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Support** + +--- + +**Introduction**: +In today's fast-paced digital world, responsive and efficient customer support is a linchpin for business success. The introduction of AI-driven swarms in the customer support domain can transform the way businesses interact with and assist their customers. By leveraging the combined power of multiple AI agents working in concert, businesses can achieve unprecedented levels of efficiency, customer satisfaction, and operational cost savings. + +--- + +### **The Benefits of Using Swarms for Customer Support:** + +1. **24/7 Availability**: Swarms never sleep. Customers receive instantaneous support at any hour, ensuring constant satisfaction and loyalty. + +2. **Infinite Scalability**: Whether it's ten inquiries or ten thousand, swarms can handle fluctuating volumes with ease, eliminating the need for vast human teams and minimizing response times. + +3. **Adaptive Intelligence**: Swarms learn collectively, meaning that a solution found for one customer can be instantly applied to benefit all. This leads to constantly improving support experiences, evolving with every interaction. + +--- + +### **Features - Reinventing Customer Support**: + +- **AI Inbox Monitor**: Continuously scans email inboxes, identifying and categorizing support requests for swift responses. + +- **Intelligent Debugging**: Proactively helps customers by diagnosing and troubleshooting underlying issues. + +- **Automated Refunds & Coupons**: Seamless integration with payment systems like Stripe allows for instant issuance of refunds or coupons if a problem remains unresolved. + +- **Full System Integration**: Holistically connects with CRM, email systems, and payment portals, ensuring a cohesive and unified support experience. + +- **Conversational Excellence**: With advanced LLMs (Language Model Transformers), the swarm agents can engage in natural, human-like conversations, enhancing customer comfort and trust. + +- **Rule-based Operation**: By working with rule engines, swarms ensure that all actions adhere to company guidelines, ensuring consistent, error-free support. + +- **Turing Test Ready**: Crafted to meet and exceed the Turing Test standards, ensuring that every customer interaction feels genuine and personal. + +--- + +**Conclusion**: +Swarms are not just another technological advancement; they represent the future of customer support. Their ability to provide round-the-clock, scalable, and continuously improving support can redefine customer experience standards. By adopting swarms, businesses can stay ahead of the curve, ensuring unparalleled customer loyalty and satisfaction. + +**Experience the future of customer support. Dive into the swarm revolution.** + diff --git a/DOCS/README.md b/docs/applications/enterprise.md similarity index 100% rename from DOCS/README.md rename to docs/applications/enterprise.md diff --git a/docs/applications/marketing_agencies.md b/docs/applications/marketing_agencies.md new file mode 100644 index 00000000..d14e359c --- /dev/null +++ b/docs/applications/marketing_agencies.md @@ -0,0 +1,64 @@ +## **Swarms in Marketing Agencies: A New Era of Automated Media Strategy** + +--- + +### **Introduction**: +- Brief background on marketing agencies and their role in driving brand narratives and sales. +- Current challenges and pain points faced in media planning, placements, and budgeting. +- Introduction to the transformative potential of swarms in reshaping the marketing industry. + +--- + +### **1. Fundamental Problem: Media Plan Creation**: + - **Definition**: The challenge of creating an effective media plan that resonates with a target audience and aligns with brand objectives. + + - **Traditional Solutions and Their Shortcomings**: Manual brainstorming sessions, over-reliance on past strategies, and long turnaround times leading to inefficiency. + + - **How Swarms Address This Problem**: + - **Benefit 1**: Automated Media Plan Generation – Swarms ingest branding summaries, objectives, and marketing strategies to generate media plans, eliminating guesswork and human error. + - **Real-world Application of Swarms**: The automation of media plans based on client briefs, including platform selections, audience targeting, and creative versions. + +--- + +### **2. Fundamental Problem: Media Placements**: + - **Definition**: The tedious task of determining where ads will be placed, considering demographics, platform specifics, and more. + + - **Traditional Solutions and Their Shortcomings**: Manual placement leading to possible misalignment with target audiences and brand objectives. + + - **How Swarms Address This Problem**: + - **Benefit 2**: Precision Media Placements – Swarms analyze audience data and demographics to suggest the best placements, optimizing for conversions and brand reach. + - **Real-world Application of Swarms**: Automated selection of ad placements across platforms like Facebook, Google, and DSPs based on media plans. + +--- + +### **3. Fundamental Problem: Budgeting**: + - **Definition**: Efficiently allocating and managing advertising budgets across multiple campaigns, platforms, and timeframes. + + - **Traditional Solutions and Their Shortcomings**: Manual budgeting using tools like Excel, prone to errors, and inefficient shifts in allocations. + + - **How Swarms Address This Problem**: + - **Benefit 3**: Intelligent Media Budgeting – Swarms enable dynamic budget allocation based on performance analytics, maximizing ROI. + - **Real-world Application of Swarms**: Real-time adjustments in budget allocations based on campaign performance, eliminating long waiting periods and manual recalculations. + +--- + +### **Features**: +1. Automated Media Plan Generator: Input your objectives and receive a comprehensive media plan. +2. Precision Media Placement Tool: Ensure your ads appear in the right places to the right people. +3. Dynamic Budget Allocation: Maximize ROI with real-time budget adjustments. +4. Integration with Common Tools: Seamless integration with tools like Excel and APIs for exporting placements. +5. Conversational Platform: A suite of tools built for modern marketing agencies, bringing all tasks under one umbrella. + +--- + +### **Testimonials**: +- "Swarms have completely revolutionized our media planning process. What used to take weeks now takes mere hours." - *Senior Media Strategist, Top-tier Marketing Agency* +- "The precision with which we can place ads now is unprecedented. It's like having a crystal ball for marketing!" - *Campaign Manager, Global Advertising Firm* + +--- + +### **Conclusion**: +- Reiterate the immense potential of swarms in revolutionizing media planning, placements, and budgeting for marketing agencies. +- Call to action: For marketing agencies looking to step into the future and leave manual inefficiencies behind, swarms are the answer. + +--- \ No newline at end of file diff --git a/docs/architecture.md b/docs/architecture.md new file mode 100644 index 00000000..3738b8aa --- /dev/null +++ b/docs/architecture.md @@ -0,0 +1,358 @@ +# Architecture + +## **1. Introduction** + +In today's rapidly evolving digital world, harnessing the collaborative power of multiple computational agents is more crucial than ever. 'Swarms' represents a bold stride in this direction—a scalable and dynamic framework designed to enable swarms of agents to function in harmony and tackle complex tasks. This document serves as a comprehensive guide, elucidating the underlying architecture and strategies pivotal to realizing the Swarms vision. + +--- + +## **2. The Vision** + +At its heart, the Swarms framework seeks to emulate the collaborative efficiency witnessed in natural systems, like ant colonies or bird flocks. These entities, though individually simple, achieve remarkable outcomes through collaboration. Similarly, Swarms will unleash the collective potential of numerous agents, operating cohesively. + +--- + +## **3. Architecture Overview** + +### **3.1 Agent Level** +The base level that serves as the building block for all further complexity. + +#### Mechanics: +* **Model**: At its core, each agent harnesses a powerful model like OpenAI's GPT. +* **Vectorstore**: A memory structure allowing agents to store and retrieve information. +* **Tools**: Utilities and functionalities that aid in the agent's task execution. + +#### Interaction: +Agents interact with the external world through their model and tools. The Vectorstore aids in retaining knowledge and facilitating inter-agent communication. + +### **3.2 Worker Infrastructure Level** +Building on the agent foundation, enhancing capability and readiness for swarm integration. + +#### Mechanics: +* **Human Input Integration**: Enables agents to accept and understand human-provided instructions. +* **Unique Identifiers**: Assigns each agent a unique ID to facilitate tracking and communication. +* **Asynchronous Tools**: Bolsters agents' capability to multitask and interact in real-time. + +#### Interaction: +Each worker is an enhanced agent, capable of operating independently or in sync with its peers, allowing for dynamic, scalable operations. + +### **3.3 Swarm Level** +Multiple Worker Nodes orchestrated into a synchronized, collaborative entity. + +#### Mechanics: +* **Orchestrator**: The maestro, responsible for directing the swarm, task allocation, and communication. +* **Scalable Communication Layer**: Facilitates interactions among nodes and between nodes and the orchestrator. +* **Task Assignment & Completion Protocols**: Structured procedures ensuring tasks are efficiently distributed and concluded. + +#### Interaction: +Nodes collaborate under the orchestrator's guidance, ensuring tasks are partitioned appropriately, executed, and results consolidated. + +### **3.4 Hivemind Level** +Envisioned as a 'Swarm of Swarms'. An upper echelon of collaboration. + +#### Mechanics: +* **Hivemind Orchestrator**: Oversees multiple swarm orchestrators, ensuring harmony on a grand scale. +* **Inter-Swarm Communication Protocols**: Dictates how swarms interact, exchange information, and co-execute tasks. + +#### Interaction: +Multiple swarms, each a formidable force, combine their prowess under the Hivemind. This level tackles monumental tasks by dividing them among swarms. + +--- + +## **4. Building the Framework: A Task Checklist** + +### **4.1 Foundations: Agent Level** +* Define and standardize agent properties. +* Integrate desired model (e.g., OpenAI's GPT) with agent. +* Implement Vectorstore mechanisms: storage, retrieval, and communication protocols. +* Incorporate essential tools and utilities. +* Conduct preliminary testing: Ensure agents can execute basic tasks and utilize the Vectorstore. + +### **4.2 Enhancements: Worker Infrastructure Level** +* Interface agents with human input mechanisms. +* Assign and manage unique identifiers for each worker. +* Integrate asynchronous capabilities: Ensure real-time response and multitasking. +* Test worker nodes for both solitary and collaborative tasks. + +### **4.3 Cohesion: Swarm Level** +* Design and develop the orchestrator: Ensure it can manage multiple worker nodes. +* Establish a scalable and efficient communication layer. +* Implement task distribution and retrieval protocols. +* Test swarms for efficiency, scalability, and robustness. + +### **4.4 Apex Collaboration: Hivemind Level** +* Build the Hivemind Orchestrator: Ensure it can oversee multiple swarms. +* Define inter-swarm communication, prioritization, and task-sharing protocols. +* Develop mechanisms to balance loads and optimize resource utilization across swarms. +* Thoroughly test the Hivemind level for macro-task execution. + +--- + +## **5. Integration and Communication Mechanisms** + +### **5.1 Vectorstore as the Universal Communication Layer** +Serving as the memory and communication backbone, the Vectorstore must: +* Facilitate rapid storage and retrieval of high-dimensional vectors. +* Enable similarity-based lookups: Crucial for recognizing patterns or finding similar outputs. +* Scale seamlessly as agent count grows. + +### **5.2 Orchestrator-Driven Communication** +* Orchestrators, both at the swarm and hivemind level, should employ adaptive algorithms to optimally distribute tasks. +* Ensure real-time monitoring of task execution and worker node health. +* Integrate feedback loops: Allow for dynamic task reassignment in case of node failures or inefficiencies. + +--- + +## **6. Conclusion & Forward Path** + +The Swarms framework, once realized, will usher in a new era of computational efficiency and collaboration. While the roadmap ahead is intricate, with diligent planning, development, and testing, Swarms will redefine the boundaries of collaborative computing. + +-------- + + +# Overview + +### 1. Model + +**Overview:** +The foundational level where a trained model (e.g., OpenAI GPT model) is initialized. It's the base on which further abstraction levels build upon. It provides the core capabilities to perform tasks, answer queries, etc. + +**Diagram:** +``` +[ Model (openai) ] +``` + +### 2. Agent Level + +**Overview:** +At the agent level, the raw model is coupled with tools and a vector store, allowing it to be more than just a model. The agent can now remember, use tools, and become a more versatile entity ready for integration into larger systems. + +**Diagram:** +``` ++-----------+ +| Agent | +| +-------+ | +| | Model | | +| +-------+ | +| +-----------+ | +| | VectorStore | | +| +-----------+ | +| +-------+ | +| | Tools | | +| +-------+ | ++-----------+ +``` + +### 3. Worker Infrastructure Level + +**Overview:** +The worker infrastructure is a step above individual agents. Here, an agent is paired with additional utilities like human input and other tools, making it a more advanced, responsive unit capable of complex tasks. + +**Diagram:** +``` ++----------------+ +| WorkerNode | +| +-----------+ | +| | Agent | | +| | +-------+ | | +| | | Model | | | +| | +-------+ | | +| | +-------+ | | +| | | Tools | | | +| | +-------+ | | +| +-----------+ | +| | +| +-----------+ | +| |Human Input| | +| +-----------+ | +| | +| +-------+ | +| | Tools | | +| +-------+ | ++----------------+ +``` + +### 4. Swarm Level + +**Overview:** +At the swarm level, the orchestrator is central. It's responsible for assigning tasks to worker nodes, monitoring their completion, and handling the communication layer (for example, through a vector store or another universal communication mechanism) between worker nodes. + +**Diagram:** +``` + +------------+ + |Orchestrator| + +------------+ + | + +---------------------------+ + | | + | Swarm-level Communication| + | Layer (e.g. | + | Vector Store) | + +---------------------------+ + / | \ + +---------------+ +---------------+ +---------------+ + |WorkerNode 1 | |WorkerNode 2 | |WorkerNode n | + | | | | | | + +---------------+ +---------------+ +---------------+ + | Task Assigned | Task Completed | Communication | +``` + +### 5. Hivemind Level + +**Overview:** +At the Hivemind level, it's a multi-swarm setup, with an upper-layer orchestrator managing multiple swarm-level orchestrators. The Hivemind orchestrator is responsible for broader tasks like assigning macro-tasks to swarms, handling inter-swarm communications, and ensuring the overall system is functioning smoothly. + +**Diagram:** +``` + +--------+ + |Hivemind| + +--------+ + | + +--------------+ + |Hivemind | + |Orchestrator | + +--------------+ + / | \ + +------------+ +------------+ +------------+ + |Orchestrator| |Orchestrator| |Orchestrator| + +------------+ +------------+ +------------+ + | | | ++--------------+ +--------------+ +--------------+ +| Swarm-level| | Swarm-level| | Swarm-level| +|Communication| |Communication| |Communication| +| Layer | | Layer | | Layer | ++--------------+ +--------------+ +--------------+ + / \ / \ / \ ++-------+ +-------+ +-------+ +-------+ +-------+ +|Worker | |Worker | |Worker | |Worker | |Worker | +| Node | | Node | | Node | | Node | | Node | ++-------+ +-------+ +-------+ +-------+ +-------+ +``` + +This setup allows the Hivemind level to operate at a grander scale, with the capability to manage hundreds or even thousands of worker nodes across multiple swarms efficiently. + + + +------- +# **Swarms Framework Development Strategy Checklist** + +## **Introduction** + +The development of the Swarms framework requires a systematic and granular approach to ensure that each component is robust and that the overall framework is efficient and scalable. This checklist will serve as a guide to building Swarms from the ground up, breaking down tasks into small, manageable pieces. + +--- + +## **1. Agent Level Development** + +### **1.1 Model Integration** +- [ ] Research the most suitable models (e.g., OpenAI's GPT). +- [ ] Design an API for the agent to call the model. +- [ ] Implement error handling when model calls fail. +- [ ] Test the model with sample data for accuracy and speed. + +### **1.2 Vectorstore Implementation** +- [ ] Design the schema for the vector storage system. +- [ ] Implement storage methods to add, delete, and update vectors. +- [ ] Develop retrieval methods with optimization for speed. +- [ ] Create protocols for vector-based communication between agents. +- [ ] Conduct stress tests to ascertain storage and retrieval speed. + +### **1.3 Tools & Utilities Integration** +- [ ] List out essential tools required for agent functionality. +- [ ] Develop or integrate APIs for each tool. +- [ ] Implement error handling and logging for tool interactions. +- [ ] Validate tools integration with unit tests. + +--- + +## **2. Worker Infrastructure Level Development** + +### **2.1 Human Input Integration** +- [ ] Design a UI/UX for human interaction with worker nodes. +- [ ] Create APIs for input collection. +- [ ] Implement input validation and error handling. +- [ ] Test human input methods for clarity and ease of use. + +### **2.2 Unique Identifier System** +- [ ] Research optimal formats for unique ID generation. +- [ ] Develop methods for generating and assigning IDs to agents. +- [ ] Implement a tracking system to manage and monitor agents via IDs. +- [ ] Validate the uniqueness and reliability of the ID system. + +### **2.3 Asynchronous Operation Tools** +- [ ] Incorporate libraries/frameworks to enable asynchrony. +- [ ] Ensure tasks within an agent can run in parallel without conflict. +- [ ] Test asynchronous operations for efficiency improvements. + +--- + +## **3. Swarm Level Development** + +### **3.1 Orchestrator Design & Development** +- [ ] Draft a blueprint of orchestrator functionalities. +- [ ] Implement methods for task distribution among worker nodes. +- [ ] Develop communication protocols for the orchestrator to monitor workers. +- [ ] Create feedback systems to detect and address worker node failures. +- [ ] Test orchestrator with a mock swarm to ensure efficient task allocation. + +### **3.2 Communication Layer Development** +- [ ] Select a suitable communication protocol/framework (e.g., gRPC, WebSockets). +- [ ] Design the architecture for scalable, low-latency communication. +- [ ] Implement methods for sending, receiving, and broadcasting messages. +- [ ] Test communication layer for reliability, speed, and error handling. + +### **3.3 Task Management Protocols** +- [ ] Develop a system to queue, prioritize, and allocate tasks. +- [ ] Implement methods for real-time task status tracking. +- [ ] Create a feedback loop for completed tasks. +- [ ] Test task distribution, execution, and feedback systems for efficiency. + +--- + +## **4. Hivemind Level Development** + +### **4.1 Hivemind Orchestrator Development** +- [ ] Extend swarm orchestrator functionalities to manage multiple swarms. +- [ ] Create inter-swarm communication protocols. +- [ ] Implement load balancing mechanisms to distribute tasks across swarms. +- [ ] Validate hivemind orchestrator functionalities with multi-swarm setups. + +### **4.2 Inter-Swarm Communication Protocols** +- [ ] Design methods for swarms to exchange data. +- [ ] Implement data reconciliation methods for swarms working on shared tasks. +- [ ] Test inter-swarm communication for efficiency and data integrity. + +--- + +## **5. Scalability & Performance Testing** + +- [ ] Simulate heavy loads to test the limits of the framework. +- [ ] Identify and address bottlenecks in both communication and computation. +- [ ] Conduct speed tests under different conditions. +- [ ] Test the system's responsiveness under various levels of stress. + +--- + +## **6. Documentation & User Guide** + +- [ ] Develop detailed documentation covering architecture, setup, and usage. +- [ ] Create user guides with step-by-step instructions. +- [ ] Incorporate visual aids, diagrams, and flowcharts for clarity. +- [ ] Update documentation regularly with new features and improvements. + +--- + +## **7. Continuous Integration & Deployment** + +- [ ] Setup CI/CD pipelines for automated testing and deployment. +- [ ] Ensure automatic rollback in case of deployment failures. +- [ ] Integrate code quality and security checks in the pipeline. +- [ ] Document deployment strategies and best practices. + +--- + +## **Conclusion** + +The Swarms framework represents a monumental leap in agent-based computation. This checklist provides a thorough roadmap for the framework's development, ensuring that every facet is addressed in depth. Through diligent adherence to this guide, the Swarms vision can be realized as a powerful, scalable, and robust system ready to tackle the challenges of tomorrow. + +(Note: This document, given the word limit, provides a high-level overview. 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+{% endblock %} \ No newline at end of file diff --git a/DOCS/Corp/PITCH.md b/docs/purpose.md similarity index 100% rename from DOCS/Corp/PITCH.md rename to docs/purpose.md diff --git a/docs/research.md b/docs/research.md new file mode 100644 index 00000000..bdfac22c --- /dev/null +++ b/docs/research.md @@ -0,0 +1,82 @@ +# Research Lists +A compilation of projects, papers, blogs in autonomous agents. + +## Table of Contents + +- [Introduction](#introduction) +- [Projects](#projects) +- [Articles](#articles) +- [Talks](#talks) + + +## Projects + +### Developer tools +- [2023/8/10] [ModelScope-Agent](https://github.com/modelscope/modelscope-agent) - An Agent Framework Connecting Models in ModelScope with the World +- [2023/05/25] [Gorilla](https://github.com/ShishirPatil/gorilla) - An API store for LLMs +- [2023/03/31] [BMTools](https://github.com/OpenBMB/BMTools) - Tool Learning for Big Models, Open-Source Solutions of ChatGPT-Plugins +- [2023/03/09] [LMQL](https://github.com/eth-sri/lmql) - A query language for programming (large) language models. +- [2022/10/25] [Langchain](https://github.com/hwchase17/langchain) - ⚡ Building applications with LLMs through composability ⚡ + +### Applications +- [2023/07/08] [ShortGPT](https://github.com/RayVentura/ShortGPT) - 🚀🎬 ShortGPT - An experimental AI framework for automated short/video content creation. Enables creators to rapidly produce, manage, and deliver content using AI and automation. +- [2023/07/05] [gpt-researcher](https://github.com/assafelovic/gpt-researcher) - GPT based autonomous agent that does online comprehensive research on any given topic +- [2023/07/04] [DemoGPT](https://github.com/melih-unsal/DemoGPT) - 🧩DemoGPT enables you to create quick demos by just using prompts. [[demo]](demogpt.io) +- [2023/06/30] [MetaGPT](https://github.com/geekan/MetaGPT) - 🌟 The Multi-Agent Framework: Given one line Requirement, return PRD, Design, Tasks, Repo +- [2023/06/11] [gpt-engineer](https://github.com/AntonOsika/gpt-engineer) - Specify what you want it to build, the AI asks for clarification, and then builds it. +- [2023/05/16] [SuperAGI](https://github.com/TransformerOptimus/SuperAGI) - <⚡️> SuperAGI - A dev-first open source autonomous AI agent framework. Enabling developers to build, manage & run useful autonomous agents quickly and reliably. +- [2023/05/13] [Developer](https://github.com/smol-ai/developer) - Human-centric & Coherent Whole Program Synthesis aka your own personal junior developer +- [2023/04/07] [AgentGPT](https://github.com/reworkd/AgentGPT) - 🤖 Assemble, configure, and deploy autonomous AI Agents in your browser. [[demo]](agentgpt.reworkd.ai) +- [2023/04/03] [BabyAGI](https://github.com/yoheinakajima/babyagi) - an example of an AI-powered task management system +- [2023/03/30] [AutoGPT](https://github.com/Significant-Gravitas/Auto-GPT) - An experimental open-source attempt to make GPT-4 fully autonomous. + +### Benchmarks +- [2023/08/07] [AgentBench](https://github.com/THUDM/AgentBench) - A Comprehensive Benchmark to Evaluate LLMs as Agents. [paper](https://arxiv.org/abs/2308.03688) +- [2023/06/18] [Auto-GPT-Benchmarks](https://github.com/Significant-Gravitas/Auto-GPT-Benchmarks) - A repo built for the purpose of benchmarking the performance of agents, regardless of how they are set up and how they work. +- [2023/05/28] [ToolBench](https://github.com/OpenBMB/ToolBench) - An open platform for training, serving, and evaluating large language model for tool learning. + +## Articles +### Research Papers +- [2023/08/11] [BOLAA: Benchmarking and Orchestrating LLM-Augmented Autonomous Agents](https://arxiv.org/pdf/2308.05960v1.pdf), Zhiwei Liu, et al. +- [2023/07/31] [ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs](https://arxiv.org/abs/2307.16789), Yujia Qin, et al. +- [2023/07/16] [Communicative Agents for Software Development](https://arxiv.org/abs/2307.07924), Chen Qian, et al. +- [2023/06/09] [Mind2Web: Towards a Generalist Agent for the Web](https://arxiv.org/pdf/2306.06070.pdf), Xiang Deng, et al. [[code]](https://github.com/OSU-NLP-Group/Mind2Web) [[demo]](https://osu-nlp-group.github.io/Mind2Web/) +- [2023/06/05] [Orca: Progressive Learning from Complex Explanation Traces of GPT-4](https://arxiv.org/pdf/2306.02707.pdf), Subhabrata Mukherjee et al. +- [2023/05/25] [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://arxiv.org/pdf/2305.16291.pdf), Guanzhi Wang, et al. [[code]](https://github.com/MineDojo/Voyager) [[website]](https://voyager.minedojo.org/) +- [2023/05/23] [ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models](https://arxiv.org/pdf/2305.18323.pdf), Binfeng Xu, et al. [[code]](https://github.com/billxbf/ReWOO) +- [2023/05/17] [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://arxiv.org/abs/2305.10601), Shunyu Yao, et al.[[code]](https://github.com/kyegomez/tree-of-thoughts) [[code-orig]](https://github.com/ysymyth/tree-of-thought-llm) +- [2023/05/12] [MEGABYTE: Predicting Million-byte Sequences with Multiscale Transformers](https://arxiv.org/abs/2305.07185), Lili Yu, et al. +- [2023/05/19] [FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance](https://arxiv.org/abs/2305.05176), Lingjiao Chen, et al. +- [2023/05/06] [Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models](https://arxiv.org/abs/2305.04091), Lei Wang, et al. +- [2023/05/01] [Learning to Reason and Memorize with Self-Notes](https://arxiv.org/abs/2305.00833), Jack Lanchantin, et al. +- [2023/04/24] [WizardLM: Empowering Large Language Models to Follow Complex Instructions](https://arxiv.org/abs/2304.12244), Can Xu, et al. +- [2023/04/22] [LLM+P: Empowering Large Language Models with Optimal Planning Proficiency](https://arxiv.org/abs/2304.11477), Bo Liu, et al. +- [2023/04/07] [Generative Agents: Interactive Simulacra of Human Behavior](https://arxiv.org/abs/2304.03442), Joon Sung Park, et al. [[code]](https://github.com/mkturkcan/generative-agents) +- [2023/03/30] [Self-Refine: Iterative Refinement with Self-Feedback](https://arxiv.org/abs/2303.17651), Aman Madaan, et al.[[code]](https://github.com/madaan/self-refine) +- [2023/03/30] [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace](https://arxiv.org/pdf/2303.17580.pdf), Yongliang Shen, et al. [[code]](https://github.com/microsoft/JARVIS) [[demo]](https://huggingface.co/spaces/microsoft/HuggingGPT) +- [2023/03/20] [Reflexion: Language Agents with Verbal Reinforcement Learning](https://arxiv.org/pdf/2303.11366.pdf), Noah Shinn, et al. [[code]](https://github.com/noahshinn024/reflexion) +- [2023/03/04] [Towards A Unified Agent with Foundation Models](https://openreview.net/pdf?id=JK_B1tB6p-), Norman Di Palo et al. +- [2023/02/23] [Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection](https://arxiv.org/abs/2302.12173), Sahar Abdelnab, et al. +- [2023/02/09] [Toolformer: Language Models Can Teach Themselves to Use Tools](https://arxiv.org/pdf/2302.04761.pdf), Timo Schick, et al. [[code]](https://github.com/lucidrains/toolformer-pytorch) +- [2022/12/12] [LMQL: Prompting Is Programming: A Query Language for Large Language Models](https://arxiv.org/abs/2212.06094), Luca Beurer-Kellner, et al. +- [2022/10/06] [ReAct: Synergizing Reasoning and Acting in Language Models](https://arxiv.org/pdf/2210.03629.pdf), Shunyu Yao, et al. [[code]](https://github.com/ysymyth/ReAct) +- [2022/07/20] [Inner Monologue: Embodied Reasoning through Planning with Language Models](https://arxiv.org/pdf/2207.05608.pdf), Wenlong Huang, et al. [[demo]](https://innermonologue.github.io/) +- [2022/04/04] [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](), Michael Ahn, e al. [[demo]](https://say-can.github.io/) +- [2021/12/17] [WebGPT: Browser-assisted question-answering with human feedback](https://arxiv.org/pdf/2112.09332.pdf), Reiichiro Nakano, et al. +- [2021/06/17] [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685), Edward J. Hu, et al. + + +### Blog Articles + +- [2023/08/14] [A Roadmap of AI Agents(Chinese)](https://zhuanlan.zhihu.com/p/649916692) By Haojie Pan +- [2023/06/23] [LLM Powered Autonomous Agents](https://lilianweng.github.io/posts/2023-06-23-agent/) By Lilian Weng +- [2023/06/11] [A CRITICAL LOOK AT AI-GENERATED SOFTWARE](https://spectrum.ieee.org/ai-software) By JAIDEEP VAIDYAHAFIZ ASIF +- [2023/04/29] [AUTO-GPT: UNLEASHING THE POWER OF AUTONOMOUS AI AGENTS](https://www.leewayhertz.com/autogpt/) By Akash Takyar +- [2023/04/20] [Conscious Machines: Experiments, Theory, and Implementations(Chinese)](https://pattern.swarma.org/article/230) By Jiang Zhang +- [2023/04/18] [Autonomous Agents & Agent Simulations](https://blog.langchain.dev/agents-round/) By Langchain +- [2023/04/16] [4 Autonomous AI Agents you need to know](https://towardsdatascience.com/4-autonomous-ai-agents-you-need-to-know-d612a643fa92) By Sophia Yang +- [2023/03/31] [ChatGPT that learns to use tools(Chinese)](https://zhuanlan.zhihu.com/p/618448188) By Haojie Pan + +### Talks +- [2023/06/05] [Two Paths to Intelligence](https://www.youtube.com/watch?v=rGgGOccMEiY&t=1497s) by Geoffrey Hinton +- [2023/05/24] [State of GPT](https://www.youtube.com/watch?v=bZQun8Y4L2A) by Andrej Karpathy | OpenAI diff --git a/docs/roadmap.md b/docs/roadmap.md new file mode 100644 index 00000000..46872c45 --- /dev/null +++ b/docs/roadmap.md @@ -0,0 +1,13 @@ +## The Plan + +### Phase 1: Building the Foundation +In the first phase, our focus is on building the basic infrastructure of Swarms. This includes developing key components like the Swarms class, integrating essential tools, and establishing task completion and evaluation logic. We'll also start developing our testing and evaluation framework during this phase. If you're interested in foundational work and have a knack for building robust, scalable systems, this phase is for you. + +### Phase 2: Optimizing the System +In the second phase, we'll focus on optimizng Swarms by integrating more advanced features, improving the system's efficiency, and refining our testing and evaluation framework. This phase involves more complex tasks, so if you enjoy tackling challenging problems and contributing to the development of innovative features, this is the phase for you. + +### Phase 3: Towards Super-Intelligence +The third phase of our bounty program is the most exciting - this is where we aim to achieve super-intelligence. In this phase, we'll be working on improving the swarm's capabilities, expanding its skills, and fine-tuning the system based on real-world testing and feedback. If you're excited about the future of AI and want to contribute to a project that could potentially transform the digital world, this is the phase for you. + +Remember, our roadmap is a guide, and we encourage you to bring your own ideas and creativity to the table. We believe that every contribution, no matter how small, can make a difference. So join us on this exciting journey and help us create the future of Swarms. + diff --git a/docs/swarms/index.md b/docs/swarms/index.md new file mode 100644 index 00000000..fe770281 --- /dev/null +++ b/docs/swarms/index.md @@ -0,0 +1,191 @@ +The Swarms framework provides developers with the ability to create AI systems that operate across two dimensions: **predictability** and **creativity**. + +For **predictability**, Swarms enforces structures like sequential pipelines, DAG-based workflows, and long-term memory. To facilitate creativity, Swarms safely prompts LLMs with tools and short-term memory connecting them to external APIs and data stores. The framework allows developers to transition between those two dimensions effortlessly based on their use case. + +Swarms not only helps developers harness the potential of LLMs but also enforces trust boundaries, schema validation, and tool activity-level permissions. By doing so, Swarms maximizes LLMs’ reasoning while adhering to strict policies regarding their capabilities. + +Swarms’s design philosophy is based on the following tenets: + +1. **Modularity and composability**: All framework primitives are useful and usable on their own in addition to being easy to plug into each other. +2. **Technology-agnostic**: Swarms is designed to work with any capable LLM, data store, and backend through the abstraction of drivers. +3. **Keep data off prompt by default**: When working with data through loaders and tools, Swarms aims to keep it off prompt by default, making it easy to work with big data securely and with low latency. +4. **Minimal prompt engineering**: It’s much easier to reason about code written in Python, not natural languages. Swarms aims to default to Python in most cases unless absolutely necessary. + + +## Installation + +There are 2 methods, one is through `git clone` and the other is by `pip install swarms`. Check out the [DOCUMENTATION](DOCS/DOCUMENTATION.md) for more information on the classes. + +* Pip install `pip3 install swarms` + +* Create new python file and unleash superintelligence + +```python + +from swarms import Worker + + +node = Worker( + openai_api_key="", + ai_name="Optimus Prime", +) + +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) +``` + +--- + +## Usage + +```python + +from swarms import HuggingFaceLLM + +hugging_face_model = HuggingFaceLLM(model_id="Voicelab/trurl-2-13b") +generated_text = hugging_face_model.generate("In a world where AI") + +``` +--- + +# Documentation +For documentation, go here, [the docs folder in the root diectory](https://swarms.apac.ai) + +**NOTE: We need help building the documentation** + +----- + +# Docker Setup +The docker file is located in the docker folder in the `infra` folder, [click here and navigate here in your environment](/infra/Docker) + +* Build the Docker image + +* You can build the Docker image using the provided Dockerfile. Navigate to the infra/Docker directory where the Dockerfiles are located. + +* For the CPU version, use: + +```bash +docker build -t swarms-api:latest -f Dockerfile.cpu . +``` +For the GPU version, use: + +```bash +docker build -t swarms-api:gpu -f Dockerfile.gpu . +``` +### Run the Docker container + +After building the Docker image, you can run the Swarms API in a Docker container. Replace your_redis_host and your_redis_port with your actual Redis host and port. + +For the CPU version: + +```bash +docker run -p 8000:8000 -e REDIS_HOST=your_redis_host -e REDIS_PORT=your_redis_port swarms-api:latest +``` + +## For the GPU version: +```bash +docker run --gpus all -p 8000:8000 -e REDIS_HOST=your_redis_host -e REDIS_PORT=your_redis_port swarms-api:gpu +``` + +## Access the Swarms API + +* The Swarms API will be accessible at http://localhost:8000. You can use tools like curl or Postman to send requests to the API. + +Here's an example curl command to send a POST request to the /chat endpoint: + +```bash +curl -X POST -H "Content-Type: application/json" -d '{"api_key": "your_openai_api_key", "objective": "your_objective"}' http://localhost:8000/chat +``` +Replace your_openai_api_key and your_objective with your actual OpenAI API key and objective. + +---- + + +# ✨ Features +* Easy to use Base LLMs, `OpenAI` `Palm` `Anthropic` `HuggingFace` +* Enterprise Grade, Production Ready with robust Error Handling +* Multi-Modality Native with Multi-Modal LLMs as tools +* Infinite Memory Processing: Store infinite sequences of infinite Multi-Modal data, text, images, videos, audio +* Usability: Extreme emphasis on useability, code is at it's theortical minimum simplicity factor to use +* Reliability: Outputs that accomplish tasks and activities you wish to execute. +* Fluidity: A seamless all-around experience to build production grade workflows +* Speed: Lower the time to automate tasks by 90%. +* Simplicity: Swarms is extremely simple to use, if not thee simplest agent framework of all time +* Powerful: Swarms is capable of building entire software apps, to large scale data analysis, and handling chaotic situations + + +--- +# Roadmap + +Please checkout our [Roadmap](DOCS/ROADMAP.md) and consider contributing to make the dream of Swarms real to advance Humanity. + +## Optimization Priorities + +1. **Reliability**: Increase the reliability of the swarm - obtaining the desired output with a basic and un-detailed input. + +2. **Speed**: Reduce the time it takes for the swarm to accomplish tasks by improving the communication layer, critiquing, and self-alignment with meta prompting. + +3. **Scalability**: Ensure that the system is asynchronous, concurrent, and self-healing to support scalability. + +Our goal is to continuously improve Swarms by following this roadmap, while also being adaptable to new needs and opportunities as they arise. + +--- + +# Bounty Program + +Our bounty program is an exciting opportunity for contributors to help us build the future of Swarms. By participating, you can earn rewards while contributing to a project that aims to revolutionize digital activity. + +Here's how it works: + +1. **Check out our Roadmap**: We've shared our roadmap detailing our short and long-term goals. These are the areas where we're seeking contributions. + +2. **Pick a Task**: Choose a task from the roadmap that aligns with your skills and interests. If you're unsure, you can reach out to our team for guidance. + +3. **Get to Work**: Once you've chosen a task, start working on it. Remember, quality is key. We're looking for contributions that truly make a difference. + +4. **Submit your Contribution**: Once your work is complete, submit it for review. We'll evaluate your contribution based on its quality, relevance, and the value it brings to Swarms. + +5. **Earn Rewards**: If your contribution is approved, you'll earn a bounty. The amount of the bounty depends on the complexity of the task, the quality of your work, and the value it brings to Swarms. + +--- + +## The Plan + +### Phase 1: Building the Foundation +In the first phase, our focus is on building the basic infrastructure of Swarms. This includes developing key components like the Swarms class, integrating essential tools, and establishing task completion and evaluation logic. We'll also start developing our testing and evaluation framework during this phase. If you're interested in foundational work and have a knack for building robust, scalable systems, this phase is for you. + +### Phase 2: Optimizing the System +In the second phase, we'll focus on optimizng Swarms by integrating more advanced features, improving the system's efficiency, and refining our testing and evaluation framework. This phase involves more complex tasks, so if you enjoy tackling challenging problems and contributing to the development of innovative features, this is the phase for you. + +### Phase 3: Towards Super-Intelligence +The third phase of our bounty program is the most exciting - this is where we aim to achieve super-intelligence. In this phase, we'll be working on improving the swarm's capabilities, expanding its skills, and fine-tuning the system based on real-world testing and feedback. If you're excited about the future of AI and want to contribute to a project that could potentially transform the digital world, this is the phase for you. + +Remember, our roadmap is a guide, and we encourage you to bring your own ideas and creativity to the table. We believe that every contribution, no matter how small, can make a difference. So join us on this exciting journey and help us create the future of Swarms. + +--- + +# EcoSystem + +* [The-Compiler, compile natural language into serene, reliable, and secure programs](https://github.com/kyegomez/the-compiler) + +*[The Replicator, an autonomous swarm that conducts Multi-Modal AI research by creating new underlying mathematical operations and models](https://github.com/kyegomez/The-Replicator) + +* Make a swarm that checks arxviv for papers -> checks if there is a github link -> then implements them and checks them + +* [SwarmLogic, where a swarm is your API, database, and backend!](https://github.com/kyegomez/SwarmLogic) + +--- + +# Demos + +![Swarms Demo](images/Screenshot_48.png) + +## Swarm Video Demo {Click for more} + +[![Watch the swarm video](https://img.youtube.com/vi/Br62cDMYXgc/maxresdefault.jpg)](https://youtu.be/Br62cDMYXgc) + +--- + +# Contact +For enterprise and production ready deployments, allow us to discover more about you and your story, [book a call with us here](https://www.apac.ai/Setup-Call) \ No newline at end of file diff --git a/docs/swarms/models/anthropic.md b/docs/swarms/models/anthropic.md new file mode 100644 index 00000000..4d5f1fcd --- /dev/null +++ b/docs/swarms/models/anthropic.md @@ -0,0 +1,110 @@ +# **Documentation for the `Anthropic` Class** + +## **Overview and Introduction** + +The `Anthropic` class provides an interface to interact with the Anthropic large language models. This class encapsulates the necessary functionality to request completions from the Anthropic API based on a provided prompt and other configurable parameters. + +### **Key Concepts and Terminology** + +- **Anthropic**: A large language model, akin to GPT-3 and its successors. +- **Prompt**: A piece of text that serves as the starting point for model completions. +- **Stop Sequences**: Specific tokens or sequences to indicate when the model should stop generating. +- **Tokens**: Discrete pieces of information in a text. For example, in English, a token can be as short as one character or as long as one word. + +## **Class Definition** + +### `Anthropic` +```python +class Anthropic: + """Anthropic large language models.""" +``` + +### Parameters: + +- `model (str)`: The name of the model to use for completions. Default is "claude-2". + +- `max_tokens_to_sample (int)`: Maximum number of tokens to generate in the output. Default is 256. + +- `temperature (float, optional)`: Sampling temperature. A higher value will make the output more random, while a lower value will make it more deterministic. + +- `top_k (int, optional)`: Sample from the top-k most probable next tokens. Setting this parameter can reduce randomness in the output. + +- `top_p (float, optional)`: Sample from the smallest set of tokens such that their cumulative probability exceeds the specified value. Used in nucleus sampling to provide a balance between randomness and determinism. + +- `streaming (bool)`: Whether to stream the output or not. Default is False. + +- `default_request_timeout (int, optional)`: Default timeout in seconds for API requests. Default is 600. + +### **Methods and their Functionality** + +#### `_default_params(self) -> dict` + +- Provides the default parameters for calling the Anthropic API. + +- **Returns**: A dictionary containing the default parameters. + +#### `generate(self, prompt: str, stop: list[str] = None) -> str` + +- Calls out to Anthropic's completion endpoint to generate text based on the given prompt. + +- **Parameters**: + - `prompt (str)`: The input text to provide context for the generated text. + + - `stop (list[str], optional)`: Sequences to indicate when the model should stop generating. + +- **Returns**: A string containing the model's generated completion based on the prompt. + +#### `__call__(self, prompt: str, stop: list[str] = None) -> str` + +- An alternative to the `generate` method that allows calling the class instance directly. + +- **Parameters**: + - `prompt (str)`: The input text to provide context for the generated text. + + - `stop (list[str], optional)`: Sequences to indicate when the model should stop generating. + +- **Returns**: A string containing the model's generated completion based on the prompt. + +## **Usage Examples** + +```python +# Import necessary modules and classes +from swarms.models import Anthropic +import torch + +# Initialize an instance of the Anthropic class +anthropic_instance = Anthropic() + +# Using the generate method +completion_1 = anthropic_instance.generate("What is the capital of France?") +print(completion_1) + +# Using the __call__ method +completion_2 = anthropic_instance("How far is the moon from the earth?", stop=["miles", "km"]) +print(completion_2) +``` + +## **Mathematical Formula** + +The underlying operations of the `Anthropic` class involve probabilistic sampling based on token logits from the Anthropic model. Mathematically, the process of generating a token \( t \) from the given logits \( l \) can be described by the softmax function: + +\[ P(t) = \frac{e^{l_t}}{\sum_{i} e^{l_i}} \] + +Where: +- \( P(t) \) is the probability of token \( t \). +- \( l_t \) is the logit corresponding to token \( t \). +- The summation runs over all possible tokens. + +The temperature, top-k, and top-p parameters are further used to modulate the probabilities. + +## **Additional Information and Tips** + +- Ensure you have a valid `ANTHROPIC_API_KEY` set as an environment variable or passed during class instantiation. + +- Always handle exceptions that may arise from API timeouts or invalid prompts. + +## **References and Resources** + +- [Anthropic's official documentation](https://www.anthropic.com/docs) + +- [Token-based sampling in Language Models](https://arxiv.org/abs/1904.09751) for a deeper understanding of token sampling. \ No newline at end of file diff --git a/docs/swarms/models/hf.md b/docs/swarms/models/hf.md new file mode 100644 index 00000000..45d88af8 --- /dev/null +++ b/docs/swarms/models/hf.md @@ -0,0 +1,91 @@ +# HuggingFaceLLM + +## Overview & Introduction + +The `HuggingFaceLLM` class in the Zeta library provides a simple and easy-to-use interface to harness the power of Hugging Face's transformer-based language models, specifically for causal language modeling. This enables developers to generate coherent and contextually relevant sentences or paragraphs given a prompt, without delving deep into the intricate details of the underlying model or the tokenization process. + +Causal Language Modeling (CLM) is a task where given a series of tokens (or words), the model predicts the next token in the sequence. This functionality is central to many natural language processing tasks, including chatbots, story generation, and code autocompletion. + +--- + +## Class Definition + +```python +class HuggingFaceLLM: +``` + +### Parameters: + +- `model_id (str)`: Identifier for the pre-trained model on the Hugging Face model hub. Examples include "gpt2-medium", "openai-gpt", etc. + +- `device (str, optional)`: The device on which to load and run the model. Defaults to 'cuda' if GPU is available, else 'cpu'. + +- `max_length (int, optional)`: Maximum length of the generated sequence. Defaults to 20. + +- `quantization_config (dict, optional)`: Configuration dictionary for model quantization (if applicable). Default is `None`. + +--- + +## Functionality & Usage + +### Initialization: + +```python +llm = HuggingFaceLLM(model_id="gpt2-medium") +``` + +Upon initialization, the specified pre-trained model and tokenizer are loaded from Hugging Face's model hub. The model is then moved to the designated device. If there's an issue loading either the model or the tokenizer, an error will be logged. + +### Generation: + +The main functionality of this class is text generation. The class provides two methods for this: `__call__` and `generate`. Both methods take in a prompt text and an optional `max_length` parameter and return the generated text. + +Usage: +```python +from swarms import HuggingFaceLLM + +# Initialize +llm = HuggingFaceLLM(model_id="gpt2-medium") + +# Generate text using __call__ method +result = llm("Once upon a time,") +print(result) + +# Alternatively, using the generate method +result = llm.generate("The future of AI is") +print(result) +``` + +--- + +## Mathematical Explanation: + +Given a sequence of tokens \( x_1, x_2, ..., x_n \), a causal language model aims to maximize the likelihood of the next token \( x_{n+1} \) in the sequence. Formally, it tries to optimize: + +\[ P(x_{n+1} | x_1, x_2, ..., x_n) \] + +Where \( P \) is the probability distribution over all possible tokens in the vocabulary. + +The model takes the tokenized input sequence, feeds it through several transformer blocks, and finally through a linear layer to produce logits for each token in the vocabulary. The token with the highest logit value is typically chosen as the next token in the sequence. + +--- + +## Additional Information & Tips: + +- Ensure you have an active internet connection when initializing the class for the first time, as the models and tokenizers are fetched from Hugging Face's servers. + +- Although the default `max_length` is set to 20, it's advisable to adjust this parameter based on the context of the problem. + +- Keep an eye on GPU memory when using large models or generating long sequences. + +--- + +## References & Resources: + +- Hugging Face Model Hub: [https://huggingface.co/models](https://huggingface.co/models) + +- Introduction to Transformers: [https://huggingface.co/transformers/introduction.html](https://huggingface.co/transformers/introduction.html) + +- Causal Language Modeling: Vaswani, A., et al. (2017). Attention is All You Need. [arXiv:1706.03762](https://arxiv.org/abs/1706.03762) + +Note: This documentation template provides a comprehensive overview of the `HuggingFaceLLM` class. Developers can follow similar structures when documenting other classes or functionalities. \ No newline at end of file diff --git a/docs/swarms/models/index.md b/docs/swarms/models/index.md new file mode 100644 index 00000000..93883779 --- /dev/null +++ b/docs/swarms/models/index.md @@ -0,0 +1,174 @@ +## LLMs in Swarms Documentation + +Welcome to the documentation for the llm section of the swarms package, designed to facilitate seamless integration with various AI language models and APIs. This package empowers developers, end-users, and system administrators to interact with AI models from different providers, such as OpenAI, Hugging Face, Google PaLM, and Anthropic. + +### Table of Contents +1. [OpenAI](#openai) +2. [HuggingFace](#huggingface) +3. [Google PaLM](#google-palm) +4. [Anthropic](#anthropic) + +### 1. OpenAI (swarms.agents.models.OpenAI) + +The OpenAI class provides an interface to interact with OpenAI's language models. It allows both synchronous and asynchronous interactions. + +**Constructor:** +```python +OpenAI(api_key: str, system: str = None, console: bool = True, model: str = None, params: dict = None, save_messages: bool = True) +``` + +**Attributes:** +- `api_key` (str): Your OpenAI API key. + +- `system` (str, optional): A system message to be used in conversations. + +- `console` (bool, default=True): Display console logs. + +- `model` (str, optional): Name of the language model to use. + +- `params` (dict, optional): Additional parameters for model interactions. + +- `save_messages` (bool, default=True): Save conversation messages. + +**Methods:** + +- `generate(message: str, **kwargs) -> str`: Generate a response using the OpenAI model. + +- `generate_async(message: str, **kwargs) -> str`: Generate a response asynchronously. + +- `ask_multiple(ids: List[str], question_template: str) -> List[str]`: Query multiple IDs simultaneously. + +- `stream_multiple(ids: List[str], question_template: str) -> List[str]`: Stream multiple responses. + +**Usage Example:** +```python +from swarms import OpenAI +import asyncio + +chat = OpenAI(api_key="YOUR_OPENAI_API_KEY") + +response = chat.generate("Hello, how can I assist you?") +print(response) + +ids = ["id1", "id2", "id3"] +async_responses = asyncio.run(chat.ask_multiple(ids, "How is {id}?")) +print(async_responses) +``` + +### 2. HuggingFace (swarms.agents.models.HuggingFaceLLM) + +The HuggingFaceLLM class allows interaction with language models from Hugging Face. + +**Constructor:** +```python +HuggingFaceLLM(model_id: str, device: str = None, max_length: int = 20, quantize: bool = False, quantization_config: dict = None) +``` + +**Attributes:** + +- `model_id` (str): ID or name of the Hugging Face model. + +- `device` (str, optional): Device to run the model on (e.g., 'cuda', 'cpu'). + +- `max_length` (int, default=20): Maximum length of generated text. + +- `quantize` (bool, default=False): Apply model quantization. + +- `quantization_config` (dict, optional): Configuration for quantization. + +**Methods:** + +- `generate(prompt_text: str, max_length: int = None) -> str`: Generate text based on a prompt. + +**Usage Example:** +```python +from swarms import HuggingFaceLLM + +model_id = "gpt2" +hugging_face_model = HuggingFaceLLM(model_id=model_id) + +prompt = "Once upon a time" +generated_text = hugging_face_model.generate(prompt) +print(generated_text) +``` + +### 3. Google PaLM (swarms.agents.models.GooglePalm) + +The GooglePalm class provides an interface for Google's PaLM Chat API. + +**Constructor:** +```python +GooglePalm(model_name: str = "models/chat-bison-001", google_api_key: str = None, temperature: float = None, top_p: float = None, top_k: int = None, n: int = 1) +``` + +**Attributes:** + +- `model_name` (str): Name of the Google PaLM model. + +- `google_api_key` (str, optional): Google API key. + +- `temperature` (float, optional): Temperature for text generation. + +- `top_p` (float, optional): Top-p sampling value. + +- `top_k` (int, optional): Top-k sampling value. + +- `n` (int, default=1): Number of candidate completions. + +**Methods:** + +- `generate(messages: List[Dict[str, Any]], stop: List[str] = None, **kwargs) -> Dict[str, Any]`: Generate text based on a list of messages. + +- `__call__(messages: List[Dict[str, Any]], stop: List[str] = None, **kwargs) -> Dict[str, Any]`: Generate text using the call syntax. + +**Usage Example:** +```python +from swarms import GooglePalm + +google_palm = GooglePalm() +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"]) +``` + +### 4. Anthropic (swarms.agents.models.Anthropic) + +The Anthropic class enables interaction with Anthropic's large language models. + +**Constructor:** +```python +Anthropic(model: str = "claude-2", max_tokens_to_sample: int = 256, temperature: float = None, top_k: int = None, top_p: float = None, streaming: bool = False, default_request_timeout: int = None) +``` + +**Attributes:** + +- `model` (str): Name of the Anthropic model. + +- `max_tokens_to_sample` (int, default=256): Maximum tokens to sample. + +- `temperature` (float, optional): Temperature for text generation. + +- `top_k` (int, optional): Top-k sampling value. + +- `top_p` (float, optional): Top-p sampling value. + +- `streaming` (bool, default=False): Enable streaming mode. + +- `default_request_timeout` (int, optional): Default request timeout. + +**Methods:** + +- `generate(prompt: str, stop: List[str] = None) -> str`: Generate text based on a prompt. + +**Usage Example:** +```python +from swarms import Anthropic + +anthropic = Anthropic() +prompt = "Once upon a time" +generated_text = anthropic.generate(prompt) +print(generated_text) +``` + +This concludes the documentation for the "models" folder, providing you with tools to seamlessly integrate with various language models and APIs. Happy coding! \ No newline at end of file diff --git a/docs/swarms/swarms/autoscaler.md b/docs/swarms/swarms/autoscaler.md new file mode 100644 index 00000000..703ae860 --- /dev/null +++ b/docs/swarms/swarms/autoscaler.md @@ -0,0 +1,175 @@ +### Enterprise Grade Documentation + +--- + +## AutoScaler Class from `swarms` Package + +The `AutoScaler` class, part of the `swarms` package, provides a dynamic mechanism to handle agents depending on the workload. This document outlines how to use it, complete with import statements and examples. + +--- + +### Importing the AutoScaler Class + +Before you can use the `AutoScaler` class, you must import it from the `swarms` package: + +```python +from swarms import AutoScaler +``` + +--- + +### Constructor: `AutoScaler.__init__()` + +**Description**: +Initializes the `AutoScaler` with a predefined number of agents and sets up configurations for scaling. + +**Parameters**: +- `initial_agents (int)`: Initial number of agents. Default is 10. +- `scale_up_factor (int)`: Multiplicative factor to scale up the number of agents. Default is 2. +- `idle_threshold (float)`: Threshold below which agents are considered idle. Expressed as a ratio (0-1). Default is 0.2. +- `busy_threshold (float)`: Threshold above which agents are considered busy. Expressed as a ratio (0-1). Default is 0.7. + +**Returns**: +- None + +**Example Usage**: +```python +from swarms import AutoScaler + +scaler = AutoScaler(initial_agents=5, scale_up_factor=3, idle_threshold=0.1, busy_threshold=0.8) +``` + +--- + +### Method: `AutoScaler.add_task(task)` + +**Description**: +Enqueues the specified task into the task queue. + +**Parameters**: +- `task`: The task to be added to the queue. + +**Returns**: +- None + +**Example Usage**: +```python +task_data = "Process dataset X" +scaler.add_task(task_data) +``` + +--- + +### Method: `AutoScaler.scale_up()` + +**Description**: +Scales up the number of agents based on the specified scale-up factor. + +**Parameters**: +- None + +**Returns**: +- None + +**Example Usage**: +```python +# Called internally but can be manually invoked if necessary +scaler.scale_up() +``` + +--- + +### Method: `AutoScaler.scale_down()` + +**Description**: +Scales down the number of agents, ensuring a minimum is always present. + +**Parameters**: +- None + +**Returns**: +- None + +**Example Usage**: +```python +# Called internally but can be manually invoked if necessary +scaler.scale_down() +``` + +--- + +### Method: `AutoScaler.monitor_and_scale()` + +**Description**: +Continuously monitors the task queue and agent utilization to decide on scaling. + +**Parameters**: +- None + +**Returns**: +- None + +**Example Usage**: +```python +# This method is internally used as a thread and does not require manual invocation in most scenarios. +``` + +--- + +### Method: `AutoScaler.start()` + +**Description**: +Initiates the monitoring process and starts processing tasks from the queue. + +**Parameters**: +- None + +**Returns**: +- None + +**Example Usage**: +```python +scaler.start() +``` + +--- + +### Full Usage + +```python +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) + +# Start the monitoring and task processing +auto_scaler.start() + +# Simulate the addition of tasks +for i in range(100): + auto_scaler.add_task(f"Task {i}") +``` + +### Pass in Custom Agent +You can pass any agent class that adheres to the required interface (like having a run() method). If no class is passed, it defaults to using AutoBot. This makes the AutoScaler more flexible and able to handle a wider range of agent implementations. + +```python +from swarms import AutoScaler + +auto_scaler = AutoScaler(agent=YourCustomAgent) +auto_scaler.start() + +for i in range(100): # Adding tasks + auto_scaler.add_task(f"Task {i}") + +``` + + +--- + +**Notes**: +1. Adjust the thresholds and scaling factors as per your specific requirements and nature of the tasks. +2. The provided implementation is a baseline. Depending on your production environment, you may need additional features, error-handling, and optimizations. +3. Ensure that the `swarms` package and its dependencies are installed in your environment. + +--- diff --git a/docs/swarms/workers/index.md b/docs/swarms/workers/index.md new file mode 100644 index 00000000..f1c9c789 --- /dev/null +++ b/docs/swarms/workers/index.md @@ -0,0 +1,97 @@ +# Module Name: Worker + +The `Worker` class encapsulates the idea of a semi-autonomous agent that utilizes a large language model to execute tasks. The module provides a unified interface for AI-driven task execution while combining a series of tools and utilities. It sets up memory storage and retrieval mechanisms for contextual recall and offers an option for human involvement, making it a versatile and adaptive agent for diverse applications. + +## **Class Definition**: + +```python +class Worker: +``` + +### **Parameters**: + +- `model_name` (str, default: "gpt-4"): Name of the language model. +- `openai_api_key` (str, Optional): API key for accessing OpenAI's models. +- `ai_name` (str, default: "Autobot Swarm Worker"): Name of the AI agent. +- `ai_role` (str, default: "Worker in a swarm"): Role description of the AI agent. +- `external_tools` (list, Optional): A list of external tool objects to be used. +- `human_in_the_loop` (bool, default: False): If set to `True`, it indicates that human intervention may be required. +- `temperature` (float, default: 0.5): Sampling temperature for the language model's output. Higher values make the output more random, and lower values make it more deterministic. + +### **Methods**: + +#### `__init__`: + +Initializes the Worker class. + +#### `setup_tools`: + +Sets up the tools available to the worker. Default tools include reading and writing files, processing CSV data, querying websites, and taking human input. Additional tools can be appended through the `external_tools` parameter. + +#### `setup_memory`: + +Initializes memory systems using embeddings and a vector store for the worker. + +#### `setup_agent`: + +Sets up the primary agent using the initialized tools, memory, and language model. + +#### `run`: + +Executes a given task using the agent. + +#### `__call__`: + +Makes the Worker class callable. When an instance of the class is called, it will execute the provided task using the agent. + +## **Usage Examples**: + +### **Example 1**: Basic usage with default parameters: + +```python +from swarms import Worker + +worker = Worker(model_name="gpt-4", openai_api_key="YOUR_API_KEY") +result = worker.run("Summarize the document.") +``` + +### **Example 2**: Usage with custom tools: + +```python +from swarms import Worker, MyCustomTool + +worker = Worker(model_name="gpt-4", openai_api_key="YOUR_API_KEY", external_tools=[MyCustomTool()]) +result = worker.run("Perform a custom operation on the document.") +``` + +### **Example 3**: Usage with human in the loop: + +```python +from swarms import Worker + +worker = Worker(model_name="gpt-4", openai_api_key="YOUR_API_KEY", human_in_the_loop=True) +result = worker.run("Translate this complex document, and ask for help if needed.") +``` + +## **Mathematical Description**: + +Conceptually, the `Worker` class can be seen as a function: + +\[ W(t, M, K, T, H, \theta) \rightarrow R \] + +Where: + +- \( W \) = Worker function +- \( t \) = task to be performed +- \( M \) = Model (e.g., "gpt-4") +- \( K \) = OpenAI API key +- \( T \) = Set of Tools available +- \( H \) = Human involvement flag (True/False) +- \( \theta \) = Temperature parameter +- \( R \) = Result of the task + +This mathematical abstraction provides a simple view of the `Worker` class's capability to transform a task input into a desired output using a combination of AI and toolsets. + +## **Notes**: + +The Worker class acts as a bridge between raw tasks and the tools & AI required to accomplish them. The setup ensures flexibility and versatility. The decorators used in the methods (e.g., log_decorator, error_decorator) emphasize the importance of logging, error handling, and performance measurement, essential for real-world applications. \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml new file mode 100644 index 00000000..5bbcda47 --- /dev/null +++ b/mkdocs.yml @@ -0,0 +1,106 @@ +site_name: Swarms Docs +plugins: + - glightbox + - search +copyright: "© APAC Corp, Inc." +extra_css: + - docs/assets/css/extra.css +extra: + # analytics: + # provider: google + # property: G-QM8EDPSCB6 + social: + - icon: fontawesome/solid/house + link: assets/img/SwarmsLogoIcon.png + - icon: fontawesome/brands/discord + link: https://discord.gg/qUtxnK2NMf + - icon: fontawesome/brands/github + link: https://github.com/kyegomez/Swarms/ + - icon: fontawesome/brands/python + link: https://pypi.org/project/Swarms/ +theme: + name: material + custom_dir: docs/overrides + logo: assets/img/SwarmsLogoIcon.png + palette: + # Palette toggle for light mode + - scheme: default + primary: black + toggle: + icon: material/brightness-7 + name: Switch to dark mode + # Palette toggle for dark mode + - scheme: slate + primary: black + toggle: + icon: material/brightness-4 + name: Switch to light mode + features: + - content.code.copy + - content.code.annotate + - navigation.tabs + - navigation.sections + - navigation.expand + - navigation.top + - announce.dismiss +markdown_extensions: + - pymdownx.highlight: + anchor_linenums: true + line_spans: __span + pygments_lang_class: true + - admonition + - pymdownx.inlinehilite + - pymdownx.snippets + - pymdownx.superfences + - pymdownx.details + - pymdownx.tabbed + - tables + - def_list + - footnotes +nav: +- Home: + - Overview: "index.md" + - Contributing: "contributing.md" + - FAQ: "faq.md" + - Purpose: "purpose.md" + - Roadmap: "roadmap.md" + - Weaknesses: "failures.md" + - Design: "design.md" + - Flywheel: "flywheel.md" + - Bounties: "bounties.md" + - Metric: "metric.md" + - Distribution: "distribution" + - Research: "research.md" + - Demos: "demos.md" + - Architecture: "architecture.md" + - Checklist: "checklist.md" + - Hiring: "hiring.md" +- Swarms: + - Overview: "swarms/index.md" + - AutoScaler: "swarms/swarms/autoscaler.md" + - Workers: + - Overview: "swarms/workers/index.md" + - Agents: + - Base Models: + - Overview: "swarms/models/index.md" + - HuggingFaceLLM: "swarms/models/hf.md" + - Anthropic: "swarms/models/anthropic.md" +- Examples: + - Overview: "examples/index.md" + - Agents: + - Talk to Redshift: "examples/talk-to-redshift.md" + - Talk to a Webpage: "examples/talk-to-a-webpage.md" + - Talk to a PDF: "examples/talk-to-a-pdf.md" + - Data: + - Load and Query Pinecone: "examples/load-and-query-pinecone.md" + - Load and Query Marqo: "examples/load-query-and-chat-marqo.md" + - Query a Webpage: "examples/query-webpage.md" + - Misc: + - Count Tokens: "examples/count-tokens.md" + - Using Text Generation Web UI: "examples/using-text-generation-web-ui.md" + - Storing Conversation Memory in DynamoDB: "examples/store-conversation-memory-in-dynamodb.md" +- Applications: + - CustomerSupport: + - Overview: "applications/customer_support.md" + - Marketing: + - Overview: "applications/marketing_agencies.md" diff --git a/DOCS/C0NTRIBUTING.md b/old-docs/C0NTRIBUTING.md similarity index 100% rename from DOCS/C0NTRIBUTING.md rename to old-docs/C0NTRIBUTING.md diff --git a/DOCS/DOCUMENTATION.md b/old-docs/DOCUMENTATION.md similarity index 100% rename from DOCS/DOCUMENTATION.md rename to old-docs/DOCUMENTATION.md diff --git a/old-docs/README.md b/old-docs/README.md new file mode 100644 index 00000000..e69de29b diff --git a/DOCS/Tutorials/GettingStartedLLM.md b/old-docs/Tutorials/GettingStartedLLM.md similarity index 100% rename from DOCS/Tutorials/GettingStartedLLM.md rename to old-docs/Tutorials/GettingStartedLLM.md diff --git a/DOCS/agents/MODELS.md b/old-docs/agents/MODELS.md similarity index 100% rename from DOCS/agents/MODELS.md rename to old-docs/agents/MODELS.md diff --git a/DOCS/agents/README.md b/old-docs/agents/README.md similarity index 100% rename from DOCS/agents/README.md rename to old-docs/agents/README.md diff --git a/DOCS/Corp/BENEFITS.md b/old-docs/corp/BENEFITS.md similarity index 100% rename from DOCS/Corp/BENEFITS.md rename to old-docs/corp/BENEFITS.md diff --git a/DOCS/Corp/DEMO_IDEAS.md b/old-docs/corp/DEMO_IDEAS.md similarity index 100% rename from DOCS/Corp/DEMO_IDEAS.md rename to old-docs/corp/DEMO_IDEAS.md diff --git a/DOCS/Corp/DEVELOPER_PLAN.md b/old-docs/corp/DEVELOPER_PLAN.md similarity index 100% rename from DOCS/Corp/DEVELOPER_PLAN.md rename to old-docs/corp/DEVELOPER_PLAN.md diff --git a/old-docs/corp/FLYWHEEL.md b/old-docs/corp/FLYWHEEL.md new file mode 100644 index 00000000..ac8851be --- /dev/null +++ b/old-docs/corp/FLYWHEEL.md @@ -0,0 +1,101 @@ +# The Swarms Flywheel + +1. **Building a Supportive Community:** Initiate by establishing an engaging and inclusive open-source community for both developers and sales freelancers around Swarms. Regular online meetups, webinars, tutorials, and sales training can make them feel welcome and encourage contributions and sales efforts. + +2. **Increased Contributions and Sales Efforts:** The more engaged the community, the more developers will contribute to Swarms and the more effort sales freelancers will put into selling Swarms. + +3. **Improvement in Quality and Market Reach:** More developer contributions mean better quality, reliability, and feature offerings from Swarms. Simultaneously, increased sales efforts from freelancers boost Swarms' market penetration and visibility. + +4. **Rise in User Base:** As Swarms becomes more robust and more well-known, the user base grows, driving more revenue. + +5. **Greater Financial Incentives:** Increased revenue can be redirected to offer more significant financial incentives to both developers and salespeople. Developers can be incentivized based on their contribution to Swarms, and salespeople can be rewarded with higher commissions. + +6. **Attract More Developers and Salespeople:** These financial incentives, coupled with the recognition and experience from participating in a successful project, attract more developers and salespeople to the community. + +7. **Wider Adoption of Swarms:** An ever-improving product, a growing user base, and an increasing number of passionate salespeople accelerate the adoption of Swarms. + +8. **Return to Step 1:** As the community, user base, and sales network continue to grow, the cycle repeats, each time speeding up the flywheel. + + +```markdown + +---------------------+ + | Building a | + | Supportive | <--+ + | Community | | + +--------+-----------+ | + | | + v | + +--------+-----------+ | + | Increased | | + | Contributions & | | + | Sales Efforts | | + +--------+-----------+ | + | | + v | + +--------+-----------+ | + | Improvement in | | + | Quality & Market | | + | Reach | | + +--------+-----------+ | + | | + v | + +--------+-----------+ | + | Rise in User | | + | Base | | + +--------+-----------+ | + | | + v | + +--------+-----------+ | + | Greater Financial | | + | Incentives | | + +--------+-----------+ | + | | + v | + +--------+-----------+ | + | Attract More | | + | Developers & | | + | Salespeople | | + +--------+-----------+ | + | | + v | + +--------+-----------+ | + | Wider Adoption of | | + | Swarms |----+ + +---------------------+ +``` + + +# Potential Risks and Mitigations: + +1. **Insufficient Contributions or Quality of Work**: Open-source efforts rely on individuals being willing and able to spend time contributing. If not enough people participate, or the work they produce is of poor quality, the product development could stall. + * **Mitigation**: Create a robust community with clear guidelines, support, and resources. Provide incentives for quality contributions, such as a reputation system, swag, or financial rewards. Conduct thorough code reviews to ensure the quality of contributions. + +2. **Lack of Sales Results**: Commission-based salespeople will only continue to sell the product if they're successful. If they aren't making enough sales, they may lose motivation and cease their efforts. + * **Mitigation**: Provide adequate sales training and resources. Ensure the product-market fit is strong, and adjust messaging or sales tactics as necessary. Consider implementing a minimum commission or base pay to reduce risk for salespeople. + +3. **Poor User Experience or User Adoption**: If users don't find the product useful or easy to use, they won't adopt it, and the user base won't grow. This could also discourage salespeople and contributors. + * **Mitigation**: Prioritize user experience in the product development process. Regularly gather and incorporate user feedback. Ensure robust user support is in place. + +4. **Inadequate Financial Incentives**: If the financial rewards don't justify the time and effort contributors and salespeople are putting in, they will likely disengage. + * **Mitigation**: Regularly review and adjust financial incentives as needed. Ensure that the method for calculating and distributing rewards is transparent and fair. + +5. **Security and Compliance Risks**: As the user base grows and the software becomes more complex, the risk of security issues increases. Moreover, as contributors from various regions join, compliance with various international laws could become an issue. + * **Mitigation**: Establish strong security practices from the start. Regularly conduct security audits. Seek legal counsel to understand and adhere to international laws and regulations. + +## Activation Plan for the Flywheel: + +1. **Community Building**: Begin by fostering a supportive community around Swarms. Encourage early adopters to contribute and provide feedback. Create comprehensive documentation, community guidelines, and a forum for discussion and support. + +2. **Sales and Development Training**: Provide resources and training for salespeople and developers. Make sure they understand the product, its value, and how to effectively contribute or sell. + +3. **Increase Contributions and Sales Efforts**: Encourage increased participation by highlighting successful contributions and sales, rewarding top contributors and salespeople, and regularly communicating about the project's progress and impact. + +4. **Iterate and Improve**: Continually gather and implement feedback to improve Swarms and its market reach. The better the product and its alignment with the market, the more the user base will grow. + +5. **Expand User Base**: As the product improves and sales efforts continue, the user base should grow. Ensure you have the infrastructure to support this growth and maintain a positive user experience. + +6. **Increase Financial Incentives**: As the user base and product grow, so too should the financial incentives. Make sure rewards continue to be competitive and attractive. + +7. **Attract More Contributors and Salespeople**: As the financial incentives and success of the product increase, this should attract more contributors and salespeople, further feeding the flywheel. + +Throughout this process, it's important to regularly reassess and adjust your strategy as necessary. Stay flexible and responsive to changes in the market, user feedback, and the evolving needs of the community. \ No newline at end of file diff --git a/DOCS/Corp/MANIFESTO.md b/old-docs/corp/MANIFESTO.md similarity index 100% rename from DOCS/Corp/MANIFESTO.md rename to old-docs/corp/MANIFESTO.md diff --git a/DOCS/Corp/MISSION.md b/old-docs/corp/MISSION.md similarity index 100% rename from DOCS/Corp/MISSION.md rename to old-docs/corp/MISSION.md diff --git a/DOCS/Corp/MONETIZATION.md b/old-docs/corp/MONETIZATION.md similarity index 100% rename from DOCS/Corp/MONETIZATION.md rename to old-docs/corp/MONETIZATION.md diff --git a/old-docs/corp/PITCH.md b/old-docs/corp/PITCH.md new file mode 100644 index 00000000..14381b50 --- /dev/null +++ b/old-docs/corp/PITCH.md @@ -0,0 +1,14 @@ + +## Purpose +Artificial Intelligence has grown at an exponential rate over the past decade. Yet, we are far from fully harnessing its potential. Today's AI operates in isolation, each working separately in their corner. But life doesn't work like that. The world doesn't work like that. Success isn't built in silos; it's built in teams. + +Imagine a world where AI models work in unison. Where they can collaborate, interact, and pool their collective intelligence to achieve more than any single model could. This is the future we envision. But today, we lack a framework for AI to collaborate effectively, to form a true swarm of intelligent agents. + + +This is a difficult problem, one that has eluded solution. It requires sophisticated systems that can allow individual models to not just communicate but also understand each other, pool knowledge and resources, and create collective intelligence. This is the next frontier of AI. + +But here at Swarms, we have a secret sauce. It's not just a technology or a breakthrough invention. It's a way of thinking - the philosophy of rapid iteration. With each cycle, we make massive progress. We experiment, we learn, and we grow. We have developed a pioneering framework that can enable AI models to work together as a swarm, combining their strengths to create richer, more powerful outputs. + +We are uniquely positioned to take on this challenge with 1,500+ devoted researchers in Agora. We have assembled a team of world-class experts, experienced and driven, united by a shared vision. Our commitment to breaking barriers, pushing boundaries, and our belief in the power of collective intelligence makes us the best team to usher in this future to fundamentally advance our species, Humanity. + +--- \ No newline at end of file diff --git a/DOCS/Corp/ROADMAP.md b/old-docs/corp/ROADMAP.md similarity index 100% rename from DOCS/Corp/ROADMAP.md rename to old-docs/corp/ROADMAP.md diff --git a/DOCS/Corp/SALES.md b/old-docs/corp/SALES.md similarity index 100% rename from DOCS/Corp/SALES.md rename to old-docs/corp/SALES.md diff --git a/DOCS/Corp/SALESPEOPLE_PLAN.md b/old-docs/corp/SALESPEOPLE_PLAN.md similarity index 100% rename from DOCS/Corp/SALESPEOPLE_PLAN.md rename to old-docs/corp/SALESPEOPLE_PLAN.md diff --git a/DOCS/Design/CLEAN_CODE.md b/old-docs/design/CLEAN_CODE.md similarity index 100% rename from DOCS/Design/CLEAN_CODE.md rename to old-docs/design/CLEAN_CODE.md diff --git a/DOCS/Design/DESIGN.md b/old-docs/design/DESIGN.md similarity index 100% rename from DOCS/Design/DESIGN.md rename to old-docs/design/DESIGN.md diff --git a/DOCS/Design/DESIGN_PHILOSOPHY.md b/old-docs/design/DESIGN_PHILOSOPHY.md similarity index 100% rename from DOCS/Design/DESIGN_PHILOSOPHY.md rename to old-docs/design/DESIGN_PHILOSOPHY.md diff --git a/old-docs/design/GOLDEN_METRIC.md b/old-docs/design/GOLDEN_METRIC.md new file mode 100644 index 00000000..8340d634 --- /dev/null +++ b/old-docs/design/GOLDEN_METRIC.md @@ -0,0 +1,225 @@ +# The Golden Metric: 95% User-Task-Completion-Satisfaction Rate + +In the world of Swarms, there’s one metric that stands above the rest: the User-Task-Completion-Satisfaction (UTCS) rate. This metric is the heart of our system, the pulse that keeps us moving forward. It’s not just a number; it’s a reflection of our commitment to our users and a measure of our success. + +## What is the UTCS Rate? +The UTCS rate is a measure of how reliably and quickly Swarms can satisfy a user demand. It’s calculated by dividing the number of tasks completed to the user’s satisfaction by the total number of tasks. Multiply that by 100, and you’ve got your UTCS rate. + +But what does it mean to complete a task to the user’s satisfaction? It means that the task is not only completed, but completed in a way that meets or exceeds the user’s expectations. It’s about quality, speed, and reliability. + +## Why is the UTCS Rate Important? +The UTCS rate is a direct reflection of the user experience. A high UTCS rate means that users are getting what they need from Swarms, and they’re getting it quickly and reliably. It means that Swarms is doing its job, and doing it well. + +But the UTCS rate is not just about user satisfaction. It’s also a measure of Swarms’ efficiency and effectiveness. A high UTCS rate means that Swarms is able to complete tasks quickly and accurately, with minimal errors or delays. It’s a sign of a well-oiled machine. + +## How Do We Achieve a 95% UTCS Rate? +Achieving a 95% UTCS rate is no small feat. It requires a deep understanding of our users and their needs, a robust and reliable system, and a commitment to continuous improvement. + +### Here are some strategies we’re implementing to reach our goal: + +* Understanding User Needs: We must have agents that gain an understanding of the user's objective and break it up into it's most fundamental building blocks + +* Improving System Reliability: We’re working to make Swarms more reliable, reducing errors and improving the accuracy of task completion. This includes improving our algorithms, refining our processes, and investing in quality assurance. + +* Optimizing for Speed: We’re optimizing Swarms to complete tasks as quickly as possible, without sacrificing quality. This includes improving our infrastructure, streamlining our workflows, and implementing performance optimizations. + +*Iterating and Improving: We’re committed to continuous improvement. We’re constantly monitoring our UTCS rate and other key metrics, and we’re always looking for ways to improve. We’re not afraid to experiment, iterate, and learn from our mistakes. + +Achieving a 95% UTCS rate is a challenging goal, but it’s a goal worth striving for. It’s a goal that will drive us to improve, innovate, and deliver the best possible experience for our users. And in the end, that’s what Swarms is all about. + + + +# Your Feedback Matters: Help Us Optimize the UTCS Rate + +As we initiate the journey of Swarms, we seek your feedback to better guide our growth and development. Your opinions and suggestions are crucial for us, helping to mold our product, pricing, branding, and a host of other facets that influence your experience. + +## Your Insights on the UTCS Rate +Our goal is to maintain a UTCS (User-Task-Completion-Satisfaction) rate of 95%. This metric is integral to the success of Swarms, indicating the efficiency and effectiveness with which we satisfy user requests. However, it's a metric that we can't optimize alone - we need your help. + +Here's what we want to understand from you: + +1. **Satisfaction:** What does a "satisfactorily completed task" mean to you? Are there specific elements that contribute to a task being carried out to your satisfaction? +2. **Timeliness:** How important is speed in the completion of a task? What would you consider a reasonable timeframe for a task to be completed? +3. **Usability:** How intuitive and user-friendly do you find the Swarms platform? Are there any aspects of the platform that you believe could be enhanced? +4. **Reliability:** How much does consistency in performance matter to you? Can you share any experiences where Swarms either met or fell short of your expectations? +5. **Value for Money:** How do you perceive our pricing? Does the value Swarms provides align with the costs? + +We invite you to share your experiences, thoughts, and ideas. Whether it's a simple suggestion or an in-depth critique, we appreciate and value your input. + +## Your Feedback: The Backbone of our Growth +Your feedback is the backbone of Swarms' evolution. It drives us to refine our strategies, fuels our innovative spirit, and, most importantly, enables us to serve you better. + +As we launch, we open the conversation around these key aspects of Swarms, and we look forward to understanding your expectations, your needs, and how we can deliver the best experience for you. + +So, let's start this conversation - how can we make Swarms work best for you? + + +Guide Our Growth: Help Optimize Swarms +As we launch Swarms, your feedback is critical for enhancing our product, pricing, and branding. A key aim for us is a User-Task-Completion-Satisfaction (UTCS) rate of 95% - indicating our efficiency and effectiveness in meeting user needs. However, we need your insights to optimize this. + +Here's what we're keen to understand: + +Satisfaction: Your interpretation of a "satisfactorily completed task". +Timeliness: The importance of speed in task completion for you. +Usability: Your experiences with our platform’s intuitiveness and user-friendliness. +Reliability: The significance of consistent performance to you. +Value for Money: Your thoughts on our pricing and value proposition. +We welcome your thoughts, experiences, and suggestions. Your feedback fuels our evolution, driving us to refine strategies, boost innovation, and enhance your experience. + +Let's start the conversation - how can we make Swarms work best for you? + + +-------- + +**The Golden Metric Analysis: The Ultimate UTCS Paradigm for Swarms** + +### Introduction + +In our ongoing journey to perfect Swarms, understanding how our product fares in the eyes of the end-users is paramount. Enter the User-Task-Completion-Satisfaction (UTCS) rate - our primary metric that gauges how reliably and swiftly Swarms can meet user demands. As we steer Swarms towards achieving a UTCS rate of 95%, understanding this metric's core and how to refine it becomes vital. + +### Decoding UTCS: An Analytical Overview + +The UTCS rate is not merely about task completion; it's about the comprehensive experience. Therefore, its foundations lie in: + +1. **Quality**: Ensuring tasks are executed flawlessly. +2. **Speed**: Delivering results in the shortest possible time. +3. **Reliability**: Consistency in quality and speed across all tasks. + +We can represent the UTCS rate with the following equation: + +```latex +\[ UTCS Rate = \frac{(Completed Tasks \times User Satisfaction)}{(Total Tasks)} \times 100 \] +``` + +Where: +- Completed Tasks refer to the number of tasks Swarms executes without errors. +- User Satisfaction is the subjective component, gauged through feedback mechanisms. This could be on a scale of 1-10 (or a percentage). +- Total Tasks refer to all tasks processed by Swarms, regardless of the outcome. + +### The Golden Metric: Swarm Efficiency Index (SEI) + +However, this basic representation doesn't factor in a critical component: system performance. Thus, we introduce the Swarm Efficiency Index (SEI). The SEI encapsulates not just the UTCS rate but also system metrics like memory consumption, number of tasks, and time taken. By blending these elements, we aim to present a comprehensive view of Swarm's prowess. + +Here’s the formula: + +```latex +\[ SEI = \frac{UTCS Rate}{(Memory Consumption + Time Window + Task Complexity)} \] +``` + +Where: +- Memory Consumption signifies the system resources used to accomplish tasks. +- Time Window is the timeframe in which the tasks were executed. +- Task Complexity could be a normalized scale that defines how intricate a task is (e.g., 1-5, with 5 being the most complex). + +Rationale: +- **Incorporating Memory Consumption**: A system that uses less memory but delivers results is more efficient. By inverting memory consumption in the formula, we emphasize that as memory usage goes down, SEI goes up. + +- **Considering Time**: Time is of the essence. The faster the results without compromising quality, the better. By adding the Time Window, we emphasize that reduced task execution time increases the SEI. + +- **Factoring in Task Complexity**: Not all tasks are equal. A system that effortlessly completes intricate tasks is more valuable. By integrating task complexity, we can normalize the SEI according to the task's nature. + +### Implementing SEI & Improving UTCS + +Using feedback from elder-plinius, we can better understand and improve SEI and UTCS: + +1. **Feedback Across Skill Levels**: By gathering feedback from users with different skill levels, we can refine our metrics, ensuring Swarms caters to all. + +2. **Simplifying Setup**: Detailed guides can help newcomers swiftly get on board, thus enhancing user satisfaction. + +3. **Enhancing Workspace and Agent Management**: A clearer view of the Swarm's internal structure, combined with on-the-go adjustments, can improve both the speed and quality of results. + +4. **Introducing System Suggestions**: A proactive Swarms that provides real-time insights and recommendations can drastically enhance user satisfaction, thus pushing up the UTCS rate. + +### Conclusion + +The UTCS rate is undeniably a pivotal metric for Swarms. However, with the introduction of the Swarm Efficiency Index (SEI), we have an opportunity to encapsulate a broader spectrum of performance indicators, leading to a more holistic understanding of Swarms' efficiency. By consistently optimizing for SEI, we can ensure that Swarms not only meets user expectations but also operates at peak system efficiency. + + +---------------- +**Research Analysis: Tracking and Ensuring Reliability of Swarm Metrics at Scale** + +### 1. Introduction + +In our pursuit to optimize the User-Task-Completion-Satisfaction (UTCS) rate and Swarm Efficiency Index (SEI), reliable tracking of these metrics at scale becomes paramount. This research analysis delves into methodologies, technologies, and practices that can be employed to monitor these metrics accurately and efficiently across vast data sets. + +### 2. Why Tracking at Scale is Challenging + +The primary challenges include: + +- **Volume of Data**: As Swarms grows, the data generated multiplies exponentially. +- **Variability of Data**: Diverse user inputs lead to myriad output scenarios. +- **System Heterogeneity**: Different configurations and deployments can yield variable results. + +### 3. Strategies for Scalable Tracking + +#### 3.1. Distributed Monitoring Systems + +**Recommendation**: Implement distributed systems like Prometheus or InfluxDB. + +**Rationale**: +- Ability to collect metrics from various Swarm instances concurrently. +- Scalable and can handle vast data influxes. + +#### 3.2. Real-time Data Processing + +**Recommendation**: Use stream processing systems like Apache Kafka or Apache Flink. + +**Rationale**: +- Enables real-time metric calculation. +- Can handle high throughput and low-latency requirements. + +#### 3.3. Data Sampling + +**Recommendation**: Random or stratified sampling of user sessions. + +**Rationale**: +- Reduces the data volume to be processed. +- Maintains representativeness of overall user experience. + +### 4. Ensuring Reliability in Data Collection + +#### 4.1. Redundancy + +**Recommendation**: Integrate redundancy into data collection nodes. + +**Rationale**: +- Ensures no single point of failure. +- Data loss prevention in case of system malfunctions. + +#### 4.2. Anomaly Detection + +**Recommendation**: Implement AI-driven anomaly detection systems. + +**Rationale**: +- Identifies outliers or aberrations in metric calculations. +- Ensures consistent and reliable data interpretation. + +#### 4.3. Data Validation + +**Recommendation**: Establish automated validation checks. + +**Rationale**: +- Ensures only accurate and relevant data is considered. +- Eliminates inconsistencies arising from corrupted or irrelevant data. + +### 5. Feedback Loops and Continuous Refinement + +#### 5.1. User Feedback Integration + +**Recommendation**: Develop an in-built user feedback mechanism. + +**Rationale**: +- Helps validate the perceived vs. actual performance. +- Allows for continuous refining of tracking metrics and methodologies. + +#### 5.2. A/B Testing + +**Recommendation**: Regularly conduct A/B tests for new tracking methods or adjustments. + +**Rationale**: +- Determines the most effective methods for data collection. +- Validates new tracking techniques against established ones. + +### 6. Conclusion + +To successfully and reliably track the UTCS rate and SEI at scale, it's essential to combine robust monitoring tools, data processing methodologies, and validation techniques. By doing so, Swarms can ensure that the metrics collected offer a genuine reflection of system performance and user satisfaction. Regular feedback and iterative refinement, rooted in a culture of continuous improvement, will further enhance the accuracy and reliability of these essential metrics. \ No newline at end of file diff --git a/DOCS/Design/abstraction.py b/old-docs/design/abstraction.py similarity index 100% rename from DOCS/Design/abstraction.py rename to old-docs/design/abstraction.py diff --git a/DOCS/ideas/AGENT_NAME.md b/old-docs/ideas/AGENT_NAME.md similarity index 100% rename from DOCS/ideas/AGENT_NAME.md rename to old-docs/ideas/AGENT_NAME.md diff --git a/DOCS/ideas/HIVEMIND.md b/old-docs/ideas/HIVEMIND.md similarity index 100% rename from DOCS/ideas/HIVEMIND.md rename to old-docs/ideas/HIVEMIND.md diff --git a/DOCS/ideas/IDEAS.MD b/old-docs/ideas/IDEAS.MD similarity index 100% rename from DOCS/ideas/IDEAS.MD rename to old-docs/ideas/IDEAS.MD diff --git a/DOCS/ideas/PLATFORMS.md b/old-docs/ideas/PLATFORMS.md similarity index 100% rename from DOCS/ideas/PLATFORMS.md rename to old-docs/ideas/PLATFORMS.md diff --git a/DOCS/ideas/SWARMSOS.md b/old-docs/ideas/SWARMSOS.md similarity index 100% rename from DOCS/ideas/SWARMSOS.md rename to old-docs/ideas/SWARMSOS.md diff --git a/DOCS/ideas/aug13.md b/old-docs/ideas/aug13.md similarity index 100% rename from DOCS/ideas/aug13.md rename to old-docs/ideas/aug13.md diff --git a/DOCS/ideas/aug16.md b/old-docs/ideas/aug16.md similarity index 100% rename from DOCS/ideas/aug16.md rename to old-docs/ideas/aug16.md diff --git a/DOCS/Research/AGENTS.md b/old-docs/research/AGENTS.md similarity index 100% rename from DOCS/Research/AGENTS.md rename to old-docs/research/AGENTS.md diff --git a/DOCS/Research/LANGCHAIN_WEAKNESS.md b/old-docs/research/LANGCHAIN_WEAKNESS.md similarity index 100% rename from DOCS/Research/LANGCHAIN_WEAKNESS.md rename to old-docs/research/LANGCHAIN_WEAKNESS.md diff --git a/DOCS/Research/RESEARCH.md b/old-docs/research/RESEARCH.md similarity index 100% rename from DOCS/Research/RESEARCH.md rename to old-docs/research/RESEARCH.md diff --git a/DOCS/workers/VortexAgent.md b/old-docs/workers/VortexAgent.md similarity index 100% rename from DOCS/workers/VortexAgent.md rename to old-docs/workers/VortexAgent.md diff --git a/DOCS/workers/WorkerNode.md b/old-docs/workers/WorkerNode.md similarity index 100% rename from DOCS/workers/WorkerNode.md rename to old-docs/workers/WorkerNode.md diff --git a/swarms/models/base.py b/swarms/models/base.py index 5db5cb31..3e747a50 100644 --- a/swarms/models/base.py +++ b/swarms/models/base.py @@ -6,4 +6,7 @@ class AbstractModel(ABC): def generate(self, prompt): #generate text using language model pass + + def chat(self, prompt, history): + pass \ No newline at end of file
+ Star and contribute to Swarms on GitHub! +