @@ -4,7 +4,7 @@ from dataclasses import dataclass from pathlib import Path from typing import IO, Dict, List, Optional -from PyPDF2 import PdfReader +from pypdf import PdfReader from swarms.utils.hash import str_to_hash diff --git a/swarms/utils/pdf_to_text.py b/swarms/utils/pdf_to_text.py index 6d589ad5..4877f3b1 100644 --- a/swarms/utils/pdf_to_text.py +++ b/swarms/utils/pdf_to_text.py @@ -1,11 +1,11 @@ import sys try: - import PyPDF2 + import pypdf except ImportError: print( - "PyPDF2 not installed. Please install it using: pip install" - " PyPDF2" + "pypdf not installed. Please install it using: pip install" + " pypdf" ) sys.exit(1) @@ -27,7 +27,7 @@ def pdf_to_text(pdf_path): try: # Open the PDF file with open(pdf_path, "rb") as file: - pdf_reader = PyPDF2.PdfReader(file) + pdf_reader = pypdf.PdfReader(file) text = "" # Iterate through each page and extract text diff --git a/tests/utils/test_pdf_to_text.py b/tests/utils/test_pdf_to_text.py index 57e3b33f..888ed764 100644 --- a/tests/utils/test_pdf_to_text.py +++ b/tests/utils/test_pdf_to_text.py @@ -1,12 +1,12 @@ import pytest -import PyPDF2 +import pypdf from swarms.utils import pdf_to_text @pytest.fixture def pdf_file(tmpdir): - pdf_writer = PyPDF2.PdfWriter() - pdf_page = PyPDF2.pdf.PageObject.createBlankPage(None, 200, 200) + pdf_writer = pypdf.PdfWriter() + pdf_page = pypdf.pdf.PageObject.createBlankPage(None, 200, 200) pdf_writer.add_page(pdf_page) pdf_file = tmpdir.join("temp.pdf") with open(pdf_file, "wb") as output: From b12ec024df91ac0f8f3a78a1364e439b16fad9cc Mon Sep 17 00:00:00 2001 From: Wyatt Stanke Date: Sat, 17 Feb 2024 20:42:04 -0500 Subject: [PATCH 013/105] Fix accedental formatting change --- docs/swarms/utils/pdf_to_text.md | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/swarms/utils/pdf_to_text.md b/docs/swarms/utils/pdf_to_text.md index 246ac931..4ff6c8fc 100644 --- a/docs/swarms/utils/pdf_to_text.md +++ b/docs/swarms/utils/pdf_to_text.md @@ -1,7 +1,6 @@ # pdf_to_text ## Introduction - The function `pdf_to_text` is a Python utility for converting a PDF file into a string of text content. It leverages the `pypdf` library, an excellent Python library for processing PDF files. The function takes in a PDF file's path and reads its content, subsequently returning the extracted textual data. This function can be very useful when you want to extract textual information from PDF files automatically. For instance, when processing a large number of documents, performing textual analysis, or when you're dealing with text data that is only available in PDF format. From d6951faf68f3f2a0b6129f695eed5295799f1f25 Mon Sep 17 00:00:00 2001 From: Wyatt Stanke Date: Sat, 17 Feb 2024 20:42:18 -0500 Subject: [PATCH 014/105] Wrong docs site --- docs/swarms/utils/pdf_to_text.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/swarms/utils/pdf_to_text.md b/docs/swarms/utils/pdf_to_text.md index 4ff6c8fc..e5b8dce0 100644 --- a/docs/swarms/utils/pdf_to_text.md +++ b/docs/swarms/utils/pdf_to_text.md @@ -68,4 +68,4 @@ print(text) - This function reads the text from the PDF. It does not handle images, graphical elements, or any non-text content. - If the PDF contains scanned images rather than textual data, the `extract_text` function may not be able to extract any text. In such cases, you would require OCR (Optical Character Recognition) tools to extract the text. - Be aware of the possibility that the output string might contain special characters or escape sequences because they were part of the PDF's content. You might need to clean the resulting text according to your requirements. -- The function uses the pypdf library to facilitate the PDF reading and text extraction. For any issues related to PDF manipulation, consult the [pypdf library documentation](https://pythonhosted.org/pypdf/). +- The function uses the pypdf library to facilitate the PDF reading and text extraction. For any issues related to PDF manipulation, consult the [pypdf library documentation](https://pypdf.readthedocs.io/en/stable/). From 9e00945ad283e1497a10a2f720aa10bacf21bfb9 Mon Sep 17 00:00:00 2001 From: Wyatt Stanke Date: Sat, 17 Feb 2024 20:43:08 -0500 Subject: [PATCH 015/105] Oops --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index 58ca7f59..7e05820b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -77,6 +77,7 @@ pinecone-client = "*" roboflow = "*" + [tool.poetry.group.lint.dependencies] ruff = ">=0.0.249,<0.1.7" types-toml = "^0.10.8.1" From 546e69d8d4751067f2de7d52ce0edf80217d2811 Mon Sep 17 00:00:00 2001 From: Kye Date: Sun, 18 Feb 2024 02:21:00 -0800 Subject: [PATCH 016/105] [DOCS FIX] [BUGF][Agent] --- docs/swarms/structs/majorityvoting.md | 31 +-------- docs/swarms/structs/taskqueuebase.md | 10 +-- playground/agents/multion_agent.py | 4 +- playground/agents/perimeter_defense_agent.py | 70 ++++++++++++++++++++ swarms/structs/agent.py | 47 +++++++++---- 5 files changed, 107 insertions(+), 55 deletions(-) create mode 100644 playground/agents/perimeter_defense_agent.py diff --git a/docs/swarms/structs/majorityvoting.md b/docs/swarms/structs/majorityvoting.md index 2707a217..97d28847 100644 --- a/docs/swarms/structs/majorityvoting.md +++ b/docs/swarms/structs/majorityvoting.md @@ -1,6 +1,4 @@ -Due to the limitations of this platform and the scope of your request, I am unable to create a full 10,000-word documentation here. However, I can provide a structured outline for a comprehensive documentation guide that you could expand upon offline. - -# swarms.structs Documentation +# `MajorityVoting` Documentation ## Overview @@ -43,15 +41,6 @@ The `MajorityVoting` class is a high-level abstraction used to coordinate a grou ### Class Definition -```python -class MajorityVoting: - def __init__(self, agents, concurrent=False, multithreaded=False, multiprocess=False, asynchronous=False, output_parser=None, autosave=False, verbose=False, *args, **kwargs): - pass - - def run(self, task, *args, **kwargs): - pass -``` - ### Parameters | Parameter | Type | Default | Description | @@ -118,21 +107,3 @@ result = majority_voting.run("What is the square root of 16?") print(result) # Output: 4 ``` -## Advanced Features - -Detailed instructions on how to use multithreading, multiprocessing, asynchronous execution, and how to parse the output with custom functions would be included in this section. - -## Troubleshooting and FAQ - -This section would cover common problems and questions related to the `swarms.structs` library. - -## Conclusion - -A summary of the `swarms.structs` library's capabilities and potential applications in various domains. - -## References - -Links to external documentation, source code repository, and any further reading regarding swarms or collective decision-making algorithms. - ---- -**Note:** Expand on each section by incorporating explanations, additional code examples, and in-depth descriptions of how the underlying mechanisms work for each method and functionality provided by the `MajorityVoting` class. Consider adding visual aids such as flowcharts or diagrams where appropriate. diff --git a/docs/swarms/structs/taskqueuebase.md b/docs/swarms/structs/taskqueuebase.md index a3eefc1c..a8d4c599 100644 --- a/docs/swarms/structs/taskqueuebase.md +++ b/docs/swarms/structs/taskqueuebase.md @@ -1,8 +1,5 @@ -Due to the limitations of the platform, it's not possible to create documentation as long and detailed as 10,000 words within a single response. However, I can provide you with an outline and the starting point for a comprehensive and professional documentation in markdown format for the `TaskQueueBase` class according to the steps provided. -Here is the template you can follow to expand upon: - -# swarms.structs Documentation +# `TaskQueueBase` ## Introduction The `swarms.structs` library is a key component of a multi-agent system's task management infrastructure. It provides the necessary classes and methods to create and manage queues of tasks that can be distributed among a swarm of agents. The purpose of this documentation is to guide users through the proper use of the `TaskQueueBase` class, which serves as an abstract base class for implementing task queues. @@ -128,8 +125,3 @@ This section would provide insights on thread safety, error handling, and best p Links to further resources and any academic papers or external documentation related to task queues and multi-agent systems would be included here. ---- - -Please note that this is just an outline of the structure and beginning of the documentation. For a full documentation, expand each section to include detail_sy examples, considerations for thread safety, performance implications, and subtleties of the implementation. You can also add a FAQ section, troubleshooting guide, and any benchmarks if available. - -Remember, each method should be thoroughly explained with explicit examples that include handling successes and failures, as well as edge cases that might be encountered. The documentation should also consider various environments where the `TaskQueueBase` class may be used, such as different operating systems, and Python environments (i.e. CPython vs. PyPy). diff --git a/playground/agents/multion_agent.py b/playground/agents/multion_agent.py index b3f10c8e..230f098b 100644 --- a/playground/agents/multion_agent.py +++ b/playground/agents/multion_agent.py @@ -64,9 +64,7 @@ class MultiOnAgent(AbstractLLM): # model -model = MultiOnAgent( - multion_api_key="" -) +model = MultiOnAgent(multion_api_key="") # out = model.run("search for a recipe") agent = Agent( diff --git a/playground/agents/perimeter_defense_agent.py b/playground/agents/perimeter_defense_agent.py new file mode 100644 index 00000000..3f5480c6 --- /dev/null +++ b/playground/agents/perimeter_defense_agent.py @@ -0,0 +1,70 @@ +import os +from dotenv import load_dotenv +from swarms.models import GPT4VisionAPI +from swarms.structs import Agent +import swarms.prompts.security_team as stsp + +# Load environment variables and initialize the Vision API +load_dotenv() +api_key = os.getenv("OPENAI_API_KEY") + +llm = GPT4VisionAPI(openai_api_key=api_key) + +# Image for analysis +img = "bank_robbery.jpg" + +# Initialize agents with respective prompts for security tasks +crowd_analysis_agent = Agent( + llm=llm, + sop=stsp.CROWD_ANALYSIS_AGENT_PROMPT, + max_loops=1, + multi_modal=True, +) + +weapon_detection_agent = Agent( + llm=llm, + sop=stsp.WEAPON_DETECTION_AGENT_PROMPT, + max_loops=1, + multi_modal=True, +) + +surveillance_monitoring_agent = Agent( + llm=llm, + sop=stsp.SURVEILLANCE_MONITORING_AGENT_PROMPT, + max_loops=1, + multi_modal=True, +) + +emergency_response_coordinator = Agent( + llm=llm, + sop=stsp.EMERGENCY_RESPONSE_COORDINATOR_PROMPT, + max_loops=1, + multi_modal=True, +) + +# Run agents with respective tasks on the same image +crowd_analysis = crowd_analysis_agent.run( + "Analyze the crowd dynamics in the scene", img +) + +weapon_detection_analysis = weapon_detection_agent.run( + "Inspect the scene for any potential threats", img +) + +surveillance_monitoring_analysis = surveillance_monitoring_agent.run( + "Monitor the overall scene for unusual activities", img +) + +emergency_response_analysis = emergency_response_coordinator.run( + "Develop a response plan based on the scene analysis", img +) + +# Process and output results for each task +# Example output (uncomment to use): +print(f"Crowd Analysis: {crowd_analysis}") +print(f"Weapon Detection Analysis: {weapon_detection_analysis}") +print( + "Surveillance Monitoring Analysis:" + f" {surveillance_monitoring_analysis}" +) +print(f"Emergency Response Analysis: {emergency_response_analysis}") diff --git a/swarms/structs/agent.py b/swarms/structs/agent.py index 4192aab3..16109dab 100644 --- a/swarms/structs/agent.py +++ b/swarms/structs/agent.py @@ -29,6 +29,7 @@ from swarms.utils.pdf_to_text import pdf_to_text from swarms.utils.token_count_tiktoken import limit_tokens_from_string from swarms.tools.exec_tool import execute_tool_by_name from swarms.prompts.worker_prompt import worker_tools_sop_promp +from swarms.structs.schemas import Step # Utils @@ -50,6 +51,14 @@ def agent_id(): return str(uuid.uuid4()) +def task_id(): + return str(uuid.uuid4()) + + +def step_id(): + return str(uuid.uuid1()) + + class Agent: """ Agent is the backbone to connect LLMs with tools and long term memory. Agent also provides the ability to @@ -296,6 +305,9 @@ class Agent: # Initialize the llm with the conditional variables # self.llm = llm(*args, **kwargs) + # Step cache + self.step_cache = [] + def set_system_prompt(self, system_prompt: str): """Set the system prompt""" self.system_prompt = system_prompt @@ -522,7 +534,7 @@ class Agent: # Activate Autonomous agent message self.activate_autonomous_agent() - response = task # or combined_prompt + # response = task # or combined_prompt history = self._history(self.user_name, task) # If dashboard = True then print the dashboard @@ -541,20 +553,13 @@ class Agent: self.loop_count_print(loop_count, self.max_loops) print("\n") - # Check to see if stopping token is in the output to stop the loop - if self.stopping_token: - if self._check_stopping_condition( - response - ) or parse_done_token(response): - break - # Adjust temperature, comment if no work if self.dynamic_temperature_enabled: print(colored("Adjusting temperature...", "blue")) self.dynamic_temperature() # Preparing the prompt - task = self.agent_history_prompt(history=response) + task = self.agent_history_prompt(history=task) attempt = 0 while attempt < self.retry_attempts: @@ -573,6 +578,24 @@ class Agent: ) print(response) + # Log each step + step = Step( + input=task, + task_id=task_id, + step_id=step_id, + output=response, + ) + + # Check to see if stopping token is in the output to stop the loop + if self.stopping_token: + if self._check_stopping_condition( + response + ) or parse_done_token(response): + break + + self.step_cache.append(step) + logging.info(f"Step: {step}") + # If parser exists then parse the response if self.parser: response = self.parser(response) @@ -692,10 +715,8 @@ class Agent: else: system_prompt = self.system_prompt agent_history_prompt = f""" - SYSTEM_PROMPT: {system_prompt} - - - ################ CHAT HISTORY #################### + System : {system_prompt} + {history} """ return agent_history_prompt From e40bf50195b25b12f85165fb1f0e66af6112c24c Mon Sep 17 00:00:00 2001 From: Wyatt Stanke Date: Sun, 18 Feb 2024 09:47:28 -0500 Subject: [PATCH 017/105] Fix tests --- tests/utils/test_pdf_to_text.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/utils/test_pdf_to_text.py b/tests/utils/test_pdf_to_text.py index 888ed764..704f7449 100644 --- a/tests/utils/test_pdf_to_text.py +++ b/tests/utils/test_pdf_to_text.py @@ -6,7 +6,7 @@ from swarms.utils import pdf_to_text @pytest.fixture def pdf_file(tmpdir): pdf_writer = pypdf.PdfWriter() - pdf_page = pypdf.pdf.PageObject.createBlankPage(None, 200, 200) + pdf_page = pypdf.PageObject.create_blank_page(None, 200, 200) pdf_writer.add_page(pdf_page) pdf_file = tmpdir.join("temp.pdf") with open(pdf_file, "wb") as output: From 3dc8a4f4448e0b30cccfd1a72f9c84d73e050ecc Mon Sep 17 00:00:00 2001 From: Kye Date: Tue, 20 Feb 2024 17:01:28 -0800 Subject: [PATCH 018/105] [CLEANUP OPERATION] --- README.md | 147 +++++------- docs/applications/discord.md | 2 + docs/examples/bingchat.md | 1 + docs/examples/flow.md | 11 +- docs/examples/omni_agent.md | 2 +- docs/examples/revgpt.md | 1 - docs/examples/stacked_worker.md | 10 +- docs/examples/worker.md | 6 +- docs/swarms/agents/abstractagent.md | 7 +- docs/swarms/agents/message.md | 16 +- docs/swarms/agents/omni_agent.md | 6 +- docs/swarms/agents/toolagent.md | 8 +- docs/swarms/agents/workeragent.md | 12 +- docs/swarms/chunkers/basechunker.md | 4 +- docs/swarms/chunkers/pdf_chunker.md | 4 +- docs/swarms/index.md | 9 +- docs/swarms/memory/pg.md | 39 ++-- docs/swarms/memory/pinecone.md | 11 +- docs/swarms/memory/qdrant.md | 16 +- docs/swarms/memory/short_term_memory.md | 4 +- docs/swarms/memory/weaviate.md | 27 +-- docs/swarms/models/anthropic.md | 4 +- docs/swarms/models/base_multimodal_model.md | 10 +- docs/swarms/models/bingchat.md | 9 +- docs/swarms/models/biogpt.md | 2 - docs/swarms/models/distilled_whisperx.md | 8 +- docs/swarms/models/gpt4v.md | 14 +- docs/swarms/models/huggingface.md | 6 +- docs/swarms/models/idefics.md | 8 +- docs/swarms/models/index.md | 8 +- docs/swarms/models/kosmos.md | 53 ++++- docs/swarms/models/mistral.md | 1 - docs/swarms/models/mpt.md | 11 +- docs/swarms/models/openai.md | 8 +- docs/swarms/models/openai_chat.md | 6 +- docs/swarms/models/vilt.md | 20 +- docs/swarms/models/vllm.md | 6 +- docs/swarms/models/zephyr.md | 1 + docs/swarms/structs/abstractswarm.md | 6 +- docs/swarms/structs/agent.md | 10 +- docs/swarms/structs/artifact.md | 9 +- docs/swarms/structs/artifactupload.md | 10 +- docs/swarms/structs/autoscaler.md | 9 +- docs/swarms/structs/baseworkflow.md | 22 +- docs/swarms/structs/groupchatmanager.md | 6 +- docs/swarms/structs/json.md | 13 +- docs/swarms/structs/majorityvoting.md | 2 + docs/swarms/structs/nonlinearworkflow.md | 4 +- docs/swarms/structs/sequential_workflow.md | 16 +- docs/swarms/structs/stackoverflowswarm.md | 2 + docs/swarms/structs/stepinput.md | 17 +- docs/swarms/structs/swarmnetwork.md | 10 + docs/swarms/structs/task.md | 3 +- docs/swarms/structs/taskinput.md | 9 + docs/swarms/structs/taskqueuebase.md | 15 +- docs/swarms/tokenizers/basetokenizer.md | 4 +- docs/swarms/tokenizers/coheretokenizer.md | 7 +- .../swarms/tokenizers/huggingfacetokenizer.md | 2 +- docs/swarms/tokenizers/openaitokenizer.md | 6 +- .../tokenizers/sentencepiecetokenizer.md | 4 +- docs/swarms/utils/check_device.md | 20 +- .../utils/extract_code_from_markdown.md | 16 +- docs/swarms/utils/find_image_path.md | 14 +- docs/swarms/utils/limit_tokens_from_string.md | 2 +- docs/swarms/utils/load_model_torch.md | 5 +- docs/swarms/utils/math_eval.md | 7 +- docs/swarms/utils/metrics_decorator.md | 1 + docs/swarms/utils/pdf_to_text.md | 2 +- docs/swarms/utils/prep_torch_inference.md | 3 +- docs/swarms/utils/print_class_parameters.md | 12 +- docs/swarms/workers/base.md | 62 +++-- docs/swarms/workers/index.md | 10 +- .../agents/multi_modal_auto_agent_example.py | 2 + playground/agents/multion_agent.py | 3 +- playground/agents/perimeter_defense_agent.py | 4 +- playground/agents/simple_agent_example.py | 3 +- playground/agents/tool_agent.py | 1 + playground/agents/worker_example.py | 4 +- .../accountant_team/account_team2_example.py | 2 + playground/demos/ad_gen/ad_gen_example.py | 6 +- playground/demos/assembly/assembly_example.py | 2 +- playground/demos/autotemp/autotemp_example.py | 1 + playground/demos/autotemp/blog_gen_example.py | 4 +- .../demos/developer_swarm/main_example.py | 3 +- .../demos/education/education_example.py | 6 +- .../gemini_chat_example.py | 2 + .../gemini_react_example.py | 2 + playground/demos/grupa/app_example.py | 4 +- .../jarvis_example.py | 3 +- .../langchain_example/langchain_example.py | 4 +- .../demos/logistics/logistics_example.py | 8 +- .../multi_modal_auto_agent_example.py | 3 +- .../demos/multimodal_tot/main_example.py | 8 +- .../demos/nutrition/nutrition_example.py | 4 +- .../better_communication_example.py | 2 +- .../personal_stylist_example.py | 10 +- .../positive_med/positive_med_example.py | 1 + playground/demos/security_team/IMG_1625.MOV | Bin 0 -> 6932435 bytes .../security_team/security_team_example.py | 44 ++-- playground/demos/simple_rag/simple_rag.py | 2 +- .../main_example.py | 7 +- .../urban_planning/urban_planning_example.py | 6 +- playground/diy/hierchical_example.py | 1 - playground/memory/qdrant/usage_example.py | 1 + playground/models/anthropic_example.py | 1 - playground/models/bingchat_example.py | 7 +- playground/models/cohere_example.py | 1 - .../models/dalle3_concurrent_example.py | 8 +- .../models/distilled_whiserpx_example.py | 1 + playground/models/gemini_example.py | 2 + playground/models/gpt4_v_example.py | 1 - playground/models/miqu.py | 1 - playground/models/roboflow_example.py | 1 - playground/models/tts_speech_example.py | 4 +- playground/structs/agent_with_longterm.py | 2 +- .../structs/agent_with_tools_example.py | 12 +- playground/structs/autoscaler_example.py | 1 - .../structs/concurrent_workflow_example.py | 4 +- playground/structs/debate_example.py | 6 +- .../structs/dialogue_simulator_example.py | 2 +- playground/structs/groupchat_example.py | 5 +- playground/structs/gui_app_example.py | 1 - playground/structs/majority_voting.py | 2 +- .../structs/multi_agent_debate_example.py | 2 +- .../structs/multi_modal_flow_example.py | 2 +- playground/structs/multi_modal_rag_agent.py | 6 +- playground/structs/orchestrate_example.py | 2 +- playground/structs/orchestrator_example.py | 2 +- playground/structs/recursive_example.py | 4 +- .../structs/sequential_workflow_example.py | 2 +- playground/structs/swarm_network_example.py | 2 +- playground/structs/todo_app_example.py | 1 - playground/structs/workflow_example.py | 3 +- playground/swarms_example.ipynb | 217 +++++++++--------- playground/tools/agent_with_tools_example.py | 4 +- scripts/auto_tests_docs/auto_docs.py | 11 +- scripts/auto_tests_docs/auto_docs_omni.py | 2 +- scripts/auto_tests_docs/auto_tests.py | 9 +- scripts/auto_tests_docs/update_mkdocs.py | 2 +- scripts/get_package_requirements.py | 2 +- scripts/requirementstxt_to_pyproject.py | 4 +- swarms/__init__.py | 16 +- swarms/agents/__init__.py | 2 +- swarms/agents/base.py | 1 - swarms/agents/developer_agents.py | 4 +- swarms/agents/omni_modal_agent.py | 3 +- swarms/agents/simple_agent.py | 7 +- swarms/artifacts/base_artifact.py | 3 +- swarms/artifacts/text_artifact.py | 7 +- swarms/chunkers/__init__.py | 2 +- swarms/chunkers/base_chunker.py | 5 +- swarms/cli/_cli.py | 1 + swarms/cli/run_file.py | 2 +- swarms/loaders/pdf_loader.py | 16 +- swarms/memory/__init__.py | 12 +- swarms/memory/base_db.py | 20 -- swarms/memory/base_vectordb.py | 18 -- swarms/memory/chroma_db.py | 7 +- swarms/memory/cosine_similarity.py | 1 + swarms/memory/dict_shared_memory.py | 6 +- swarms/memory/lanchain_chroma.py | 3 +- swarms/memory/pg.py | 1 + swarms/memory/pinecone.py | 4 +- swarms/memory/short_term_memory.py | 7 +- swarms/memory/sqlite.py | 3 +- swarms/memory/utils.py | 2 +- swarms/memory/visual_memory.py | 2 +- swarms/models/__init__.py | 107 +++++---- swarms/models/anthropic.py | 6 +- swarms/models/base_embedding_model.py | 9 +- swarms/models/base_llm.py | 2 - swarms/models/base_multimodal_model.py | 3 - swarms/models/base_tts.py | 6 +- swarms/models/base_ttv.py | 10 +- swarms/models/biogpt.py | 6 +- swarms/models/chest_agent.py | 2 + swarms/models/cog_agent.py | 3 +- swarms/models/cohere_chat.py | 19 +- swarms/models/embeddings_base.py | 1 + swarms/models/gemini.py | 1 - swarms/models/gpt4_sam.py | 3 +- swarms/models/gpt4_vision_api.py | 5 +- swarms/models/huggingface_pipeline.py | 3 +- swarms/models/idefics.py | 6 +- swarms/models/jina_embeds.py | 3 +- swarms/models/kosmos_two.py | 2 +- swarms/models/layoutlm_document_qa.py | 4 +- swarms/models/llama_function_caller.py | 5 +- swarms/models/llava.py | 6 +- swarms/models/mistral.py | 2 +- swarms/models/mixtral.py | 2 + swarms/models/model_registry.py | 2 +- swarms/models/mpt.py | 3 +- swarms/models/nougat.py | 2 + swarms/models/odin.py | 6 +- swarms/models/open_dalle.py | 3 +- swarms/models/openai_embeddings.py | 91 ++++---- swarms/models/openai_models.py | 194 ++++++++-------- swarms/models/palm.py | 18 +- swarms/models/petals.py | 2 +- swarms/models/sam.py | 5 +- swarms/models/sam_supervision.py | 9 +- swarms/models/sampling_params.py | 3 +- swarms/models/speecht5.py | 9 +- swarms/models/ssd_1b.py | 2 +- swarms/models/stable_diffusion.py | 7 +- swarms/models/timm.py | 1 + swarms/models/types.py | 3 +- swarms/models/ultralytics_model.py | 6 +- swarms/models/vllm.py | 1 + swarms/models/zephyr.py | 1 + swarms/prompts/__init__.py | 2 +- swarms/prompts/base.py | 10 +- swarms/prompts/chat_prompt.py | 18 +- swarms/structs/SWARMS.md | 2 +- swarms/structs/__init__.py | 39 ++-- swarms/structs/agent.py | 34 +-- swarms/structs/async_workflow.py | 1 + swarms/structs/autoscaler.py | 4 +- swarms/structs/base.py | 12 +- swarms/structs/base_multiagent_structure.py | 4 +- swarms/structs/base_swarm.py | 28 --- swarms/structs/base_workflow.py | 3 +- swarms/structs/blocksdict.py | 6 +- swarms/structs/blockslist.py | 6 +- swarms/structs/company.py | 2 +- swarms/structs/concurrent_workflow.py | 3 +- swarms/structs/conversation.py | 6 +- swarms/structs/debate.py | 10 +- swarms/structs/graph_workflow.py | 5 +- swarms/structs/groupchat.py | 1 + swarms/structs/load_balancer.py | 3 +- swarms/structs/long_swarm.py | 153 ++++++++++++ swarms/structs/model_parallizer.py | 2 +- swarms/structs/multi_agent_collab.py | 2 +- swarms/structs/multi_threaded_workflow.py | 7 +- swarms/structs/nonlinear_workflow.py | 2 +- swarms/structs/recursive_workflow.py | 3 +- swarms/structs/schemas.py | 26 +-- swarms/structs/sequential_workflow.py | 2 +- swarms/structs/stackoverflow_swarm.py | 4 +- swarms/structs/swarming_architectures.py | 3 +- swarms/structs/task.py | 10 +- swarms/structs/task_queue_base.py | 1 + swarms/structs/test_majority_voting.py | 1 + swarms/structs/tool_json_schema.py | 3 +- swarms/structs/utils.py | 5 +- swarms/telemetry/__init__.py | 2 +- swarms/telemetry/auto_upgrade_swarms.py | 1 + swarms/telemetry/bootup.py | 2 +- swarms/telemetry/check_update.py | 6 +- swarms/telemetry/main.py | 5 +- swarms/telemetry/sys_info.py | 2 +- swarms/telemetry/user_utils.py | 7 +- swarms/tokenizers/__init__.py | 16 +- swarms/tokenizers/anthropic_tokenizer.py | 3 +- swarms/tokenizers/base_tokenizer.py | 7 +- swarms/tokenizers/cohere_tokenizer.py | 1 + swarms/tokenizers/openai_tokenizers.py | 10 +- swarms/tokenizers/r_tokenizers.py | 8 +- swarms/tools/__init__.py | 20 +- swarms/tools/code_executor.py | 2 +- swarms/tools/format_tools.py | 9 +- swarms/tools/logits_processor.py | 4 +- swarms/tools/tool.py | 164 ++++++------- swarms/tools/tool_func_doc_scraper.py | 1 + swarms/tools/tool_utils.py | 4 +- swarms/utils/README.md | 21 +- swarms/utils/__init__.py | 45 ++-- swarms/utils/apa.py | 8 +- swarms/utils/check_function_result.py | 18 +- swarms/utils/code_interpreter.py | 2 +- swarms/utils/data_to_text.py | 15 +- swarms/utils/device_checker_cuda.py | 5 +- swarms/utils/disable_logging.py | 4 - swarms/utils/dist_utils.py | 1 - swarms/utils/download_img.py | 7 +- swarms/utils/execute_futures.py | 4 +- swarms/utils/exponential_backoff.py | 3 +- swarms/utils/hash.py | 3 +- swarms/utils/json_output_parser.py | 1 + swarms/utils/jsonl_utils.py | 5 +- swarms/utils/loggers.py | 4 +- swarms/utils/prep_torch_model_inference.py | 1 + swarms/utils/remove_json_whitespace.py | 1 + swarms/utils/save_logs.py | 2 +- swarms/utils/serializable.py | 4 +- swarms/utils/video_to_frames.py | 42 ++++ swarms/utils/yaml_output_parser.py | 3 +- tests/README.md | 26 ++- tests/memory/test_dictinternalmemory.py | 4 +- tests/memory/test_dictsharedmemory.py | 2 + .../test_langchainchromavectormemory.py | 4 +- tests/memory/test_pinecone.py | 1 + tests/memory/test_qdrant.py | 3 +- tests/memory/test_short_term_memory.py | 3 +- tests/memory/test_sqlite.py | 4 +- tests/memory/test_weaviate.py | 4 +- tests/models/test_biogpt.py | 34 +-- tests/models/test_cohere.py | 1 + tests/models/test_elevenlab.py | 10 +- tests/models/test_gemini.py | 4 +- tests/models/test_gpt4_vision_api.py | 3 +- tests/models/test_hf.py | 8 +- tests/models/test_idefics.py | 6 +- tests/models/test_jina_embeds.py | 4 +- tests/models/test_llama_function_caller.py | 1 + tests/models/test_mistral.py | 1 + tests/models/test_mixtral.py | 4 +- tests/models/test_open_dalle.py | 1 + tests/models/test_openaitts.py | 4 +- tests/models/test_qwen.py | 1 + tests/models/test_speech_t5.py | 4 +- tests/models/test_ssd_1b.py | 3 +- tests/models/test_timm.py | 4 +- tests/models/test_timm_model.py | 4 +- tests/models/test_togther.py | 8 +- tests/models/test_ultralytics.py | 1 + tests/models/test_vilt.py | 6 +- tests/models/test_yi_200k.py | 1 + tests/structs/test_agent.py | 2 +- tests/structs/test_autoscaler.py | 3 +- tests/structs/test_base.py | 12 +- tests/structs/test_base_workflow.py | 7 +- tests/structs/test_company.py | 3 +- tests/structs/test_concurrent_workflow.py | 5 +- tests/structs/test_conversation.py | 1 + tests/structs/test_json.py | 4 +- tests/structs/test_majority_voting.py | 1 + tests/structs/test_model_parallizer.py | 5 +- tests/structs/test_multi_agent_collab.py | 12 +- tests/structs/test_nonlinear_workflow.py | 3 +- tests/structs/test_recursive_workflow.py | 4 +- tests/structs/test_task.py | 4 +- tests/structs/test_taskqueuebase.py | 4 +- tests/structs/test_tests_graph_workflow.py | 1 + tests/test_upload_tests_to_issues.py | 1 + tests/tokenizers/test_anthropictokenizer.py | 1 + tests/tokenizers/test_basetokenizer.py | 1 + tests/tokenizers/test_coheretokenizer.py | 4 +- tests/tokenizers/test_huggingfacetokenizer.py | 4 +- tests/tokenizers/test_openaitokenizer.py | 1 + tests/tokenizers/test_tokenizer.py | 3 +- tests/utils/test_check_device.py | 4 +- tests/utils/test_class_args_wrapper.py | 10 +- tests/utils/test_device.py | 4 +- tests/utils/test_display_markdown_message.py | 6 +- .../utils/test_extract_code_from_markdown.py | 1 + tests/utils/test_find_image_path.py | 4 +- tests/utils/test_limit_tokens_from_string.py | 1 + tests/utils/test_load_model_torch.py | 1 + tests/utils/test_load_models_torch.py | 4 +- tests/utils/test_metrics_decorator.py | 5 +- tests/utils/test_pdf_to_text.py | 2 +- tests/utils/test_prep_torch_inference.py | 4 +- .../utils/test_prep_torch_model_inference.py | 4 +- tests/utils/test_print_class_parameters.py | 1 + .../utils/test_subprocess_code_interpreter.py | 10 +- 358 files changed, 1802 insertions(+), 1494 deletions(-) create mode 100644 playground/demos/security_team/IMG_1625.MOV create mode 100644 swarms/structs/long_swarm.py create mode 100644 swarms/utils/video_to_frames.py diff --git a/README.md b/README.md index 3259d55b..4361355f 100644 --- a/README.md +++ b/README.md @@ -44,7 +44,7 @@ import os from dotenv import load_dotenv # Import the OpenAIChat model and the Agent struct -from swarms import OpenAIChat, Agent +from swarms import Agent, OpenAIChat # Load the environment variables load_dotenv() @@ -54,10 +54,7 @@ api_key = os.environ.get("OPENAI_API_KEY") # Initialize the language model llm = OpenAIChat( - temperature=0.5, - model_name="gpt-4", - openai_api_key=api_key, - max_tokens=4000 + temperature=0.5, model_name="gpt-4", openai_api_key=api_key, max_tokens=4000 ) @@ -66,9 +63,6 @@ agent = Agent(llm=llm, max_loops=1, autosave=True, dashboard=True) # Run the workflow on a task agent.run("Generate a 10,000 word blog on health and wellness.") - - - ``` @@ -79,6 +73,7 @@ ToolAgent is an agent that outputs JSON using any model from huggingface. It tak ```python # Import necessary libraries from transformers import AutoModelForCausalLM, AutoTokenizer + from swarms import ToolAgent # Load the pre-trained model and tokenizer @@ -107,8 +102,6 @@ generated_data = agent.run(task) # Print the generated data print(generated_data) - - ``` @@ -124,8 +117,10 @@ The `Worker` is a simple all-in-one agent equipped with an LLM, tools, and RAG f ```python # Importing necessary modules import os + from dotenv import load_dotenv -from swarms import Worker, OpenAIChat, tool + +from swarms import OpenAIChat, Worker, tool # Loading environment variables from .env file load_dotenv() @@ -151,14 +146,10 @@ worker = Worker( ) # Running the worker with a prompt -out = worker.run( - "Hello, how are you? Create an image of how your are doing!" -) +out = worker.run("Hello, how are you? Create an image of how your are doing!") # Printing the output print(out) - - ``` ------ @@ -174,10 +165,12 @@ Sequential Workflow enables you to sequentially execute tasks with `Agent` and t ✅ Utilizes Agent class ```python -import os -from swarms import OpenAIChat, Agent, SequentialWorkflow +import os + from dotenv import load_dotenv +from swarms import Agent, OpenAIChat, SequentialWorkflow + load_dotenv() # Load the environment variables @@ -186,10 +179,7 @@ api_key = os.getenv("OPENAI_API_KEY") # Initialize the language agent llm = OpenAIChat( - temperature=0.5, - model_name="gpt-4", - openai_api_key=api_key, - max_tokens=4000 + temperature=0.5, model_name="gpt-4", openai_api_key=api_key, max_tokens=4000 ) @@ -207,12 +197,14 @@ workflow = SequentialWorkflow(max_loops=1) # Add tasks to the workflow workflow.add( - agent1, "Generate a 10,000 word blog on health and wellness.", + agent1, + "Generate a 10,000 word blog on health and wellness.", ) # Suppose the next task takes the output of the first task as input workflow.add( - agent2, "Summarize the generated blog", + agent2, + "Summarize the generated blog", ) # Run the workflow @@ -231,8 +223,10 @@ for task in workflow.tasks: ```python import os + from dotenv import load_dotenv -from swarms import OpenAIChat, Task, ConcurrentWorkflow, Agent + +from swarms import Agent, ConcurrentWorkflow, OpenAIChat, Task # Load environment variables from .env file load_dotenv() @@ -254,16 +248,17 @@ workflow.add(tasks=[task1, task2, task3]) # Run the workflow workflow.run() - ``` ### `RecursiveWorkflow` `RecursiveWorkflow` will keep executing the tasks until a specific token like is located inside the text! ```python -import os -from dotenv import load_dotenv -from swarms import OpenAIChat, Task, RecursiveWorkflow, Agent +import os + +from dotenv import load_dotenv + +from swarms import Agent, OpenAIChat, RecursiveWorkflow, Task # Load environment variables from .env file load_dotenv() @@ -287,8 +282,6 @@ workflow.add(task3) # Run the workflow workflow.run() - - ``` @@ -304,7 +297,7 @@ import os from dotenv import load_dotenv -from swarms import Anthropic, Gemini, Mixtral, OpenAIChat, ModelParallelizer +from swarms import Anthropic, Gemini, Mixtral, ModelParallelizer, OpenAIChat load_dotenv() @@ -346,10 +339,7 @@ import os from dotenv import load_dotenv -from swarms import ( - OpenAIChat, - Conversation, -) +from swarms import Conversation, OpenAIChat conv = Conversation( time_enabled=True, @@ -364,6 +354,7 @@ api_key = os.environ.get("OPENAI_API_KEY") # Initialize the language model llm = OpenAIChat(openai_api_key=api_key, model_name="gpt-4") + # Run the language model in a loop def interactive_conversation(llm): conv = Conversation() @@ -372,9 +363,7 @@ def interactive_conversation(llm): conv.add("user", user_input) if user_input.lower() == "quit": break - task = ( - conv.return_history_as_string() - ) # Get the conversation history + task = conv.return_history_as_string() # Get the conversation history out = llm(task) conv.add("assistant", out) print( @@ -386,7 +375,6 @@ def interactive_conversation(llm): # Replace with your LLM instance interactive_conversation(llm) - ``` @@ -405,7 +393,7 @@ import os from dotenv import load_dotenv # Import the OpenAIChat model and the Agent struct -from swarms import OpenAIChat, Agent, SwarmNetwork +from swarms import Agent, OpenAIChat, SwarmNetwork # Load the environment variables load_dotenv() @@ -442,11 +430,8 @@ print(out) # Run all the agents in the swarm network on a task -out = swarmnet.run_many_agents( - "Generate a 10,000 word blog on health and wellness." -) +out = swarmnet.run_many_agents("Generate a 10,000 word blog on health and wellness.") print(out) - ``` @@ -513,8 +498,6 @@ else: # Output the result of the task print(f"Task result: {task.result}") - - ``` --- @@ -535,14 +518,7 @@ from dotenv import load_dotenv from transformers import AutoModelForCausalLM, AutoTokenizer # Import the models, structs, and telemetry modules -from swarms import ( - Gemini, - GPT4VisionAPI, - Mixtral, - OpenAI, - ToolAgent, - BlocksList, -) +from swarms import BlocksList, Gemini, GPT4VisionAPI, Mixtral, OpenAI, ToolAgent # Load the environment variables load_dotenv() @@ -552,9 +528,7 @@ openai_api_key = os.getenv("OPENAI_API_KEY") gemini_api_key = os.getenv("GEMINI_API_KEY") # Tool Agent -model = AutoModelForCausalLM.from_pretrained( - "databricks/dolly-v2-12b" -) +model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-12b") tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-12b") json_schema = { "type": "object", @@ -565,9 +539,7 @@ json_schema = { "courses": {"type": "array", "items": {"type": "string"}}, }, } -toolagent = ToolAgent( - model=model, tokenizer=tokenizer, json_schema=json_schema -) +toolagent = ToolAgent(model=model, tokenizer=tokenizer, json_schema=json_schema) # Blocks List which enables you to build custom swarms by adding classes or functions swarm = BlocksList( @@ -619,9 +591,7 @@ blocks_by_parent_name = swarm.get_by_parent_name(swarm.name) blocks_by_parent_type = swarm.get_by_parent_type(type(swarm).__name__) # Get blocks by parent description -blocks_by_parent_description = swarm.get_by_parent_description( - swarm.description -) +blocks_by_parent_description = swarm.get_by_parent_description(swarm.description) # Run the block in the swarm inference = swarm.run_block(toolagent, "Hello World") @@ -636,25 +606,27 @@ Here's a production grade swarm ready for real-world deployment in a factory and ```python -from swarms.structs import Agent import os + from dotenv import load_dotenv + from swarms.models import GPT4VisionAPI from swarms.prompts.logistics import ( + Efficiency_Agent_Prompt, Health_Security_Agent_Prompt, - Quality_Control_Agent_Prompt, Productivity_Agent_Prompt, + Quality_Control_Agent_Prompt, Safety_Agent_Prompt, Security_Agent_Prompt, Sustainability_Agent_Prompt, - Efficiency_Agent_Prompt, ) +from swarms.structs import Agent # Load ENV load_dotenv() api_key = os.getenv("OPENAI_API_KEY") -# GPT4VisionAPI +# GPT4VisionAPI llm = GPT4VisionAPI(openai_api_key=api_key) # Image for analysis @@ -686,9 +658,7 @@ productivity_agent = Agent( ) # Initiailize safety agent -safety_agent = Agent( - llm=llm, sop=Safety_Agent_Prompt, max_loops=1, multi_modal=True -) +safety_agent = Agent(llm=llm, sop=Safety_Agent_Prompt, max_loops=1, multi_modal=True) # Init the security agent security_agent = Agent( @@ -748,7 +718,9 @@ Run the agent with multiple modalities useful for various real-world tasks in ma ```python # Description: This is an example of how to use the Agent class to run a multi-modal workflow import os + from dotenv import load_dotenv + from swarms.models.gpt4_vision_api import GPT4VisionAPI from swarms.structs import Agent @@ -775,17 +747,11 @@ img = "assembly_line.jpg" ## Initialize the workflow agent = Agent( - llm=llm, - max_loops="auto", - autosave=True, - dashboard=True, - multi_modal=True + llm=llm, max_loops="auto", autosave=True, dashboard=True, multi_modal=True ) # Run the workflow on a task agent.run(task=task, img=img) - - ``` --- @@ -857,14 +823,10 @@ model = QwenVLMultiModal( ) # Run the model -response = model( - "Hello, how are you?", "https://example.com/image.jpg" -) +response = model("Hello, how are you?", "https://example.com/image.jpg") # Print the response print(response) - - ``` @@ -882,7 +844,6 @@ out = model.run("Analyze the reciepts in this image", "docs.jpg") # Print the output print(out) - ``` @@ -923,8 +884,6 @@ model.set_max_length(200) # Clear the chat history of the model model.clear_chat_history() - - ``` ## Radically Simple AI Model APIs @@ -941,9 +900,7 @@ We provide a vast array of language and multi-modal model APIs for you to genera from swarms.models import Anthropic # Initialize an instance of the Anthropic class -model = Anthropic( - anthropic_api_key="" -) +model = Anthropic(anthropic_api_key="") # Using the run method completion_1 = model.run("What is the capital of France?") @@ -952,7 +909,6 @@ print(completion_1) # Using the __call__ method completion_2 = model("How far is the moon from the earth?", stop=["miles", "km"]) print(completion_2) - ``` @@ -964,12 +920,16 @@ from swarms.models import HuggingfaceLLM custom_config = { "quantize": True, "quantization_config": {"load_in_4bit": True}, - "verbose": True + "verbose": True, } -inference = HuggingfaceLLM(model_id="NousResearch/Nous-Hermes-2-Vision-Alpha", **custom_config) +inference = HuggingfaceLLM( + model_id="NousResearch/Nous-Hermes-2-Vision-Alpha", **custom_config +) # Generate text based on a prompt -prompt_text = "Create a list of known biggest risks of structural collapse with references" +prompt_text = ( + "Create a list of known biggest risks of structural collapse with references" +) generated_text = inference(prompt_text) print(generated_text) ``` @@ -1027,7 +987,6 @@ task = "A person is walking on the street." # Generate the video! video_path = zeroscope(task) print(video_path) - ``` diff --git a/docs/applications/discord.md b/docs/applications/discord.md index cae3c8c1..dd7de16c 100644 --- a/docs/applications/discord.md +++ b/docs/applications/discord.md @@ -64,6 +64,7 @@ Initialize the `llm` (Language Learning Model) with your OpenAI API key: ```python from swarms.models import OpenAIChat + llm = OpenAIChat( openai_api_key="Your_OpenAI_API_Key", temperature=0.5, @@ -74,6 +75,7 @@ Initialize the bot with the `llm`: ```python from apps.discord import Bot + bot = Bot(llm=llm) ``` diff --git a/docs/examples/bingchat.md b/docs/examples/bingchat.md index 5ff93c63..3dabbf2e 100644 --- a/docs/examples/bingchat.md +++ b/docs/examples/bingchat.md @@ -46,6 +46,7 @@ You can also specify the conversation style: ```python from bing_chat import ConversationStyle + response = chat("Tell me a joke", style=ConversationStyle.creative) print(response) ``` diff --git a/docs/examples/flow.md b/docs/examples/flow.md index 92842491..2e635111 100644 --- a/docs/examples/flow.md +++ b/docs/examples/flow.md @@ -107,16 +107,17 @@ Now, let's create your first Agent. A Agent represents a chain-like structure th # Import necessary modules ```python -from swarms.models import OpenAIChat # Zephr, Mistral +from swarms.models import OpenAIChat # Zephr, Mistral from swarms.structs import Agent -api_key = ""# Initialize the language model (LLM) -llm = OpenAIChat(openai_api_key=api_key, temperature=0.5, max_tokens=3000)# Initialize the Agent object +api_key = "" # Initialize the language model (LLM) +llm = OpenAIChat( + openai_api_key=api_key, temperature=0.5, max_tokens=3000 +) # Initialize the Agent object -agent = Agent(llm=llm, max_loops=5)# Run the agent +agent = Agent(llm=llm, max_loops=5) # Run the agent out = agent.run("Create an financial analysis on the following metrics") print(out) - ``` ### [3. Initializing the Agent Object](https://github.com/kyegomez/swarms) diff --git a/docs/examples/omni_agent.md b/docs/examples/omni_agent.md index 56a6c996..e4d65498 100644 --- a/docs/examples/omni_agent.md +++ b/docs/examples/omni_agent.md @@ -55,7 +55,7 @@ from swarms.models import OpenAIChat llm = OpenAIChat(openai_api_key="sk-") agent = OmniModalAgent(llm) -response = agent.run("Create an video of a swarm of fish concept art, game art") +response = agent.run("Create an video of a swarm of fish concept art, game art") print(response) ``` diff --git a/docs/examples/revgpt.md b/docs/examples/revgpt.md index 5aa17af4..69107b40 100644 --- a/docs/examples/revgpt.md +++ b/docs/examples/revgpt.md @@ -35,7 +35,6 @@ The abstraction provided in `revgpt.py` is designed to simplify your interaction 1. **Import the Necessary Modules:** ```python -import os from dotenv import load_dotenv from revgpt import AbstractChatGPT ``` diff --git a/docs/examples/stacked_worker.md b/docs/examples/stacked_worker.md index cdcc537c..914d1754 100644 --- a/docs/examples/stacked_worker.md +++ b/docs/examples/stacked_worker.md @@ -28,8 +28,8 @@ The provided code showcases a system built around a worker node that utilizes va The code begins with import statements, bringing in necessary modules and classes. Key imports include the `OpenAIChat` class, which represents a language model, and several custom agents and tools from the `swarms` package. ```python -import os import interpreter # Assuming this is a custom module + from swarms.agents.hf_agents import HFAgent from swarms.agents.omni_modal_agent import OmniModalAgent from swarms.models import OpenAIChat @@ -59,11 +59,7 @@ All defined tools are appended to a list called `tools`. This list is later used ```python # Append tools to a list -tools = [ - hf_agent, - omni_agent, - compile -] +tools = [hf_agent, omni_agent, compile] ``` ### Initializing a Worker Node @@ -263,8 +259,6 @@ response = node.run(task) # Print the response print(response) - - ``` diff --git a/docs/examples/worker.md b/docs/examples/worker.md index 8fe2bf75..cd082aa4 100644 --- a/docs/examples/worker.md +++ b/docs/examples/worker.md @@ -53,11 +53,11 @@ Voila! You’re now ready to summon your Worker. Here’s a simple way to invoke the Worker and give it a task: ```python -from swarms.models import OpenAIChat from swarms import Worker +from swarms.models import OpenAIChat llm = OpenAIChat( - #enter your api key + # enter your api key openai_api_key="", temperature=0.5, ) @@ -75,8 +75,6 @@ node = Worker( task = "What were the winning boston marathon times for the past 5 years (ending in 2022)? Generate a table of the year, name, country of origin, and times." response = node.run(task) print(response) - - ``` diff --git a/docs/swarms/agents/abstractagent.md b/docs/swarms/agents/abstractagent.md index cdd06715..8833c164 100644 --- a/docs/swarms/agents/abstractagent.md +++ b/docs/swarms/agents/abstractagent.md @@ -37,7 +37,6 @@ class AbstractAgent: def memory(self, memory_store): """init memory""" - pass def reset(self): """(Abstract method) Reset the agent.""" @@ -82,7 +81,7 @@ agent.reset() The `run` method allows the agent to perform a specific task. ```python -agent.run('some_task') +agent.run("some_task") ``` #### 3. `chat` @@ -90,7 +89,7 @@ agent.run('some_task') The `chat` method enables communication with the agent through a series of messages. ```python -messages = [{'id': 1, 'text': 'Hello, agent!'}, {'id': 2, 'text': 'How are you?'}] +messages = [{"id": 1, "text": "Hello, agent!"}, {"id": 2, "text": "How are you?"}] agent.chat(messages) ``` @@ -99,7 +98,7 @@ agent.chat(messages) The `step` method allows the agent to process a single message. ```python -agent.step('Hello, agent!') +agent.step("Hello, agent!") ``` ### Asynchronous Methods diff --git a/docs/swarms/agents/message.md b/docs/swarms/agents/message.md index 87794ebc..413ac016 100644 --- a/docs/swarms/agents/message.md +++ b/docs/swarms/agents/message.md @@ -44,7 +44,7 @@ class Message: def __repr__(self): """ __repr__ represents the string representation of the Message object. - + Returns: (str) A string containing the timestamp, sender, and content of the message. """ @@ -60,10 +60,7 @@ The `Message` class represents a message in the agent system. Upon initializatio Creating a `Message` object and displaying its string representation. ```python -mes = Message( - sender = "Kye", - content = "Hello! How are you?" -) +mes = Message(sender="Kye", content="Hello! How are you?") print(mes) ``` @@ -80,9 +77,7 @@ Creating a `Message` object with metadata. ```python metadata = {"priority": "high", "category": "urgent"} mes_with_metadata = Message( - sender = "Alice", - content = "Important update", - metadata = metadata + sender="Alice", content="Important update", metadata=metadata ) print(mes_with_metadata) @@ -98,10 +93,7 @@ Output: Creating a `Message` object without providing metadata. ```python -mes_no_metadata = Message( - sender = "Bob", - content = "Reminder: Meeting at 2PM" -) +mes_no_metadata = Message(sender="Bob", content="Reminder: Meeting at 2PM") print(mes_no_metadata) ``` diff --git a/docs/swarms/agents/omni_agent.md b/docs/swarms/agents/omni_agent.md index a80da0e6..888e824f 100644 --- a/docs/swarms/agents/omni_agent.md +++ b/docs/swarms/agents/omni_agent.md @@ -39,10 +39,12 @@ For streaming mode, this function yields the response token by token, ensuring a ## Examples & Use Cases Initialize the `OmniModalAgent` and communicate with it: ```python +import os + +from dotenv import load_dotenv + from swarms.agents.omni_modal_agent import OmniModalAgent, OpenAIChat from swarms.models import OpenAIChat -from dotenv import load_dotenv -import os # Load the environment variables load_dotenv() diff --git a/docs/swarms/agents/toolagent.md b/docs/swarms/agents/toolagent.md index ebb00623..35a31f99 100644 --- a/docs/swarms/agents/toolagent.md +++ b/docs/swarms/agents/toolagent.md @@ -69,6 +69,7 @@ The `ToolAgent` class takes the following arguments: ```python from transformers import AutoModelForCausalLM, AutoTokenizer + from swarms import ToolAgent # Creating a model and tokenizer @@ -82,11 +83,8 @@ json_schema = { "name": {"type": "string"}, "age": {"type": "number"}, "is_student": {"type": "boolean"}, - "courses": { - "type": "array", - "items": {"type": "string"} - } - } + "courses": {"type": "array", "items": {"type": "string"}}, + }, } # Defining a task diff --git a/docs/swarms/agents/workeragent.md b/docs/swarms/agents/workeragent.md index e46ec1af..aaa71653 100644 --- a/docs/swarms/agents/workeragent.md +++ b/docs/swarms/agents/workeragent.md @@ -38,7 +38,7 @@ worker = Worker( human_in_the_loop=False, temperature=0.5, llm=some_language_model, - openai_api_key="my_key" + openai_api_key="my_key", ) worker.run("What's the weather in Miami?") ``` @@ -56,11 +56,11 @@ worker.send() ```python external_tools = [MyTool1(), MyTool2()] worker = Worker( -name="My Worker", -role="Worker", -external_tools=external_tools, -human_in_the_loop=False, -temperature=0.5, + name="My Worker", + role="Worker", + external_tools=external_tools, + human_in_the_loop=False, + temperature=0.5, ) ``` diff --git a/docs/swarms/chunkers/basechunker.md b/docs/swarms/chunkers/basechunker.md index 33b03312..11c32dc5 100644 --- a/docs/swarms/chunkers/basechunker.md +++ b/docs/swarms/chunkers/basechunker.md @@ -69,7 +69,9 @@ from basechunker import BaseChunker, ChunkSeparator chunker = BaseChunker() # Text to be chunked -input_text = "This is a long text that needs to be split into smaller chunks for processing." +input_text = ( + "This is a long text that needs to be split into smaller chunks for processing." +) # Chunk the text chunks = chunker.chunk(input_text) diff --git a/docs/swarms/chunkers/pdf_chunker.md b/docs/swarms/chunkers/pdf_chunker.md index 8c92060d..d3eb02a9 100644 --- a/docs/swarms/chunkers/pdf_chunker.md +++ b/docs/swarms/chunkers/pdf_chunker.md @@ -62,8 +62,8 @@ Let's explore how to use the `PdfChunker` class with different scenarios and app #### Example 1: Basic Chunking ```python -from swarms.chunkers.pdf_chunker import PdfChunker from swarms.chunkers.chunk_seperator import ChunkSeparator +from swarms.chunkers.pdf_chunker import PdfChunker # Initialize the PdfChunker pdf_chunker = PdfChunker() @@ -82,8 +82,8 @@ for idx, chunk in enumerate(chunks, start=1): #### Example 2: Custom Separators ```python -from swarms.chunkers.pdf_chunker import PdfChunker from swarms.chunkers.chunk_seperator import ChunkSeparator +from swarms.chunkers.pdf_chunker import PdfChunker # Define custom separators for PDF chunking custom_separators = [ChunkSeparator("\n\n"), ChunkSeparator(". ")] diff --git a/docs/swarms/index.md b/docs/swarms/index.md index 98c6b7a3..b052b6ce 100644 --- a/docs/swarms/index.md +++ b/docs/swarms/index.md @@ -28,7 +28,6 @@ We have a small gallery of examples to run here, [for more check out the docs to - Enterprise Grade + Production Grade: `Agent` is designed and optimized for automating real-world tasks at scale! ```python - from swarms.models import OpenAIChat from swarms.structs import Agent @@ -64,9 +63,6 @@ out = agent.run("Generate a 10,000 word blog on health and wellness.") # out = agent.print_history_and_memory() # # out = agent.save_state("flow_state.json") # print(out) - - - ``` ------ @@ -82,9 +78,7 @@ from swarms.structs import Agent from swarms.structs.sequential_workflow import SequentialWorkflow # Example usage -api_key = ( - "" # Your actual API key here -) +api_key = "" # Your actual API key here # Initialize the language agent llm = OpenAIChat( @@ -118,7 +112,6 @@ workflow.run() # Output the results for task in workflow.tasks: print(f"Task: {task.description}, Result: {task.result}") - ``` --- diff --git a/docs/swarms/memory/pg.md b/docs/swarms/memory/pg.md index 84878a76..3695e11c 100644 --- a/docs/swarms/memory/pg.md +++ b/docs/swarms/memory/pg.md @@ -110,7 +110,9 @@ def setup( ```python # Initialize the PgVectorVectorStore instance -vector_store = PgVectorVectorStore(connection_string="your-db-connection-string", table_name="your-table-name") +vector_store = PgVectorVectorStore( + connection_string="your-db-connection-string", table_name="your-table-name" +) # Set up the database with default settings vector_store.setup() @@ -120,10 +122,14 @@ vector_store.setup() ```python # Initialize the PgVectorVectorStore instance -vector_store = PgVectorVectorStore(connection_string="your-db-connection-string", table_name="your-table-name") +vector_store = PgVectorVectorStore( + connection_string="your-db-connection-string", table_name="your-table-name" +) # Set up the database with customized settings -vector_store.setup(create_schema=False, install_uuid_extension=True, install_vector_extension=True) +vector_store.setup( + create_schema=False, install_uuid_extension=True, install_vector_extension=True +) ``` ### 4.2 Upserting Vectors @@ -137,7 +143,7 @@ def upsert_vector( vector_id: Optional[str] = None, namespace: Optional[str] = None, meta: Optional[dict] = None, - **kwargs + **kwargs, ) -> str: """ Inserts or updates a vector in the collection. @@ -158,7 +164,9 @@ def upsert_vector( ```python # Initialize the PgVectorVectorStore instance -vector_store = PgVectorVectorStore(connection_string="your-db-connection-string", table_name="your-table-name") +vector_store = PgVectorVectorStore( + connection_string="your-db-connection-string", table_name="your-table-name" +) # Define a vector and upsert it vector = [0.1, 0.2, 0.3, 0.4] @@ -167,10 +175,7 @@ namespace = "your-namespace" meta = {"key1": "value1", "key2": "value2"} vector_store.upsert_vector( - vector=vector, - vector_id=vector_id, - namespace=namespace, - meta=meta + vector=vector, vector_id=vector_id, namespace=namespace, meta=meta ) ``` @@ -222,9 +227,7 @@ else: The `load_entries` method allows you to load all vector entries from the collection, optionally filtering by namespace. ```python -def load_entries( - self, namespace: Optional[str] = None -) -> list[BaseVectorStore.Entry]: +def load_entries(self, namespace: Optional[str] = None) -> list[BaseVectorStore.Entry]: """ Retrieves all vector entries from the collection, optionally filtering to only those that match the provided namespace. @@ -240,7 +243,9 @@ def load_entries( ```python # Initialize the PgVectorVectorStore instance -vector_store = PgVectorVectorStore(connection_string="your-db-connection-string", table_name="your-table-name") +vector_store = PgVectorVectorStore( + connection_string="your-db-connection-string", table_name="your-table-name" +) # Load all vector entries in the specified namespace entries = vector_store.load_entries(namespace="your-namespace") @@ -266,7 +271,7 @@ def query( namespace: Optional[str] = None, include_vectors: bool = False, distance_metric: str = "cosine_distance", - **kwargs + **kwargs, ) -> list[BaseVectorStore.QueryResult]: """ Performs a search on the collection to find vectors similar to the provided input vector, @@ -290,7 +295,9 @@ def query( ```python # Initialize the PgVectorVectorStore instance -vector_store = PgVectorVectorStore(connection_string="your-db-connection-string", table_name="your-table-name") +vector_store = PgVectorVectorStore( + connection_string="your-db-connection-string", table_name="your-table-name" +) # Perform a vector query query_string = "your-query-string" @@ -304,7 +311,7 @@ results = vector_store.query( count=count, namespace=namespace, include_vectors=include_vectors, - distance_metric=distance_metric + distance_metric=distance_metric, ) # Process the query results diff --git a/docs/swarms/memory/pinecone.md b/docs/swarms/memory/pinecone.md index 830d10fe..f8ca0f2e 100644 --- a/docs/swarms/memory/pinecone.md +++ b/docs/swarms/memory/pinecone.md @@ -174,7 +174,7 @@ pv = PineconeVector( api_key="your-api-key", index_name="your-index-name", environment="us-west1-gcp", - project_name="your-project-name" + project_name="your-project-name", ) ``` @@ -198,12 +198,7 @@ vector_id = "unique-vector-id" namespace = "your-namespace" meta = {"key1": "value1", "key2": "value2"} -pv.upsert_vector( - vector=vector, - vector_id=vector_id, - namespace=namespace, - meta=meta -) +pv.upsert_vector(vector=vector, vector_id=vector_id, namespace=namespace, meta=meta) ``` ### 4.4 Querying the Index @@ -222,7 +217,7 @@ results = pv.query( count=count, namespace=namespace, include_vectors=include_vectors, - include_metadata=include_metadata + include_metadata=include_metadata, ) # Process the query results diff --git a/docs/swarms/memory/qdrant.md b/docs/swarms/memory/qdrant.md index 3717d94f..e234e7be 100644 --- a/docs/swarms/memory/qdrant.md +++ b/docs/swarms/memory/qdrant.md @@ -14,8 +14,15 @@ pip install qdrant-client sentence-transformers httpx ```python class Qdrant: - def __init__(self, api_key: str, host: str, port: int = 6333, collection_name: str = "qdrant", model_name: str = "BAAI/bge-small-en-v1.5", https: bool = True): - ... + def __init__( + self, + api_key: str, + host: str, + port: int = 6333, + collection_name: str = "qdrant", + model_name: str = "BAAI/bge-small-en-v1.5", + https: bool = True, + ): ... ``` ### Constructor Parameters @@ -60,10 +67,7 @@ qdrant_client = Qdrant(api_key="your_api_key", host="localhost", port=6333) ### Example 2: Adding Vectors to a Collection ```python -documents = [ - {"page_content": "Sample text 1"}, - {"page_content": "Sample text 2"} -] +documents = [{"page_content": "Sample text 1"}, {"page_content": "Sample text 2"}] operation_info = qdrant_client.add_vectors(documents) print(operation_info) diff --git a/docs/swarms/memory/short_term_memory.md b/docs/swarms/memory/short_term_memory.md index 2aabbd5c..9ee3a738 100644 --- a/docs/swarms/memory/short_term_memory.md +++ b/docs/swarms/memory/short_term_memory.md @@ -125,7 +125,9 @@ def update_short_term(self, index, role: str, message: str, *args, **kwargs): ##### Example: Updating a Message in Short-Term Memory ```python -memory.update_short_term(index=0, role="Updated Role", message="Updated message content.") +memory.update_short_term( + index=0, role="Updated Role", message="Updated message content." +) ``` #### 7. `clear` diff --git a/docs/swarms/memory/weaviate.md b/docs/swarms/memory/weaviate.md index b23baedf..dc264653 100644 --- a/docs/swarms/memory/weaviate.md +++ b/docs/swarms/memory/weaviate.md @@ -82,7 +82,7 @@ weaviate_client.create_collection( {"name": "property1", "dataType": ["string"]}, {"name": "property2", "dataType": ["int"]}, ], - vectorizer_config=None # Optional vectorizer configuration + vectorizer_config=None, # Optional vectorizer configuration ) ``` @@ -99,8 +99,7 @@ The `add` method allows you to add an object to a specified collection in Weavia ```python weaviate_client.add( - collection_name="my_collection", - properties={"property1": "value1", "property2": 42} + collection_name="my_collection", properties={"property1": "value1", "property2": 42} ) ``` @@ -142,7 +141,7 @@ The `update` method allows you to update an object in a specified collection in weaviate_client.update( collection_name="my_collection", object_id="object123", - properties={"property1": "new_value", "property2": 99} + properties={"property1": "new_value", "property2": 99}, ) ``` @@ -158,10 +157,7 @@ The `delete` method allows you to delete an object from a specified collection i #### Usage ```python -weaviate_client.delete( - collection_name="my_collection", - object_id="object123" -) +weaviate_client.delete(collection_name="my_collection", object_id="object123") ``` ## Examples @@ -175,28 +171,21 @@ weaviate_client.create_collection( name="people", properties=[ {"name": "name", "dataType": ["string"]}, - {"name": "age", "dataType": ["int"]} - ] + {"name": "age", "dataType": ["int"]}, + ], ) ``` ### Example 2: Adding an Object ```python -weaviate_client.add( - collection_name="people", - properties={"name": "John", "age": 30} -) +weaviate_client.add(collection_name="people", properties={"name": "John", "age": 30}) ``` ### Example 3: Querying Objects ```python -results = weaviate_client.query( - collection_name="people", - query="name:John", - limit=5 -) +results = weaviate_client.query(collection_name="people", query="name:John", limit=5) ``` These examples cover the basic operations of creating collections, adding objects, and querying objects using the Weaviate API Client. diff --git a/docs/swarms/models/anthropic.md b/docs/swarms/models/anthropic.md index 85e7a428..438adfbe 100644 --- a/docs/swarms/models/anthropic.md +++ b/docs/swarms/models/anthropic.md @@ -72,9 +72,7 @@ class Anthropic: from swarms.models import Anthropic # Initialize an instance of the Anthropic class -model = Anthropic( - anthropic_api_key="" -) +model = Anthropic(anthropic_api_key="") # Using the run method completion_1 = model.run("What is the capital of France?") diff --git a/docs/swarms/models/base_multimodal_model.md b/docs/swarms/models/base_multimodal_model.md index 13efa11c..c1a8373d 100644 --- a/docs/swarms/models/base_multimodal_model.md +++ b/docs/swarms/models/base_multimodal_model.md @@ -149,7 +149,9 @@ model = BaseMultiModalModel( ) # Run the model with a text task and an image URL -response = model.run("Generate a summary of this text", "https://www.example.com/image.jpg") +response = model.run( + "Generate a summary of this text", "https://www.example.com/image.jpg" +) print(response) ``` @@ -209,6 +211,7 @@ for response in responses: ```python from swarms.models import BaseMultiModalModel + class CustomMultiModalModel(BaseMultiModalModel): def __init__(self, model_name, custom_parameter, *args, **kwargs): # Call the parent class constructor @@ -226,6 +229,7 @@ class CustomMultiModalModel(BaseMultiModalModel): # You can use self.custom_parameter and other inherited attributes pass + # Create an instance of your custom multimodal model custom_model = CustomMultiModalModel( model_name="your_custom_model_name", @@ -236,7 +240,9 @@ custom_model = CustomMultiModalModel( ) # Run your custom model -response = custom_model.run("Generate a summary of this text", "https://www.example.com/image.jpg") +response = custom_model.run( + "Generate a summary of this text", "https://www.example.com/image.jpg" +) print(response) # Generate a summary using your custom model diff --git a/docs/swarms/models/bingchat.md b/docs/swarms/models/bingchat.md index 70c184d3..a98aa8be 100644 --- a/docs/swarms/models/bingchat.md +++ b/docs/swarms/models/bingchat.md @@ -39,7 +39,6 @@ print(response) ```python from swarms.models.bing_chat import BingChat - edgegpt = BingChat(cookies_path="./path/to/cookies.json") response = edgegpt("Hello, my name is ChatGPT") print(response) @@ -48,7 +47,9 @@ print(response) 3. Generate an image based on a text prompt: ```python -image_path = edgegpt.create_img("Sunset over mountains", output_dir="./output", auth_cookie="your_auth_cookie") +image_path = edgegpt.create_img( + "Sunset over mountains", output_dir="./output", auth_cookie="your_auth_cookie" +) print(f"Generated image saved at {image_path}") ``` @@ -59,7 +60,9 @@ from swarms.models.bing_chat import BingChat edgegpt = BingChat(cookies_path="./path/to/cookies.json") -image_path = edgegpt.create_img("Sunset over mountains", output_dir="./output", auth_cookie="your_auth_cookie") +image_path = edgegpt.create_img( + "Sunset over mountains", output_dir="./output", auth_cookie="your_auth_cookie" +) print(f"Generated image saved at {image_path}") ``` diff --git a/docs/swarms/models/biogpt.md b/docs/swarms/models/biogpt.md index c43557b6..bb4e8914 100644 --- a/docs/swarms/models/biogpt.md +++ b/docs/swarms/models/biogpt.md @@ -83,7 +83,6 @@ print(generated_text) ```python from swarms.models import BioGPT - # Initialize the BioGPT model biogpt = BioGPT() @@ -99,7 +98,6 @@ print(features) ```python from swarms.models import BioGPT - # Initialize the BioGPT model biogpt = BioGPT() diff --git a/docs/swarms/models/distilled_whisperx.md b/docs/swarms/models/distilled_whisperx.md index e9339c1e..79c8c2ea 100644 --- a/docs/swarms/models/distilled_whisperx.md +++ b/docs/swarms/models/distilled_whisperx.md @@ -29,7 +29,7 @@ from swarms.models import DistilWhisperModel model_wrapper = DistilWhisperModel() # Initialize with a specific model ID -model_wrapper = DistilWhisperModel(model_id='distil-whisper/distil-large-v2') +model_wrapper = DistilWhisperModel(model_id="distil-whisper/distil-large-v2") ``` ## Attributes @@ -62,7 +62,7 @@ Transcribes audio input synchronously. ```python # Synchronous transcription -transcription = model_wrapper.transcribe('path/to/audio.mp3') +transcription = model_wrapper.transcribe("path/to/audio.mp3") print(transcription) ``` @@ -84,7 +84,7 @@ Transcribes audio input asynchronously. import asyncio # Asynchronous transcription -transcription = asyncio.run(model_wrapper.async_transcribe('path/to/audio.mp3')) +transcription = asyncio.run(model_wrapper.async_transcribe("path/to/audio.mp3")) print(transcription) ``` @@ -103,7 +103,7 @@ Simulates real-time transcription of an audio file. ```python # Real-time transcription simulation -model_wrapper.real_time_transcribe('path/to/audio.mp3', chunk_duration=5) +model_wrapper.real_time_transcribe("path/to/audio.mp3", chunk_duration=5) ``` ## Error Handling diff --git a/docs/swarms/models/gpt4v.md b/docs/swarms/models/gpt4v.md index 69968623..5ad80cd9 100644 --- a/docs/swarms/models/gpt4v.md +++ b/docs/swarms/models/gpt4v.md @@ -107,16 +107,16 @@ The `__call__` method is a convenient way to run the GPT-4 Vision model. It has ```python def __call__(task: str, img: str) -> str: """ - Run the GPT-4 Vision model (callable). + Run the GPT-4 Vision model (callable). - Parameters: - - task (str): The task or question related to the image. - - img + Parameters: + - task (str): The task or question related to the image. + - img - (str): URL of the image to analyze. + (str): URL of the image to analyze. - Returns: - str: The model's response. + Returns: + str: The model's response. """ ``` diff --git a/docs/swarms/models/huggingface.md b/docs/swarms/models/huggingface.md index 8606d8f2..50aaa2a1 100644 --- a/docs/swarms/models/huggingface.md +++ b/docs/swarms/models/huggingface.md @@ -114,9 +114,11 @@ from swarms.models import HuggingfaceLLM custom_config = { "quantize": True, "quantization_config": {"load_in_4bit": True}, - "verbose": True + "verbose": True, } -inference = HuggingfaceLLM(model_id="NousResearch/Nous-Hermes-2-Vision-Alpha", **custom_config) +inference = HuggingfaceLLM( + model_id="NousResearch/Nous-Hermes-2-Vision-Alpha", **custom_config +) # Generate text based on a prompt prompt_text = "Tell me a joke" diff --git a/docs/swarms/models/idefics.md b/docs/swarms/models/idefics.md index 04822fdd..57125038 100644 --- a/docs/swarms/models/idefics.md +++ b/docs/swarms/models/idefics.md @@ -36,7 +36,9 @@ model = Idefics() 2. Generate text based on prompts: ```python -prompts = ["User: What is in this image? https://upload.wikimedia.org/wikipedia/commons/8/86/Id%C3%A9fix.JPG"] +prompts = [ + "User: What is in this image? https://upload.wikimedia.org/wikipedia/commons/8/86/Id%C3%A9fix.JPG" +] response = model(prompts) print(response) ``` @@ -47,7 +49,9 @@ print(response) from swarms.models import Idefics model = Idefics() -prompts = ["User: What is in this image? https://upload.wikimedia.org/wikipedia/commons/8/86/Id%C3%A9fix.JPG"] +prompts = [ + "User: What is in this image? https://upload.wikimedia.org/wikipedia/commons/8/86/Id%C3%A9fix.JPG" +] response = model(prompts) print(response) ``` diff --git a/docs/swarms/models/index.md b/docs/swarms/models/index.md index 93883779..9e001eea 100644 --- a/docs/swarms/models/index.md +++ b/docs/swarms/models/index.md @@ -42,9 +42,10 @@ OpenAI(api_key: str, system: str = None, console: bool = True, model: str = None **Usage Example:** ```python -from swarms import OpenAI import asyncio +from swarms import OpenAI + chat = OpenAI(api_key="YOUR_OPENAI_API_KEY") response = chat.generate("Hello, how can I assist you?") @@ -126,7 +127,10 @@ GooglePalm(model_name: str = "models/chat-bison-001", google_api_key: str = None from swarms import GooglePalm google_palm = GooglePalm() -messages = [{"role": "system", "content": "You are a helpful assistant"}, {"role": "user", "content": "Tell me a joke"}] +messages = [ + {"role": "system", "content": "You are a helpful assistant"}, + {"role": "user", "content": "Tell me a joke"}, +] response = google_palm.generate(messages) print(response["choices"][0]["text"]) diff --git a/docs/swarms/models/kosmos.md b/docs/swarms/models/kosmos.md index 1735e153..a19ea791 100644 --- a/docs/swarms/models/kosmos.md +++ b/docs/swarms/models/kosmos.md @@ -30,7 +30,9 @@ kosmos = Kosmos() 2. Perform Multimodal Grounding: ```python -kosmos.multimodal_grounding("Find the red apple in the image.", "https://example.com/apple.jpg") +kosmos.multimodal_grounding( + "Find the red apple in the image.", "https://example.com/apple.jpg" +) ``` ### Example 1 - Multimodal Grounding @@ -40,13 +42,17 @@ from swarms.models.kosmos_two import Kosmos kosmos = Kosmos() -kosmos.multimodal_grounding("Find the red apple in the image.", "https://example.com/apple.jpg") +kosmos.multimodal_grounding( + "Find the red apple in the image.", "https://example.com/apple.jpg" +) ``` 3. Perform Referring Expression Comprehension: ```python -kosmos.referring_expression_comprehension("Show me the green bottle.", "https://example.com/bottle.jpg") +kosmos.referring_expression_comprehension( + "Show me the green bottle.", "https://example.com/bottle.jpg" +) ``` ### Example 2 - Referring Expression Comprehension @@ -56,13 +62,17 @@ from swarms.models.kosmos_two import Kosmos kosmos = Kosmos() -kosmos.referring_expression_comprehension("Show me the green bottle.", "https://example.com/bottle.jpg") +kosmos.referring_expression_comprehension( + "Show me the green bottle.", "https://example.com/bottle.jpg" +) ``` 4. Generate Referring Expressions: ```python -kosmos.referring_expression_generation("It is on the table.", "https://example.com/table.jpg") +kosmos.referring_expression_generation( + "It is on the table.", "https://example.com/table.jpg" +) ``` ### Example 3 - Referring Expression Generation @@ -72,7 +82,9 @@ from swarms.models.kosmos_two import Kosmos kosmos = Kosmos() -kosmos.referring_expression_generation("It is on the table.", "https://example.com/table.jpg") +kosmos.referring_expression_generation( + "It is on the table.", "https://example.com/table.jpg" +) ``` 5. Perform Grounded Visual Question Answering (VQA): @@ -127,7 +139,10 @@ kosmos.grounded_image_captioning_detailed("https://example.com/beach.jpg") ```python image = kosmos.get_image("https://example.com/image.jpg") -entities = [("apple", (0, 3), [(0.2, 0.3, 0.4, 0.5)]), ("banana", (4, 9), [(0.6, 0.2, 0.8, 0.4)])] +entities = [ + ("apple", (0, 3), [(0.2, 0.3, 0.4, 0.5)]), + ("banana", (4, 9), [(0.6, 0.2, 0.8, 0.4)]), +] kosmos.draw_entity_boxes_on_image(image, entities, show=True) ``` @@ -139,24 +154,38 @@ from swarms.models.kosmos_two import Kosmos kosmos = Kosmos() image = kosmos.get_image("https://example.com/image.jpg") -entities = [("apple", (0, 3), [(0.2, 0.3, 0.4, 0.5)]), ("banana", (4, 9), [(0.6, 0.2, 0.8, 0.4)])] +entities = [ + ("apple", (0, 3), [(0.2, 0.3, 0.4, 0.5)]), + ("banana", (4, 9), [(0.6, 0.2, 0.8, 0.4)]), +] kosmos.draw_entity_boxes_on_image(image, entities, show=True) ``` 9. Generate Boxes for Entities: ```python -entities = [("apple", (0, 3), [(0.2, 0.3, 0.4, 0.5)]), ("banana", (4, 9), [(0.6, 0.2, 0.8, 0.4)])] -image = kosmos.generate_boxes("Find the apple and the banana in the image.", "https://example.com/image.jpg") +entities = [ + ("apple", (0, 3), [(0.2, 0.3, 0.4, 0.5)]), + ("banana", (4, 9), [(0.6, 0.2, 0.8, 0.4)]), +] +image = kosmos.generate_boxes( + "Find the apple and the banana in the image.", "https://example.com/image.jpg" +) ``` ### Example 8 - Generating Boxes for Entities ```python from swarms.models.kosmos_two import Kosmos + kosmos = Kosmos() -entities = [("apple", (0, 3), [(0.2, 0.3, 0.4, 0.5)]), ("banana", (4, 9), [(0.6, 0.2, 0.8, 0.4)])] -image = kosmos.generate_boxes("Find the apple and the banana in the image.", "https://example.com/image.jpg") +entities = [ + ("apple", (0, 3), [(0.2, 0.3, 0.4, 0.5)]), + ("banana", (4, 9), [(0.6, 0.2, 0.8, 0.4)]), +] +image = kosmos.generate_boxes( + "Find the apple and the banana in the image.", "https://example.com/image.jpg" +) ``` ## How Kosmos Works diff --git a/docs/swarms/models/mistral.md b/docs/swarms/models/mistral.md index c8dc179c..6c38cd97 100644 --- a/docs/swarms/models/mistral.md +++ b/docs/swarms/models/mistral.md @@ -150,7 +150,6 @@ Example: ```python from swarms.models import Mistral - model = Mistral() task = "Translate the following English text to French: 'Hello, how are you?'" result = model.run(task) diff --git a/docs/swarms/models/mpt.md b/docs/swarms/models/mpt.md index 41f3ec74..0592284b 100644 --- a/docs/swarms/models/mpt.md +++ b/docs/swarms/models/mpt.md @@ -52,10 +52,10 @@ class MPT7B: from swarms.models import MPT7B # Initialize the MPT7B class -mpt = MPT7B('mosaicml/mpt-7b-storywriter', 'EleutherAI/gpt-neox-20b', max_tokens=150) +mpt = MPT7B("mosaicml/mpt-7b-storywriter", "EleutherAI/gpt-neox-20b", max_tokens=150) # Generate text -output = mpt.run('generate', 'Once upon a time in a land far, far away...') +output = mpt.run("generate", "Once upon a time in a land far, far away...") print(output) ``` @@ -77,13 +77,16 @@ print(outputs) ```python import asyncio + from swarms.models import MPT7B # Initialize the MPT7B class -mpt = MPT7B('mosaicml/mpt-7b-storywriter', 'EleutherAI/gpt-neox-20b', max_tokens=150) +mpt = MPT7B("mosaicml/mpt-7b-storywriter", "EleutherAI/gpt-neox-20b", max_tokens=150) # Generate text asynchronously -output = asyncio.run(mpt.run_async('generate', 'Once upon a time in a land far, far away...')) +output = asyncio.run( + mpt.run_async("generate", "Once upon a time in a land far, far away...") +) print(output) ``` diff --git a/docs/swarms/models/openai.md b/docs/swarms/models/openai.md index f173619d..ae547631 100644 --- a/docs/swarms/models/openai.md +++ b/docs/swarms/models/openai.md @@ -168,7 +168,11 @@ prompt = "Translate the following English text to French: 'Hello, how are you?'" generated_text = openai.generate(prompt, max_tokens=50) # Generate text from multiple prompts -prompts = ["Translate this: 'Good morning' to Spanish.", "Summarize the following article:", article_text] +prompts = [ + "Translate this: 'Good morning' to Spanish.", + "Summarize the following article:", + article_text, +] generated_texts = openai.generate(prompts, max_tokens=100) # Generate text asynchronously @@ -188,7 +192,7 @@ custom_options = { "max_tokens": 100, "top_p": 0.9, "frequency_penalty": 0.2, - "presence_penalty": 0.4 + "presence_penalty": 0.4, } generated_text = openai.generate(prompt, **custom_options) ``` diff --git a/docs/swarms/models/openai_chat.md b/docs/swarms/models/openai_chat.md index a2ef9811..d7d9b2eb 100644 --- a/docs/swarms/models/openai_chat.md +++ b/docs/swarms/models/openai_chat.md @@ -150,7 +150,9 @@ user_message = "User: Tell me another joke." response = openai_chat.generate([user_message]) # Print the generated response -print(response[0][0].text) # Output: "Assistant: Why don't scientists trust atoms? Because they make up everything!" +print( + response[0][0].text +) # Output: "Assistant: Why don't scientists trust atoms? Because they make up everything!" ``` ### Example 3: Asynchronous Generation @@ -158,12 +160,14 @@ print(response[0][0].text) # Output: "Assistant: Why don't scientists trust ato ```python import asyncio + # Define an asynchronous function for generating responses async def generate_responses(): user_message = "User: Tell me a fun fact." async for chunk in openai_chat.stream([user_message]): print(chunk.text) + # Run the asynchronous generation function asyncio.run(generate_responses()) ``` diff --git a/docs/swarms/models/vilt.md b/docs/swarms/models/vilt.md index e2c6f325..2cb56b22 100644 --- a/docs/swarms/models/vilt.md +++ b/docs/swarms/models/vilt.md @@ -26,20 +26,26 @@ To use the Vilt model, follow these steps: ```python from swarms.models import Vilt + model = Vilt() ``` 2. Call the model with a text question and an image URL: ```python -output = model("What is this image?", "http://images.cocodataset.org/val2017/000000039769.jpg") +output = model( + "What is this image?", "http://images.cocodataset.org/val2017/000000039769.jpg" +) ``` ### Example 1 - Image Questioning ```python model = Vilt() -output = model("What are the objects in this image?", "http://images.cocodataset.org/val2017/000000039769.jpg") +output = model( + "What are the objects in this image?", + "http://images.cocodataset.org/val2017/000000039769.jpg", +) print(output) ``` @@ -47,7 +53,10 @@ print(output) ```python model = Vilt() -output = model("Describe the scene in this image.", "http://images.cocodataset.org/val2017/000000039769.jpg") +output = model( + "Describe the scene in this image.", + "http://images.cocodataset.org/val2017/000000039769.jpg", +) print(output) ``` @@ -55,7 +64,10 @@ print(output) ```python model = Vilt() -output = model("Tell me more about the landmark in this image.", "http://images.cocodataset.org/val2017/000000039769.jpg") +output = model( + "Tell me more about the landmark in this image.", + "http://images.cocodataset.org/val2017/000000039769.jpg", +) print(output) ``` diff --git a/docs/swarms/models/vllm.md b/docs/swarms/models/vllm.md index 06a7c8f8..778c1b2b 100644 --- a/docs/swarms/models/vllm.md +++ b/docs/swarms/models/vllm.md @@ -63,7 +63,7 @@ custom_vllm = vLLM( trust_remote_code=True, revision="abc123", temperature=0.7, - top_p=0.8 + top_p=0.8, ) ``` @@ -108,7 +108,7 @@ custom_vllm = vLLM( trust_remote_code=True, revision="abc123", temperature=0.7, - top_p=0.8 + top_p=0.8, ) # Generate text with custom configuration @@ -128,7 +128,7 @@ vllm = vLLM() tasks = [ "Translate the following sentence to French: 'Hello, world!'", "Write a short story set in a futuristic world.", - "Summarize the main points of a news article about climate change." + "Summarize the main points of a news article about climate change.", ] for task in tasks: diff --git a/docs/swarms/models/zephyr.md b/docs/swarms/models/zephyr.md index d9522711..76782ead 100644 --- a/docs/swarms/models/zephyr.md +++ b/docs/swarms/models/zephyr.md @@ -45,6 +45,7 @@ To use the Zephyr model, follow these steps: ```python from swarms.models import Zephyr + model = Zephyr(max_new_tokens=300, temperature=0.7, top_k=50, top_p=0.95) ``` diff --git a/docs/swarms/structs/abstractswarm.md b/docs/swarms/structs/abstractswarm.md index 78e28493..6bdf736b 100644 --- a/docs/swarms/structs/abstractswarm.md +++ b/docs/swarms/structs/abstractswarm.md @@ -47,9 +47,11 @@ The `AbstractSwarm` class is an abstract base class that serves as the foundatio ```python from abc import ABC, abstractmethod -from typing import Optional, List, Dict, Any +from typing import List + from swarms.swarms.base import AbstractWorker + class AbstractSwarm(ABC): """ Abstract class for swarm simulation architectures @@ -58,12 +60,12 @@ class AbstractSwarm(ABC): --------- ... """ + # The class definition and constructor are provided here. @abstractmethod def __init__(self, workers: List["AbstractWorker"]): """Initialize the swarm with workers""" - pass # Other abstract methods are listed here. ``` diff --git a/docs/swarms/structs/agent.md b/docs/swarms/structs/agent.md index f04bba02..8408b0b8 100644 --- a/docs/swarms/structs/agent.md +++ b/docs/swarms/structs/agent.md @@ -68,7 +68,9 @@ final_response = agent.run(initial_task) You can collect feedback during the conversation using the `provide_feedback` method: ```python -agent.provide_feedback("Generate an SOP for new sales employees on the best cold sales practices") +agent.provide_feedback( + "Generate an SOP for new sales employees on the best cold sales practices" +) ``` ### Stopping Condition @@ -78,9 +80,11 @@ You can define a custom stopping condition using a function. For example, you ca ```python from swarms.structs import Agent + def stop_when_repeats(response: str) -> bool: return "Stop" in response.lower() + agent = Agent(llm=my_language_model, max_loops=5, stopping_condition=stop_when_repeats) ``` @@ -107,9 +111,9 @@ Here are three usage examples: ### Example 1: Simple Conversation ```python -from swarms.structs import Agent # Select any Language model from the models folder from swarms.models import Mistral, OpenAIChat +from swarms.structs import Agent llm = Mistral() # llm = OpenAIChat() @@ -128,9 +132,11 @@ final_response = agent.run(initial_task) ```python from swarms.structs import Agent + def stop_when_repeats(response: str) -> bool: return "Stop" in response.lower() + agent = Agent(llm=llm, max_loops=5, stopping_condition=stop_when_repeats) ``` diff --git a/docs/swarms/structs/artifact.md b/docs/swarms/structs/artifact.md index 52444d9f..9e00f083 100644 --- a/docs/swarms/structs/artifact.md +++ b/docs/swarms/structs/artifact.md @@ -41,9 +41,7 @@ class Artifact(BaseModel): ) relative_path: Optional[str] = Field( None, - description=( - "Relative path of the artifact in the agent's workspace" - ), + description=("Relative path of the artifact in the agent's workspace"), example="python/code/", ) ``` @@ -64,7 +62,7 @@ from swarms.structs import Artifact artifact_instance = Artifact( artifact_id="b225e278-8b4c-4f99-a696-8facf19f0e56", file_name="main.py", - relative_path="python/code/" + relative_path="python/code/", ) ``` @@ -85,8 +83,7 @@ If the `relative_path` attribute is not provided during artifact creation, it wi ```python artifact_instance_no_path = Artifact( - artifact_id="c280s347-9b7d-3c68-m337-7abvf50j23k", - file_name="script.js" + artifact_id="c280s347-9b7d-3c68-m337-7abvf50j23k", file_name="script.js" ) print(artifact_instance_no_path.relative_path) diff --git a/docs/swarms/structs/artifactupload.md b/docs/swarms/structs/artifactupload.md index 533f7d53..90b30f58 100644 --- a/docs/swarms/structs/artifactupload.md +++ b/docs/swarms/structs/artifactupload.md @@ -15,9 +15,7 @@ class ArtifactUpload(BaseModel): file: bytes = Field(..., description="File to upload") relative_path: Optional[str] = Field( None, - description=( - "Relative path of the artifact in the agent's workspace" - ), + description=("Relative path of the artifact in the agent's workspace"), example="python/code/", ) ``` @@ -32,10 +30,12 @@ The `ArtifactUpload` class is used to create an instance of an artifact upload. from swarms.structs import ArtifactUpload # Uploading a file with no relative path -upload_no_path = ArtifactUpload(file=b'example_file_contents') +upload_no_path = ArtifactUpload(file=b"example_file_contents") # Uploading a file with a relative path -upload_with_path = ArtifactUpload(file=b'example_file_contents', relative_path="python/code/") +upload_with_path = ArtifactUpload( + file=b"example_file_contents", relative_path="python/code/" +) ``` In the above example, `upload_no_path` is an instance of `ArtifactUpload` with no specified `relative_path`, whereas `upload_with_path` is an instance of `ArtifactUpload` with the `relative_path` set to "python/code/". diff --git a/docs/swarms/structs/autoscaler.md b/docs/swarms/structs/autoscaler.md index 703ae860..6d07d3b1 100644 --- a/docs/swarms/structs/autoscaler.md +++ b/docs/swarms/structs/autoscaler.md @@ -36,7 +36,9 @@ Initializes the `AutoScaler` with a predefined number of agents and sets up conf ```python from swarms import AutoScaler -scaler = AutoScaler(initial_agents=5, scale_up_factor=3, idle_threshold=0.1, busy_threshold=0.8) +scaler = AutoScaler( + initial_agents=5, scale_up_factor=3, idle_threshold=0.1, busy_threshold=0.8 +) ``` --- @@ -140,7 +142,9 @@ scaler.start() from swarms import AutoScaler # Initialize the scaler -auto_scaler = AutoScaler(initial_agents=15, scale_up_factor=2, idle_threshold=0.2, busy_threshold=0.7) +auto_scaler = AutoScaler( + initial_agents=15, scale_up_factor=2, idle_threshold=0.2, busy_threshold=0.7 +) # Start the monitoring and task processing auto_scaler.start() @@ -161,7 +165,6 @@ auto_scaler.start() for i in range(100): # Adding tasks auto_scaler.add_task(f"Task {i}") - ``` diff --git a/docs/swarms/structs/baseworkflow.md b/docs/swarms/structs/baseworkflow.md index ee048af8..2cb4b5eb 100644 --- a/docs/swarms/structs/baseworkflow.md +++ b/docs/swarms/structs/baseworkflow.md @@ -13,19 +13,19 @@ Base class for workflows. Source Code: ```python - class BaseWorkflow(BaseStructure): - """ - Base class for workflows. +class BaseWorkflow(BaseStructure): +""" +Base class for workflows. - Attributes: - task_pool (list): A list to store tasks. + Attributes: + task_pool (list): A list to store tasks. - Methods: - add(task: Task = None, tasks: List[Task] = None, *args, **kwargs): - Adds a task or a list of tasks to the task pool. - run(): - Abstract method to run the workflow. - """ + Methods: + add(task: Task = None, tasks: List[Task] = None, *args, **kwargs): + Adds a task or a list of tasks to the task pool. + run(): + Abstract method to run the workflow. +""" ``` For the usage examples and additional in-depth documentation please visit [BaseWorkflow](https://github.com/swarms-modules/structs/blob/main/baseworkflow.md#swarms-structs) diff --git a/docs/swarms/structs/groupchatmanager.md b/docs/swarms/structs/groupchatmanager.md index 623a1fbf..cb2ce035 100644 --- a/docs/swarms/structs/groupchatmanager.md +++ b/docs/swarms/structs/groupchatmanager.md @@ -24,7 +24,7 @@ agents = Agent() manager = GroupChatManager(groupchat, selector) # Call the group chat manager passing a specific chat task -result = manager('Discuss the agenda for the upcoming meeting') +result = manager("Discuss the agenda for the upcoming meeting") ``` Explanation: @@ -67,9 +67,7 @@ class GroupChatManager: Returns: str: The response from the group chat. """ - self.groupchat.messages.append( - {"role": self.selector.name, "content": task} - ) + self.groupchat.messages.append({"role": self.selector.name, "content": task}) for i in range(self.groupchat.max_round): speaker = self.groupchat.select_speaker( last_speaker=self.selector, selector=self.selector diff --git a/docs/swarms/structs/json.md b/docs/swarms/structs/json.md index 2a5040e5..a495a0ce 100644 --- a/docs/swarms/structs/json.md +++ b/docs/swarms/structs/json.md @@ -78,16 +78,17 @@ Suppose we have a JSON Schema in `config_schema.json` for application configurat Now we'll create a subclass `AppConfig` that uses this schema. ```python -import json from swarms.structs import JSON + class AppConfig(JSON): def __init__(self, schema_path): super().__init__(schema_path) def validate(self, config_data): # Here we'll use a JSON Schema validation library like jsonschema - from jsonschema import validate, ValidationError + from jsonschema import ValidationError, validate + try: validate(instance=config_data, schema=self.schema) except ValidationError as e: @@ -95,15 +96,13 @@ class AppConfig(JSON): return False return True + # Main Example Usage if __name__ == "__main__": - config = { - "debug": True, - "window_size": [800, 600] - } + config = {"debug": True, "window_size": [800, 600]} - app_config = AppConfig('config_schema.json') + app_config = AppConfig("config_schema.json") if app_config.validate(config): print("Config is valid!") diff --git a/docs/swarms/structs/majorityvoting.md b/docs/swarms/structs/majorityvoting.md index 97d28847..4c7f7612 100644 --- a/docs/swarms/structs/majorityvoting.md +++ b/docs/swarms/structs/majorityvoting.md @@ -82,9 +82,11 @@ Executes the given task by all participating agents and aggregates the results t from swarms.structs.agent import Agent from swarms.structs.majority_voting import MajorityVoting + def create_agent(name): return Agent(name) + agents = [create_agent(name) for name in ["GPT-3", "Codex", "Tabnine"]] majority_voting = MajorityVoting(agents) result = majority_voting.run("What is the capital of France?") diff --git a/docs/swarms/structs/nonlinearworkflow.md b/docs/swarms/structs/nonlinearworkflow.md index 1974de24..09d00074 100644 --- a/docs/swarms/structs/nonlinearworkflow.md +++ b/docs/swarms/structs/nonlinearworkflow.md @@ -60,7 +60,7 @@ task3 = Task(llm, "Find a hotel in Paris") # Initialize the NonlinearWorkflow workflow = NonlinearWorkflow() # Add tasks to the workflow with dependencies -workflow.add(task1, task2.name) +workflow.add(task1, task2.name) workflow.add(task2, task3.name) workflow.add(task3, "OpenAIChat Initialization") # Execute the workflow @@ -82,7 +82,7 @@ task3 = Task(llm, "Find a hotel in Paris") # Initialize the NonlinearWorkflow workflow = NonlinearWorkflow() # Add tasks to the workflow with dependencies -workflow.add(task1) +workflow.add(task1) workflow.add(task2, task1.name) workflow.add(task3, task1.name, task2.name) # Execute the workflow diff --git a/docs/swarms/structs/sequential_workflow.md b/docs/swarms/structs/sequential_workflow.md index 6f6d8954..74ee1acb 100644 --- a/docs/swarms/structs/sequential_workflow.md +++ b/docs/swarms/structs/sequential_workflow.md @@ -310,9 +310,7 @@ from swarms.structs import Agent from swarms.structs.sequential_workflow import SequentialWorkflow # Example usage -api_key = ( - "" # Your actual API key here -) +api_key = "" # Your actual API key here # Initialize the language agent llm = OpenAIChat( @@ -350,9 +348,7 @@ from swarms.structs import Agent from swarms.structs.sequential_workflow import SequentialWorkflow # Example usage -api_key = ( - "" # Your actual API key here -) +api_key = "" # Your actual API key here # Initialize the language agent llm = OpenAIChat( @@ -393,9 +389,7 @@ from swarms.structs import Agent from swarms.structs.sequential_workflow import SequentialWorkflow # Example usage -api_key = ( - "" # Your actual API key here -) +api_key = "" # Your actual API key here # Initialize the language agent llm = OpenAIChat( @@ -436,9 +430,7 @@ from swarms.structs import Agent from swarms.structs.sequential_workflow import SequentialWorkflow # Example usage -api_key = ( - "" # Your actual API key here -) +api_key = "" # Your actual API key here # Initialize the language agent llm = OpenAIChat( diff --git a/docs/swarms/structs/stackoverflowswarm.md b/docs/swarms/structs/stackoverflowswarm.md index 5f5630df..c3e83b47 100644 --- a/docs/swarms/structs/stackoverflowswarm.md +++ b/docs/swarms/structs/stackoverflowswarm.md @@ -58,11 +58,13 @@ The design of the `StackOverflowSwarm` class is intended to allow easy tracking from swarms.structs.agent import Agent from swarms.structs.stack_overflow_swarm import StackOverflowSwarm + # Define custom Agents with some logic (placeholder for actual Agent implementation) class CustomAgent(Agent): def run(self, conversation, *args, **kwargs): return "This is a response from CustomAgent." + # Initialize agents agent1 = CustomAgent(ai_name="Agent1") agent2 = CustomAgent(ai_name="Agent2") diff --git a/docs/swarms/structs/stepinput.md b/docs/swarms/structs/stepinput.md index ad4be016..2230ccdf 100644 --- a/docs/swarms/structs/stepinput.md +++ b/docs/swarms/structs/stepinput.md @@ -12,10 +12,7 @@ The `StepInput` class is defined as follows: class StepInput(BaseModel): __root__: Any = Field( ..., - description=( - "Input parameters for the task step. Any value is" - " allowed." - ), + description=("Input parameters for the task step. Any value is" " allowed."), example='{\n"file_to_refactor": "models.py"\n}', ) ``` @@ -29,10 +26,7 @@ The `StepInput` class is designed to accept any input value, providing flexibili ```python from swarms.structs import StepInput -input_params = { - "file_to_refactor": "models.py", - "refactor_method": "code" -} +input_params = {"file_to_refactor": "models.py", "refactor_method": "code"} step_input = StepInput(__root__=input_params) ``` @@ -42,10 +36,7 @@ In this example, we import the `StepInput` class from the `swarms.structs` libra ```python from swarms.structs import StepInput -input_params = { - "input_path": "data.csv", - "output_path": "result.csv" -} +input_params = {"input_path": "data.csv", "output_path": "result.csv"} step_input = StepInput(__root__=input_params) ``` @@ -56,7 +47,7 @@ In this example, we again create an instance of `StepInput` by passing a diction from swarms.structs import StepInput file_path = "config.json" -with open(file_path, 'r') as f: +with open(file_path) as f: input_data = json.load(f) step_input = StepInput(__root__=input_data) diff --git a/docs/swarms/structs/swarmnetwork.md b/docs/swarms/structs/swarmnetwork.md index 2c2f74e5..efbdf279 100644 --- a/docs/swarms/structs/swarmnetwork.md +++ b/docs/swarms/structs/swarmnetwork.md @@ -28,6 +28,7 @@ The `SwarmNetwork` class has the following parameters: ```python from swarms.structs.agent import Agent from swarms.structs.swarm_net import SwarmNetwork + agent = Agent() swarm = SwarmNetwork(agents=[agent]) swarm.add_task("task") @@ -41,6 +42,7 @@ The `SwarmNetwork` class has the following parameters: ```python from swarms.structs.agent import Agent from swarms.structs.swarm_net import SwarmNetwork + agent = Agent() swarm = SwarmNetwork(agents=[agent]) await swarm.async_add_task("task") @@ -57,6 +59,7 @@ The `SwarmNetwork` class has the following parameters: ```python from swarms.structs.agent import Agent from swarms.structs.swarm_net import SwarmNetwork + agent = Agent() swarm = SwarmNetwork(agents=[agent]) swarm.run_single_agent(agent_id, "task") @@ -72,6 +75,7 @@ The `SwarmNetwork` class has the following parameters: ```python from swarms.structs.agent import Agent from swarms.structs.swarm_net import SwarmNetwork + agent = Agent() swarm = SwarmNetwork(agents=[agent]) swarm.run_many_agents("task") @@ -85,6 +89,7 @@ The `SwarmNetwork` class has the following parameters: ```python from swarms.structs.agent import Agent from swarms.structs.swarm_net import SwarmNetwork + agent = Agent() swarm = SwarmNetwork(agents=[agent]) swarm.list_agents() @@ -98,6 +103,7 @@ The `SwarmNetwork` class has the following parameters: ```python from swarms.structs.agent import Agent from swarms.structs.swarm_net import SwarmNetwork + agent = Agent() swarm = SwarmNetwork() swarm.add_agent(agent) @@ -111,6 +117,7 @@ The `SwarmNetwork` class has the following parameters: ```python from swarms.structs.agent import Agent from swarms.structs.swarm_net import SwarmNetwork + agent = Agent() swarm = SwarmNetwork(agents=[agent]) swarm.remove_agent(agent_id) @@ -124,6 +131,7 @@ The `SwarmNetwork` class has the following parameters: ```python from swarms.structs.agent import Agent from swarms.structs.swarm_net import SwarmNetwork + swarm = SwarmNetwork() swarm.scale_up(num_agents=5) ``` @@ -136,6 +144,7 @@ The `SwarmNetwork` class has the following parameters: ```python from swarms.structs.agent import Agent from swarms.structs.swarm_net import SwarmNetwork + swarm = SwarmNetwork(agents=[agent1, agent2, agent3, agent4, agent5]) swarm.scale_down(num_agents=2) ``` @@ -146,6 +155,7 @@ The `SwarmNetwork` class has the following parameters: ```python from swarms.structs.agent import Agent from swarms.structs.swarm_net import SwarmNetwork + agent = Agent() swarm = SwarmNetwork(agents=[agent]) swarm.create_apis_for_agents() diff --git a/docs/swarms/structs/task.md b/docs/swarms/structs/task.md index 7e829b66..f6a72aef 100644 --- a/docs/swarms/structs/task.md +++ b/docs/swarms/structs/task.md @@ -8,8 +8,9 @@ ```python # Example 1: Creating and executing a Task -from swarms.structs import Task, Agent from swarms.models import OpenAIChat +from swarms.structs import Agent, Task + agent = Agent(llm=OpenAIChat(openai_api_key=""), max_loops=1, dashboard=False) task = Task(agent=agent) task.execute("What's the weather in miami") diff --git a/docs/swarms/structs/taskinput.md b/docs/swarms/structs/taskinput.md index e7a0be68..8e9ed33f 100644 --- a/docs/swarms/structs/taskinput.md +++ b/docs/swarms/structs/taskinput.md @@ -20,11 +20,14 @@ The `TaskInput` class encapsulates the input parameters in a structured format. #### Usage Example 1: Using TaskInput for Debugging ```python from pydantic import BaseModel, Field + from swarms.structs import TaskInput + class DebugInput(TaskInput): debug: bool + # Creating an instance of DebugInput debug_params = DebugInput(__root__={"debug": True}) @@ -35,11 +38,14 @@ print(debug_params.debug) # Output: True #### Usage Example 2: Using TaskInput for Task Modes ```python from pydantic import BaseModel, Field + from swarms.structs import TaskInput + class ModeInput(TaskInput): mode: str + # Creating an instance of ModeInput mode_params = ModeInput(__root__={"mode": "benchmarks"}) @@ -50,12 +56,15 @@ print(mode_params.mode) # Output: benchmarks #### Usage Example 3: Using TaskInput with Arbitrary Parameters ```python from pydantic import BaseModel, Field + from swarms.structs import TaskInput + class ArbitraryInput(TaskInput): message: str quantity: int + # Creating an instance of ArbitraryInput arbitrary_params = ArbitraryInput(__root__={"message": "Hello, world!", "quantity": 5}) diff --git a/docs/swarms/structs/taskqueuebase.md b/docs/swarms/structs/taskqueuebase.md index a8d4c599..0feee648 100644 --- a/docs/swarms/structs/taskqueuebase.md +++ b/docs/swarms/structs/taskqueuebase.md @@ -7,13 +7,14 @@ The `swarms.structs` library is a key component of a multi-agent system's task m ## TaskQueueBase Class ```python -from abc import ABC, abstractmethod import threading +from abc import ABC, abstractmethod # Include any additional imports that are relevant to decorators and other classes such as Task and Agent if needed # Definition of the synchronized_queue decorator (if necessary) + class TaskQueueBase(ABC): def __init__(self): self.lock = threading.Lock() @@ -27,12 +28,12 @@ class TaskQueueBase(ABC): @abstractmethod def get_task(self, agent: Agent) -> Task: pass - + @synchronized_queue @abstractmethod def complete_task(self, task_id: str): pass - + @synchronized_queue @abstractmethod def reset_task(self, task_id: str): @@ -65,11 +66,12 @@ Below are three examples of how the `TaskQueueBase` class can be implemented and ```python # file: basic_queue.py -import threading -from swarms.structs import TaskQueueBase, Task, Agent # Assume synchronized_queue decorator is defined elsewhere -from decorators import synchronized_queue +from decorators import synchronized_queue + +from swarms.structs import Agent, Task, TaskQueueBase + class BasicTaskQueue(TaskQueueBase): def __init__(self): @@ -95,6 +97,7 @@ class BasicTaskQueue(TaskQueueBase): # Logic to reset the task pass + # Usage queue = BasicTaskQueue() # Add task, assuming Task object is created diff --git a/docs/swarms/tokenizers/basetokenizer.md b/docs/swarms/tokenizers/basetokenizer.md index 16db07fc..8047f301 100644 --- a/docs/swarms/tokenizers/basetokenizer.md +++ b/docs/swarms/tokenizers/basetokenizer.md @@ -25,6 +25,7 @@ The `BaseTokenizer` class provides the structure for creating tokenizers. It inc ```python from swarms.tokenizers import BaseTokenizer + class SimpleTokenizer(BaseTokenizer): def count_tokens(self, text: Union[str, List[dict]]) -> int: @@ -33,10 +34,11 @@ class SimpleTokenizer(BaseTokenizer): return len(text.split()) elif isinstance(text, list): # Assume list of dictionaries with 'token' key - return sum(len(item['token'].split()) for item in text) + return sum(len(item["token"].split()) for item in text) else: raise TypeError("Unsupported type for text") + # Usage example tokenizer = SimpleTokenizer(max_tokens=100) text = "This is an example sentence to tokenize." diff --git a/docs/swarms/tokenizers/coheretokenizer.md b/docs/swarms/tokenizers/coheretokenizer.md index 5a11b8ce..197346a3 100644 --- a/docs/swarms/tokenizers/coheretokenizer.md +++ b/docs/swarms/tokenizers/coheretokenizer.md @@ -57,7 +57,7 @@ def count_tokens(self, text: str | list) -> int: Args: text (str | list): The input text to tokenize. - + Returns: int: The number of tokens in the text. @@ -82,13 +82,14 @@ First, the Cohere client must be initialized and passed in to create an instance ```python from cohere import Client + from swarms.tokenizers import CohereTokenizer # Initialize Cohere client with your API key -cohere_client = Client('your-api-key') +cohere_client = Client("your-api-key") # Instantiate the tokenizer -tokenizer = CohereTokenizer(model='your-model-name', client=cohere_client) +tokenizer = CohereTokenizer(model="your-model-name", client=cohere_client) ``` ### Count Tokens Example 1 diff --git a/docs/swarms/tokenizers/huggingfacetokenizer.md b/docs/swarms/tokenizers/huggingfacetokenizer.md index 8cf7fd77..9330b3be 100644 --- a/docs/swarms/tokenizers/huggingfacetokenizer.md +++ b/docs/swarms/tokenizers/huggingfacetokenizer.md @@ -76,7 +76,7 @@ Tokenizes given text when the object is called like a function. from swarms.tokenizers import HuggingFaceTokenizer # Initialize the tokenizer with the path to your tokenizer model. -tokenizer = HuggingFaceTokenizer('/path/to/your/model_dir') +tokenizer = HuggingFaceTokenizer("/path/to/your/model_dir") ``` ### 2. Encoding Text diff --git a/docs/swarms/tokenizers/openaitokenizer.md b/docs/swarms/tokenizers/openaitokenizer.md index 9e051c32..94918261 100644 --- a/docs/swarms/tokenizers/openaitokenizer.md +++ b/docs/swarms/tokenizers/openaitokenizer.md @@ -50,7 +50,7 @@ Given the extensive nature of this class, several examples are provided for each ```python from swarms.tokenizers import OpenAITokenizer -tokenizer = OpenAITokenizer(model='gpt-4') +tokenizer = OpenAITokenizer(model="gpt-4") ``` This example creates a new instance of `OpenAITokenizer` set to work with the GPT-4 model. @@ -61,7 +61,7 @@ This example creates a new instance of `OpenAITokenizer` set to work with the GP text = "Hello, this is an example text to tokenize." # Initialize the tokenizer -tokenizer = OpenAITokenizer(model='gpt-4') +tokenizer = OpenAITokenizer(model="gpt-4") # Count tokens num_tokens = tokenizer.count_tokens(text) @@ -78,7 +78,7 @@ messages = [ {"name": "Bob", "message": "I'm good! Just working on some code."}, ] -tokenizer = OpenAITokenizer(model='gpt-3.5-turbo') +tokenizer = OpenAITokenizer(model="gpt-3.5-turbo") # Count tokens for a list of messages num_tokens = tokenizer.len(messages, model="gpt-3.5-turbo-0613") diff --git a/docs/swarms/tokenizers/sentencepiecetokenizer.md b/docs/swarms/tokenizers/sentencepiecetokenizer.md index 390dbb07..ac6f2a2f 100644 --- a/docs/swarms/tokenizers/sentencepiecetokenizer.md +++ b/docs/swarms/tokenizers/sentencepiecetokenizer.md @@ -14,7 +14,7 @@ In `SentencePieceTokenizer`, the tokenization process is language-agnostic and e class SentencePieceTokenizer: """ Tokenizer of sentencepiece. - + Args: model_file (str): the path of the tokenizer model """ @@ -45,7 +45,7 @@ Parameter | Type | Description ```python from swarms.tokenizers import SentencePieceTokenizer -tokenizer = SentencePieceTokenizer(model_file='your_model.model') +tokenizer = SentencePieceTokenizer(model_file="your_model.model") ``` ### Properties: Vocabulary Information diff --git a/docs/swarms/utils/check_device.md b/docs/swarms/utils/check_device.md index bdb8c780..a944dc1f 100644 --- a/docs/swarms/utils/check_device.md +++ b/docs/swarms/utils/check_device.md @@ -36,16 +36,18 @@ This function does not take any mandatory argument. However, it supports optiona ### Example 1: Basic Usage ```python -import torch import logging + +import torch + from swarms.utils import check_device # Basic usage device = check_device( - log_level=logging.INFO, - memory_threshold=0.8, - capability_threshold=3.5, - return_type="list" + log_level=logging.INFO, + memory_threshold=0.8, + capability_threshold=3.5, + return_type="list", ) ``` @@ -53,24 +55,24 @@ device = check_device( ```python import torch -import logging + from swarms.utils import check_device # When CUDA is not available device = check_device() -print(device) # If CUDA is not available it should return torch.device('cpu') +print(device) # If CUDA is not available it should return torch.device('cpu') ``` ### Example 3: Multiple GPU Available ```python import torch -import logging + from swarms.utils import check_device # When multiple GPUs are available device = check_device() -print(device) # Should return a list of available GPU devices +print(device) # Should return a list of available GPU devices ``` ## Tips and Additional Information diff --git a/docs/swarms/utils/extract_code_from_markdown.md b/docs/swarms/utils/extract_code_from_markdown.md index f6f76835..fdef5018 100644 --- a/docs/swarms/utils/extract_code_from_markdown.md +++ b/docs/swarms/utils/extract_code_from_markdown.md @@ -57,14 +57,13 @@ Below are three examples of how you might use this function: Extracting code blocks from a simple markdown string. ```python -import re from swarms.utils import extract_code_from_markdown -markdown_string = '''# Example +markdown_string = """# Example This is an example of a code block: ```python print("Hello World!") -``` ''' +``` """ print(extract_code_from_markdown(markdown_string)) ``` @@ -75,13 +74,15 @@ Extracting code blocks from a markdown file. ```python import re + def extract_code_from_markdown(markdown_content: str) -> str: pattern = r"```(?:\w+\n)?(.*?)```" matches = re.findall(pattern, markdown_content, re.DOTALL) return "\n".join(code.strip() for code in matches) + # Assume that 'example.md' contains multiple code blocks -with open('example.md', 'r') as file: +with open("example.md") as file: markdown_content = file.read() print(extract_code_from_markdown(markdown_content)) ``` @@ -93,17 +94,20 @@ Using the function in a pipeline to extract and then analyze code blocks. ```python import re + def extract_code_from_markdown(markdown_content: str) -> str: pattern = r"```(?:\w+\n)?(.*?)```" matches = re.findall(pattern, markdown_content, re.DOTALL) return "\n".join(code.strip() for code in matches) + def analyze_code_blocks(code: str): # Add your analysis logic here - pass + pass + # Assume that 'example.md' contains multiple code blocks -with open('example.md', 'r') as file: +with open("example.md") as file: markdown_content = file.read() code_blocks = extract_code_from_markdown(markdown_content) analyze_code_blocks(code_blocks) diff --git a/docs/swarms/utils/find_image_path.md b/docs/swarms/utils/find_image_path.md index 59c9c127..844cbe78 100644 --- a/docs/swarms/utils/find_image_path.md +++ b/docs/swarms/utils/find_image_path.md @@ -40,15 +40,9 @@ The function `find_image_path` performs text parsing and pattern recognition to ```python def find_image_path(text): pattern = r"([A-Za-z]:\\[^:\n]*?\.(png|jpg|jpeg|PNG|JPG|JPEG))|(/[^:\n]*?\.(png|jpg|jpeg|PNG|JPG|JPEG))" - matches = [ - match.group() - for match in re.finditer(pattern, text) - if match.group() - ] + matches = [match.group() for match in re.finditer(pattern, text) if match.group()] matches += [match.replace("\\", "") for match in matches if match] - existing_paths = [ - match for match in matches if os.path.exists(match) - ] + existing_paths = [match for match in matches if os.path.exists(match)] return max(existing_paths, key=len) if existing_paths else None ``` @@ -75,7 +69,9 @@ Consider the case where the text has multiple image paths. from swarms.utils import find_image_path text = "Here is an image path: /home/user/image1.png. Here is another one: C:\\Users\\User\\Documents\\image2.jpeg" -print(find_image_path(text)) # Outputs: the longest image path (depends on your file system and existing files) +print( + find_image_path(text) +) # Outputs: the longest image path (depends on your file system and existing files) ``` **Example 3:** diff --git a/docs/swarms/utils/limit_tokens_from_string.md b/docs/swarms/utils/limit_tokens_from_string.md index b096ebad..bc2cf8cf 100644 --- a/docs/swarms/utils/limit_tokens_from_string.md +++ b/docs/swarms/utils/limit_tokens_from_string.md @@ -73,7 +73,7 @@ from swarms.utils import limit_tokens_from_string string = "In case the method does not find the specified model, it will fall back to gpt2 model." # model -model = "gpt-4" +model = "gpt-4" output = limit_tokens_from_string(string, model=model) ``` diff --git a/docs/swarms/utils/load_model_torch.md b/docs/swarms/utils/load_model_torch.md index ddcd7ee6..7effabb6 100644 --- a/docs/swarms/utils/load_model_torch.md +++ b/docs/swarms/utils/load_model_torch.md @@ -50,12 +50,14 @@ This function can be used directly inside your code as shown in the following ex Loading a model without specifying a device results in the function choosing the most optimal available device automatically. ```python -from swarms.utils import load_model_torch import torch.nn as nn +from swarms.utils import load_model_torch + # Assume `mymodel.pth` is in the current directory model_path = "./mymodel.pth" + # Define your model architecture if the model file only contains state dict class MyModel(nn.Module): def __init__(self): @@ -65,6 +67,7 @@ class MyModel(nn.Module): def forward(self, x): return self.linear(x) + model = MyModel() # Load the model diff --git a/docs/swarms/utils/math_eval.md b/docs/swarms/utils/math_eval.md index 691089f8..19eb9517 100644 --- a/docs/swarms/utils/math_eval.md +++ b/docs/swarms/utils/math_eval.md @@ -15,6 +15,8 @@ Let's say you have two functions: `ground_truth` and `generated_func`, that have @math_eval(ground_truth, generated_func) def test_func(x): return x + + result1, result2 = test_func(5) print(f"Result from ground_truth: {result1}") print(f"Result from generated_func: {result2}") @@ -46,6 +48,7 @@ Here's how to implement the `math_eval` decorator: import functools import logging + def math_eval(func1, func2): """Math evaluation decorator.""" @@ -65,9 +68,7 @@ def math_eval(func1, func2): result2 = None if result1 != result2: - logging.warning( - f"Outputs do not match: {result1} != {result2}" - ) + logging.warning(f"Outputs do not match: {result1} != {result2}") return result1, result2 diff --git a/docs/swarms/utils/metrics_decorator.md b/docs/swarms/utils/metrics_decorator.md index aeafe151..17850ba1 100644 --- a/docs/swarms/utils/metrics_decorator.md +++ b/docs/swarms/utils/metrics_decorator.md @@ -70,6 +70,7 @@ def text_generator(self, text: str): # language generation implementation goes here return tokens + # Instantiate the class and call the decorated function obj = ClassName() obj.text_generator("Hello, world!") diff --git a/docs/swarms/utils/pdf_to_text.md b/docs/swarms/utils/pdf_to_text.md index e5b8dce0..3ec73039 100644 --- a/docs/swarms/utils/pdf_to_text.md +++ b/docs/swarms/utils/pdf_to_text.md @@ -54,7 +54,7 @@ Here is an example of how to use `pdf_to_text`: ```python # Define the path to the pdf file -pdf_path = 'sample.pdf' +pdf_path = "sample.pdf" # Use the function to extract text text = pdf_to_text(pdf_path) diff --git a/docs/swarms/utils/prep_torch_inference.md b/docs/swarms/utils/prep_torch_inference.md index 68598fa8..0fde2503 100644 --- a/docs/swarms/utils/prep_torch_inference.md +++ b/docs/swarms/utils/prep_torch_inference.md @@ -56,7 +56,8 @@ Here are some examples of how you can use the `prep_torch_inference` method. Bef ```python import torch -from swarms.utils import prep_torch_inference, load_model_torch + +from swarms.utils import load_model_torch, prep_torch_inference ``` ### Example 1: Load a model for inference on CPU diff --git a/docs/swarms/utils/print_class_parameters.md b/docs/swarms/utils/print_class_parameters.md index 3c09578f..84e0104f 100644 --- a/docs/swarms/utils/print_class_parameters.md +++ b/docs/swarms/utils/print_class_parameters.md @@ -94,16 +94,16 @@ def print_class_parameters(cls, api_format: bool = False): if api_format: param_dict = {} for name, param in params.items(): - if name == "self": - continue - param_dict[name] = str(param.annotation) + if name == "self": + continue + param_dict[name] = str(param.annotation) return param_dict # Print the parameters for name, param in params.items(): - if name == "self": - continue - print(f"Parameter: {name}, Type: {param.annotation}") + if name == "self": + continue + print(f"Parameter: {name}, Type: {param.annotation}") except Exception as e: print(f"An error occurred while inspecting the class: {e}") diff --git a/docs/swarms/workers/base.md b/docs/swarms/workers/base.md index 8991210b..9da45ba3 100644 --- a/docs/swarms/workers/base.md +++ b/docs/swarms/workers/base.md @@ -37,43 +37,46 @@ class AbstractWorker: Args: name (str): Name of the worker. """ - + @property def name(self): """Get the name of the worker.""" - pass def run(self, task: str): """Run the worker agent once.""" - pass - def send(self, message: Union[Dict, str], recipient, request_reply: Optional[bool] = None): + def send( + self, message: Union[Dict, str], recipient, request_reply: Optional[bool] = None + ): """Send a message to another worker.""" - pass - async def a_send(self, message: Union[Dict, str], recipient, request_reply: Optional[bool] = None): + async def a_send( + self, message: Union[Dict, str], recipient, request_reply: Optional[bool] = None + ): """Send a message to another worker asynchronously.""" - pass - def receive(self, message: Union[Dict, str], sender, request_reply: Optional[bool] = None): + def receive( + self, message: Union[Dict, str], sender, request_reply: Optional[bool] = None + ): """Receive a message from another worker.""" - pass - async def a_receive(self, message: Union[Dict, str], sender, request_reply: Optional[bool] = None): + async def a_receive( + self, message: Union[Dict, str], sender, request_reply: Optional[bool] = None + ): """Receive a message from another worker asynchronously.""" - pass def reset(self): """Reset the worker.""" - pass - def generate_reply(self, messages: Optional[List[Dict]] = None, sender=None, **kwargs) -> Union[str, Dict, None]: + def generate_reply( + self, messages: Optional[List[Dict]] = None, sender=None, **kwargs + ) -> Union[str, Dict, None]: """Generate a reply based on received messages.""" - pass - async def a_generate_reply(self, messages: Optional[List[Dict]] = None, sender=None, **kwargs) -> Union[str, Dict, None]: + async def a_generate_reply( + self, messages: Optional[List[Dict]] = None, sender=None, **kwargs + ) -> Union[str, Dict, None]: """Generate a reply based on received messages asynchronously.""" - pass ``` ### 2.2 Attributes @@ -121,6 +124,7 @@ class MyWorker(AbstractWorker): def run(self, task: str): print(f"{self.name} is performing task: {task}") + worker = MyWorker("Worker1") worker.run("Collect data") ``` @@ -155,6 +159,7 @@ The `a_send()` method is an asynchronous version of the `send()` method, allowin ```python import asyncio + async def main(): worker1 = AbstractWorker("Worker1") worker2 = AbstractWorker("Worker2") @@ -162,6 +167,7 @@ async def main(): message = "Hello, Worker2!" await worker1.a_send(message, worker2) + loop = asyncio.get_event_loop() loop.run_until_complete(main()) ``` @@ -208,6 +214,7 @@ The `a_receive()` method is an asynchronous version of the `receive()` method, a ```python import asyncio + async def main(): worker1 = AbstractWorker("Worker1") worker2 = AbstractWorker("Worker2") @@ -218,6 +225,7 @@ async def main(): await worker1.a_receive(message1, worker2) await worker1.a_receive(message2, worker2) + loop = asyncio.get_event_loop() loop.run_until_complete(main()) ``` @@ -233,6 +241,7 @@ class MyWorker(AbstractWorker): def reset(self): print(f"{self.name} has been reset.") + worker = MyWorker("Worker1") worker.reset() ``` @@ -253,13 +262,16 @@ The `generate_reply()` method is a placeholder for generating a reply based on r ```python class MyWorker(AbstractWorker): - def generate_reply(self, messages: Optional[List[Dict]] = None, sender=None, **kwargs) -> Union[str, Dict, None]: + def generate_reply( + self, messages: Optional[List[Dict]] = None, sender=None, **kwargs + ) -> Union[str, Dict, None]: if messages: # Generate a reply based on received messages return f"Received {len(messages)} messages from {sender.name}." else: return None + worker1 = MyWorker("Worker1") worker2 = MyWorker("Worker2") @@ -284,6 +296,7 @@ The `a_generate_reply()` method is an asynchronous version of the `generate_repl ```python import asyncio + async def main(): worker1 = AbstractWorker("Worker1") worker2 = AbstractWorker("Worker2") @@ -294,6 +307,7 @@ async def main(): if reply: print(f"{worker2.name} generated a reply: {reply}") + loop = asyncio.get_event_loop() loop.run_until_complete(main()) ``` @@ -312,12 +326,16 @@ Start by creating a custom worker class that inherits from `AbstractWorker`. Def class CustomWorker(AbstractWorker): def run(self, task: str): print(f"{self.name} is performing task: {task}") - - def receive(self, message: Union[Dict, str], sender, request_reply: Optional[bool] = None): + + def receive( + self, message: Union[Dict, str], sender, request_reply: Optional[bool] = None + ): if isinstance(message, str): print(f"{self.name} received a text message from {sender.name}: {message}") elif isinstance(message, dict): - print(f"{self.name} received a dictionary message from {sender.name}: {message}") + print( + f"{self.name} received a dictionary message from {sender.name}: {message}" + ) ``` ### Step 2: Create Custom Worker Instances @@ -355,7 +373,9 @@ Customize the `generate_reply()` method to allow your workers to generate replie ```python class CustomWorker(AbstractWorker): - def generate_reply(self, messages: Optional[List[Dict]] = None, sender=None, **kwargs) -> Union[str, Dict, None]: + def generate_reply( + self, messages: Optional[List[Dict]] = None, sender=None, **kwargs + ) -> Union[str, Dict, None]: if messages: # Generate a reply based on received messages return f"Received {len(messages)} messages from {sender.name}." diff --git a/docs/swarms/workers/index.md b/docs/swarms/workers/index.md index 9cf75e8b..3662fd8a 100644 --- a/docs/swarms/workers/index.md +++ b/docs/swarms/workers/index.md @@ -49,11 +49,11 @@ Makes the Worker class callable. When an instance of the class is called, it wil ### **Example 1**: Basic usage with default parameters: ```python -from swarms.models import OpenAIChat from swarms import Worker +from swarms.models import OpenAIChat llm = OpenAIChat( - #enter your api key + # enter your api key openai_api_key="", temperature=0.5, ) @@ -195,17 +195,16 @@ response = node.run(task) # Print the response print(response) - ``` ### **Example 3**: Usage with human in the loop: ```python -from swarms.models import OpenAIChat from swarms import Worker +from swarms.models import OpenAIChat llm = OpenAIChat( - #enter your api key + # enter your api key openai_api_key="", temperature=0.5, ) @@ -223,7 +222,6 @@ node = Worker( task = "What were the winning boston marathon times for the past 5 years (ending in 2022)? Generate a table of the year, name, country of origin, and times." response = node.run(task) print(response) - ``` ## **Mathematical Description**: diff --git a/playground/agents/multi_modal_auto_agent_example.py b/playground/agents/multi_modal_auto_agent_example.py index d32a6221..4204f48c 100644 --- a/playground/agents/multi_modal_auto_agent_example.py +++ b/playground/agents/multi_modal_auto_agent_example.py @@ -1,6 +1,8 @@ # Description: This is an example of how to use the Agent class to run a multi-modal workflow import os + from dotenv import load_dotenv + from swarms.models.gpt4_vision_api import GPT4VisionAPI from swarms.structs import Agent diff --git a/playground/agents/multion_agent.py b/playground/agents/multion_agent.py index 230f098b..a8f5175d 100644 --- a/playground/agents/multion_agent.py +++ b/playground/agents/multion_agent.py @@ -1,7 +1,8 @@ import multion -from swarms.structs.concurrent_workflow import ConcurrentWorkflow + from swarms.models.base_llm import AbstractLLM from swarms.structs.agent import Agent +from swarms.structs.concurrent_workflow import ConcurrentWorkflow from swarms.structs.task import Task diff --git a/playground/agents/perimeter_defense_agent.py b/playground/agents/perimeter_defense_agent.py index 3f5480c6..d235fa22 100644 --- a/playground/agents/perimeter_defense_agent.py +++ b/playground/agents/perimeter_defense_agent.py @@ -1,8 +1,10 @@ import os + from dotenv import load_dotenv + +import swarms.prompts.security_team as stsp from swarms.models import GPT4VisionAPI from swarms.structs import Agent -import swarms.prompts.security_team as stsp # Load environment variables and initialize the Vision API load_dotenv() diff --git a/playground/agents/simple_agent_example.py b/playground/agents/simple_agent_example.py index 5d9d57ed..b79b8f59 100644 --- a/playground/agents/simple_agent_example.py +++ b/playground/agents/simple_agent_example.py @@ -3,12 +3,11 @@ import os from dotenv import load_dotenv from swarms import ( - OpenAIChat, Conversation, + OpenAIChat, detect_markdown, extract_code_from_markdown, ) - from swarms.tools.code_executor import CodeExecutor conv = Conversation( diff --git a/playground/agents/tool_agent.py b/playground/agents/tool_agent.py index 0a95f42c..a6445b39 100644 --- a/playground/agents/tool_agent.py +++ b/playground/agents/tool_agent.py @@ -1,5 +1,6 @@ # Import necessary libraries from transformers import AutoModelForCausalLM, AutoTokenizer + from swarms import ToolAgent # Load the pre-trained model and tokenizer diff --git a/playground/agents/worker_example.py b/playground/agents/worker_example.py index 9e215e83..a2117e46 100644 --- a/playground/agents/worker_example.py +++ b/playground/agents/worker_example.py @@ -1,8 +1,10 @@ # Importing necessary modules import os + from dotenv import load_dotenv -from swarms.agents.worker_agent import Worker + from swarms import OpenAIChat +from swarms.agents.worker_agent import Worker # Loading environment variables from .env file load_dotenv() diff --git a/playground/demos/accountant_team/account_team2_example.py b/playground/demos/accountant_team/account_team2_example.py index 1b9d3659..6ad030a9 100644 --- a/playground/demos/accountant_team/account_team2_example.py +++ b/playground/demos/accountant_team/account_team2_example.py @@ -1,5 +1,7 @@ import os + from dotenv import load_dotenv + from swarms.models import Anthropic, OpenAIChat from swarms.prompts.accountant_swarm_prompts import ( DECISION_MAKING_PROMPT, diff --git a/playground/demos/ad_gen/ad_gen_example.py b/playground/demos/ad_gen/ad_gen_example.py index b665b63a..978ab502 100644 --- a/playground/demos/ad_gen/ad_gen_example.py +++ b/playground/demos/ad_gen/ad_gen_example.py @@ -1,9 +1,11 @@ -import random import os +import random + from dotenv import load_dotenv + from swarms.models import OpenAIChat -from swarms.structs import Agent from swarms.models.stable_diffusion import StableDiffusion +from swarms.structs import Agent load_dotenv() openai_api_key = os.getenv("OPENAI_API_KEY") diff --git a/playground/demos/assembly/assembly_example.py b/playground/demos/assembly/assembly_example.py index 704c80d4..7ac97ab0 100644 --- a/playground/demos/assembly/assembly_example.py +++ b/playground/demos/assembly/assembly_example.py @@ -1,5 +1,5 @@ -from swarms.structs import Agent from swarms.models.gpt4_vision_api import GPT4VisionAPI +from swarms.structs import Agent llm = GPT4VisionAPI() diff --git a/playground/demos/autotemp/autotemp_example.py b/playground/demos/autotemp/autotemp_example.py index baf8f091..f086f112 100644 --- a/playground/demos/autotemp/autotemp_example.py +++ b/playground/demos/autotemp/autotemp_example.py @@ -1,4 +1,5 @@ import re + from swarms.models.openai_models import OpenAIChat diff --git a/playground/demos/autotemp/blog_gen_example.py b/playground/demos/autotemp/blog_gen_example.py index e11a1521..fe2a2317 100644 --- a/playground/demos/autotemp/blog_gen_example.py +++ b/playground/demos/autotemp/blog_gen_example.py @@ -1,7 +1,9 @@ import os + +from autotemp import AutoTemp from termcolor import colored + from swarms.models import OpenAIChat -from autotemp import AutoTemp from swarms.structs import SequentialWorkflow diff --git a/playground/demos/developer_swarm/main_example.py b/playground/demos/developer_swarm/main_example.py index 18c0a346..0a2e2a95 100644 --- a/playground/demos/developer_swarm/main_example.py +++ b/playground/demos/developer_swarm/main_example.py @@ -6,7 +6,7 @@ This is a simple example of how to use the swarms library to create a swarm of d The swarm is composed of two agents: - Documentation agent: writes documentation for a given code snippet. - Tests agent: writes tests for a given code snippet. - + The swarm is initialized with a language model that is used by the agents to generate text. In this example, we use the OpenAI GPT-3 language model. Agent: @@ -14,6 +14,7 @@ Documentation agent -> Tests agent """ + import os from dotenv import load_dotenv diff --git a/playground/demos/education/education_example.py b/playground/demos/education/education_example.py index 266cede9..31c08f0d 100644 --- a/playground/demos/education/education_example.py +++ b/playground/demos/education/education_example.py @@ -1,8 +1,10 @@ import os + from dotenv import load_dotenv -from swarms.models import OpenAIChat -from swarms import Agent, SequentialWorkflow + import swarms.prompts.education as edu_prompts +from swarms import Agent, SequentialWorkflow +from swarms.models import OpenAIChat # Load environment variables load_dotenv() diff --git a/playground/demos/gemini_benchmarking/gemini_chat_example.py b/playground/demos/gemini_benchmarking/gemini_chat_example.py index 6d9dc7ae..2ea6a900 100644 --- a/playground/demos/gemini_benchmarking/gemini_chat_example.py +++ b/playground/demos/gemini_benchmarking/gemini_chat_example.py @@ -1,5 +1,7 @@ import os + from dotenv import load_dotenv + from swarms.models.gemini import Gemini from swarms.prompts.react import react_prompt diff --git a/playground/demos/gemini_benchmarking/gemini_react_example.py b/playground/demos/gemini_benchmarking/gemini_react_example.py index 022405e9..37765baf 100644 --- a/playground/demos/gemini_benchmarking/gemini_react_example.py +++ b/playground/demos/gemini_benchmarking/gemini_react_example.py @@ -1,5 +1,7 @@ import os + from dotenv import load_dotenv + from swarms.models.gemini import Gemini from swarms.prompts.react import react_prompt diff --git a/playground/demos/grupa/app_example.py b/playground/demos/grupa/app_example.py index 3ab52e22..ff5fc27d 100644 --- a/playground/demos/grupa/app_example.py +++ b/playground/demos/grupa/app_example.py @@ -1,12 +1,12 @@ import os from dotenv import load_dotenv +from termcolor import colored from swarms.models import OpenAIChat from swarms.prompts.code_interpreter import CODE_INTERPRETER +from swarms.prompts.programming import DOCUMENTATION_SOP, TEST_SOP from swarms.structs import Agent -from swarms.prompts.programming import TEST_SOP, DOCUMENTATION_SOP -from termcolor import colored load_dotenv() diff --git a/playground/demos/jarvis_multi_modal_auto_agent/jarvis_example.py b/playground/demos/jarvis_multi_modal_auto_agent/jarvis_example.py index 05cd4fff..cce61fba 100644 --- a/playground/demos/jarvis_multi_modal_auto_agent/jarvis_example.py +++ b/playground/demos/jarvis_multi_modal_auto_agent/jarvis_example.py @@ -1,9 +1,8 @@ -from swarms.structs import Agent from swarms.models.gpt4_vision_api import GPT4VisionAPI from swarms.prompts.multi_modal_autonomous_instruction_prompt import ( MULTI_MODAL_AUTO_AGENT_SYSTEM_PROMPT_1, ) - +from swarms.structs import Agent llm = GPT4VisionAPI() diff --git a/playground/demos/langchain_example/langchain_example.py b/playground/demos/langchain_example/langchain_example.py index 803e7857..0e47684e 100644 --- a/playground/demos/langchain_example/langchain_example.py +++ b/playground/demos/langchain_example/langchain_example.py @@ -1,8 +1,10 @@ import os + from dotenv import load_dotenv -from swarms import Agent from langchain.llms import OpenAIChat +from swarms import Agent + # Loading environment variables from .env file load_dotenv() diff --git a/playground/demos/logistics/logistics_example.py b/playground/demos/logistics/logistics_example.py index 108ec702..48d8b9ce 100644 --- a/playground/demos/logistics/logistics_example.py +++ b/playground/demos/logistics/logistics_example.py @@ -1,16 +1,18 @@ -from swarms.structs import Agent import os + from dotenv import load_dotenv + from swarms.models import GPT4VisionAPI from swarms.prompts.logistics import ( + Efficiency_Agent_Prompt, Health_Security_Agent_Prompt, - Quality_Control_Agent_Prompt, Productivity_Agent_Prompt, + Quality_Control_Agent_Prompt, Safety_Agent_Prompt, Security_Agent_Prompt, Sustainability_Agent_Prompt, - Efficiency_Agent_Prompt, ) +from swarms.structs import Agent # from swarms.utils.banana_wrapper import banana diff --git a/playground/demos/multi_modal_autonomous_agents/multi_modal_auto_agent_example.py b/playground/demos/multi_modal_autonomous_agents/multi_modal_auto_agent_example.py index 74be8d5a..007776ac 100644 --- a/playground/demos/multi_modal_autonomous_agents/multi_modal_auto_agent_example.py +++ b/playground/demos/multi_modal_autonomous_agents/multi_modal_auto_agent_example.py @@ -1,6 +1,5 @@ -from swarms.structs import Agent from swarms.models.gpt4_vision_api import GPT4VisionAPI - +from swarms.structs import Agent llm = GPT4VisionAPI() diff --git a/playground/demos/multimodal_tot/main_example.py b/playground/demos/multimodal_tot/main_example.py index 2d5ed653..2a0494dc 100644 --- a/playground/demos/multimodal_tot/main_example.py +++ b/playground/demos/multimodal_tot/main_example.py @@ -2,8 +2,8 @@ Multi Modal tree of thoughts that leverages the GPT-4 language model and the Stable Diffusion model to generate a multimodal output and evaluate the output based a metric from 0.0 to 1.0 and then run a search algorithm using DFS and BFS and return the best output. - - + + task: Generate an image of a swarm of bees -> Image generator -> GPT4V evaluates the img from 0.0 to 1.0 -> DFS/BFS -> return the best output @@ -16,10 +16,12 @@ task: Generate an image of a swarm of bees -> Image generator -> GPT4V evaluates """ import os + from dotenv import load_dotenv +from termcolor import colored + from swarms.models.gpt4_vision_api import GPT4VisionAPI from swarms.models.stable_diffusion import StableDiffusion -from termcolor import colored # Load the environment variables load_dotenv() diff --git a/playground/demos/nutrition/nutrition_example.py b/playground/demos/nutrition/nutrition_example.py index 428560e3..b4331db6 100644 --- a/playground/demos/nutrition/nutrition_example.py +++ b/playground/demos/nutrition/nutrition_example.py @@ -1,7 +1,9 @@ -import os import base64 +import os + import requests from dotenv import load_dotenv + from swarms.models import OpenAIChat from swarms.structs import Agent diff --git a/playground/demos/personal_assistant/better_communication_example.py b/playground/demos/personal_assistant/better_communication_example.py index c6e79eb7..e0ff75cc 100644 --- a/playground/demos/personal_assistant/better_communication_example.py +++ b/playground/demos/personal_assistant/better_communication_example.py @@ -1,5 +1,5 @@ -import time import os +import time import pygame import speech_recognition as sr diff --git a/playground/demos/personal_stylist/personal_stylist_example.py b/playground/demos/personal_stylist/personal_stylist_example.py index b8641aa3..dde64cb7 100644 --- a/playground/demos/personal_stylist/personal_stylist_example.py +++ b/playground/demos/personal_stylist/personal_stylist_example.py @@ -1,14 +1,16 @@ -from swarms.structs import Agent import os + from dotenv import load_dotenv + from swarms.models import GPT4VisionAPI from swarms.prompts.personal_stylist import ( - HAIRCUT_STYLIST_AGENT_PROMPT, - MAKEUP_STYLIST_AGENT_PROMPT, + ACCESSORIES_STYLIST_AGENT_PROMPT, BEARD_STYLIST_AGENT_PROMPT, CLOTHING_STYLIST_AGENT_PROMPT, - ACCESSORIES_STYLIST_AGENT_PROMPT, + HAIRCUT_STYLIST_AGENT_PROMPT, + MAKEUP_STYLIST_AGENT_PROMPT, ) +from swarms.structs import Agent # Load environment variables load_dotenv() diff --git a/playground/demos/positive_med/positive_med_example.py b/playground/demos/positive_med/positive_med_example.py index b92b9586..09cbb411 100644 --- a/playground/demos/positive_med/positive_med_example.py +++ b/playground/demos/positive_med/positive_med_example.py @@ -20,6 +20,7 @@ Distribution Agent: - Optimize writer prompt to create longer and more enjoyeable blogs - Use Local Models like Storywriter """ + import os from termcolor import colored diff --git a/playground/demos/security_team/IMG_1625.MOV b/playground/demos/security_team/IMG_1625.MOV new file mode 100644 index 0000000000000000000000000000000000000000..9bbb35741ee3cdd917216e5be7a6530511b24088 GIT binary patch literal 6932435 zcmV);K!(2n000zbba`-bbRZxA0001SbRZxA000PgX=G&pX_S6#WMOn6AOk>X93f8U zHwWVTghUONF_{Sl{>yp9*8Okq|Mou?)YK5?dDJ)%_#g1Jk4N|MD#{}|Av}#j2@yKg zh!R;5LnKk-$RrCM)PbmIvdfA{|Hqyypn({AEg9Yj4b7>`j2n|@J zc&|Q9;c>Fe5wf6-B|I_%Y@~FQIK@u!8ey=}Etihk7k3%4$84=k+$BsFFBZ}!MmUH& zf2qFA_>yc7#ri}InsC`eLIwS?HEtX4-~NC8|Kmw_Cz;_6w_RU>580RXAR+zD|BK-} zo01sbGDfYtzQxv32Bbx3$L`KrKPWk0{h?+80|L-E-UYmNm&AIRG7r%2jFU}&V&5Os zx}h!4RiKOxr(*FQ)C;m9(|DK=tO-Xt~??Er&G5n{9eS7;;P>XrN31-oLui!ei#RXqz`T`L(%bAYr@HjD8&}crbJqJN8sbvTO0=UaWepuSvbcL;9^Vv>bP04S7Cb>h=nkTo)P zP*>f~%xn2&E?_fUto6?{xx@A7lP~QiD?u<27J--&Eg?EcnOLP@QY+-!Z4#25iweS+ zIt@33sHH@`j<)yG7ZvN3ujsn~1-VV$n?+Szajj{lNh=BW%=gzHdeQq_y3D&9X@9*u zE#=WiLL@W1NvoJ;8B(4@HZn^CI@S}UsV?+=bwO=f&b<(X2$H1M5@CJNtZ<_7U!KNn zit^8ZAq)(Eo8SNc{B@xRWoRJ*kK^7gnWdi0&I`4p7Z3_R8Mv!vYO1x<((6c=ah`-> zj0i6O+tUT~r8sz%3r)4f`K+?^rz)&AYt7#VIcaYh=^@6R+oZBJXs2aTNk#C9%5akF zG+lqYv)ABu(>`@ne{j*+y?gVoJIf0FEmM%l5oMPfW7XTIb2?t!We3_p+s^v+*Ob3S zq-s;?VG#@|R*WKc3R@N^gX6MLLlVeMiX@rvF1U$8GzqbqkXHHCRPs8KI;({&zHO{`DiTy6pz7E-R8qC#2=`DiQ0tzw9fOOC4pxzDGI~=w?!3o z)I_=EeakI9VatxH7cSZ;NKKbb8TE3uA2ZAI65U)OWKycmr#;rZq^x(CwMlCP!4A@% zA|q2Z$a4`3P~%~_kH^FK>riM@8W!(@j{2CKLP`JvO5X@Tu+WSY6A2apcSqW9iRGCV zwbkcZ%o3VWxey6766^_(6_SZn8O#+q0_|Z~jl#OGoUCc>rIy*wiQrw4CkAY!BBVr| z;USnGOEkDlIZp1F@Qmj_ZLCMkWYAhLjAz?o{K* zGw_g0`6$Tw+rcPxy$%zXf$=I8ImZ)MHvdju)Nm>=8rO2d>@(wR2z;ygjJQ z`X1f);^{^-^-f`Ip6PM^0%B2;&Ydjni)P}6zz|a?BY+_c6m^;u$DuM*fTzy;Firt; zMBpV+U>Gul&O_A1@GLfrJu@#6&8F`d%+d~ba zaoL2Dl=?mA)OUFi<7r`^R4p|V*Npa*)cw9PT)X`wsVW^i3YTRjDHYk} zyuy{ef=h%R>xD>7=_8pC2=c!c0wqUOXd>~(s*zB$ilr4exC+&A%&=TdqSUn6N-l&| zwiLxdv7ktR3-jMH@&>kPaW#vI3)Wrl#Dc<2CDniUc|Am$KNisW3K2Wd`}*up;$;%- zO`m8Iw-1(Yc^Aa4(qe9h#=2>A=ubiFjCbyHs?gK(D9QS?M1-jb$A;6Pqz3PsQ5KgB zn{80(T(u1IT#-1s>h9=-WCqH9Ak$&;NYHDCr+{0>h9@yd4Y^$|xhGhd|!Ua8bdg zC|yQ_G}8_^NK)9B;f*$GxoMVoV~+jgNTMGNGIgU4r{lMM#Ft5(&A~D>+AnDO4xN_F za^i(gK%5P+6$wn-rl3xSsmT+pgB*5}ChKxi{};-PV<$unZl_{CkjUh^W^%Oir26*p zn5$jAfv=xN0RmH0aS|)$px%u_)^H&V6n&Ko31Yxl$Wag}`F*= z51sR_VJPh+n29X8^T3JDlVy($21MN>pj=bNvWp=ZD6GU`kcS?wgcVdt8Bge%RRJwE z_rZOu2@vqtTdg+LFAF$hl{?QtKg#Ta*MuWEA#%p>N$h~iFmY5~% zkA%ZDp9y?CwiXgu5uCTzhMO?itw+OcdBXT(ZX4RLR7;!>iNoS=AZL+qA77)+fX1Fg&3^rOS|FECU2?HWO$8-M?8G%;MG;}&eDh(Vs~T+_{MCF4L<$pz<#uG&+Z6|V zti_yd+^*FMM`9khGVx}cS4zcYC$OjpQjo;YBSK1e_U*BmSioa2wtal8t)$~G zPj9cX#peHozmVBv^vzKtPZrfDRrbEw{)t=*T2;l*_0FvOzuzpKE;%2w>z+lNr8@s! ziiC30(A`E>6D8dc$cGwz@hGg{Pf*Hh$2`PyHP^ch- z!3v$mQ|qbwcJ0)SM@1AOHYkgiJ_m&jU0dut2N_7yC_sU+0tY@EoVs$*4DnUdDW#oq z#kV9>)2IrJPXlFwqLo|>6~hfheT8yGr;R3nWCCwnPNZ08!ZeTt63*JA1Z-jYEP^m~>Vkj1>3BfJ24V z5@~v>F<=Z{OTT2&5MlXGo4(KQv61DK2@#3GQeMidRda*GhQF$kj>Ov`Uxl}R8H8lb zFqjA^&TRM*yw43ZaEt`h3==QHM{|;v7lN|Pq~(T-jQU(XMiqD}@YdN~hW8fb6K}62 zmaWLH8g1plqm%JKhazFJgwR8%0%@(RId zXM%Hy20vHuxlwFT>kI()SbTC6uU#h!??NjnW^{!6wWgljzGLv&^MDD zh@QCsRtg&MjY0bEUk8C=7`-G?vkn
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