swarms cleanup

.github/PULL_REQUEST_TEMPLATE.yml

@@ -22,4 +22,4 @@ Maintainer responsibilities:
 
 If no one reviews your PR within a few days, feel free to [email protected]
 
-See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/kyegomez/swarms
+See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/kyegomez/zeta

.github/workflows/pull-request-links.yml

@@ -15,4 +15,4 @@ jobs:
  steps:
  - uses: readthedocs/actions/preview@v1
  with:
- project-slug: swarms
+ project-slug: zeta

.github/workflows/unit-test.yml

@@ -24,7 +24,7 @@ jobs:
  run: pip install -r requirements.txt
 
  - name: Run Python unit tests
- run: python3 -m unittest tests/swarms
+ run: python3 -m unittest tests/zeta
 
  - name: Verify that the Docker image for the action builds
  run: docker build . --file Dockerfile
@@ -42,4 +42,4 @@ jobs:
  input-two: false
 
  - name: Verify integration test results
- run: python3 -m unittest unittesting/swarms
+ run: python3 -m unittest unittesting/zeta

README.md

@@ -1,9 +1,9 @@
 [![Multi-Modality](agorabanner.png)](https://discord.gg/qUtxnK2NMf)
 
 # Zeta - A Library for Zetascale Transformers
-[![Docs](https://readthedocs.org/projects/swarms/badge/)](https://swarms.readthedocs.io)
+[![Docs](https://readthedocs.org/projects/zeta/badge/)](https://zeta.readthedocs.io)
 
-Docs for [Zeta](https://github.com/kyegomez/swarms).
+Docs for [Zeta](https://github.com/kyegomez/zeta).
 
 <p>
  <a href="https://github.com/kyegomez/zeta/blob/main/LICENSE"><img alt="MIT License" src="https://img.shields.io/badge/license-MIT-blue.svg" /></a>

docs/applications/customer_support.md

@@ -3,15 +3,15 @@
 ---
 
 **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.
+In today's fast-paced digital world, responsive and efficient customer support is a linchpin for business success. The introduction of AI-driven zeta 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 Zeta for Customer Support:**
 
 1. **24/7 Availability**: Zeta 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.
+2. **Infinite Scalability**: Whether it's ten inquiries or ten thousand, zeta can handle fluctuating volumes with ease, eliminating the need for vast human teams and minimizing response times.
 
 3. **Adaptive Intelligence**: Zeta 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.
 
@@ -29,14 +29,14 @@ In today's fast-paced digital world, responsive and efficient customer support i
 
 - **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.
+- **Rule-based Operation**: By working with rule engines, zeta 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**: 
-Zeta 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.
+Zeta 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 zeta, businesses can stay ahead of the curve, ensuring unparalleled customer loyalty and satisfaction.
 
 **Experience the future of customer support. Dive into the swarm revolution.**
 

docs/applications/marketing_agencies.md

@@ -5,7 +5,7 @@
 ### **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.
+- Introduction to the transformative potential of zeta in reshaping the marketing industry.
 
 ---
 
@@ -58,7 +58,7 @@
 ---
 
 ### **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.
+- Reiterate the immense potential of zeta 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, zeta are the answer.
 
 ---

docs/architecture.md

@@ -2,7 +2,7 @@
 
 ## **1. Introduction**
 
-In today's rapidly evolving digital world, harnessing the collaborative power of multiple computational agents is more crucial than ever. 'Zeta' 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 Zeta vision.
+In today's rapidly evolving digital world, harnessing the collaborative power of multiple computational agents is more crucial than ever. 'Zeta' represents a bold stride in this direction—a scalable and dynamic framework designed to enable zeta 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 Zeta vision.
 
 ---
 
@@ -52,10 +52,10 @@ Envisioned as a 'Swarm of Zeta'. 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.
+* **Inter-Swarm Communication Protocols**: Dictates how zeta 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.
+Multiple zeta, each a formidable force, combine their prowess under the Hivemind. This level tackles monumental tasks by dividing them among zeta.
 
 ---
 
@@ -78,12 +78,12 @@ Multiple swarms, each a formidable force, combine their prowess under the Hivemi
 * 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.
+* Test zeta for efficiency, scalability, and robustness.
 
 ### **4.4 Apex Collaboration: Hivemind Level**
-* Build the Hivemind Orchestrator: Ensure it can oversee multiple swarms.
+* Build the Hivemind Orchestrator: Ensure it can oversee multiple zeta.
 * Define inter-swarm communication, prioritization, and task-sharing protocols.
-* Develop mechanisms to balance loads and optimize resource utilization across swarms.
+* Develop mechanisms to balance loads and optimize resource utilization across zeta.
 * Thoroughly test the Hivemind level for macro-task execution.
 
 ---
@@ -200,7 +200,7 @@ At the swarm level, the orchestrator is central. It's responsible for assigning
 ### 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.
+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 zeta, handling inter-swarm communications, and ensuring the overall system is functioning smoothly.
 
 **Diagram:**
 ```
@@ -229,7 +229,7 @@ At the Hivemind level, it's a multi-swarm setup, with an upper-layer orchestrato
 +-------+ +-------+ +-------+ +-------+ +-------+
 ```
 
-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.
+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 zeta efficiently.
 
 
 
@@ -312,14 +312,14 @@ The development of the Zeta framework requires a systematic and granular approac
 ## **4. Hivemind Level Development**
 
 ### **4.1 Hivemind Orchestrator Development**
-- [ ] Extend swarm orchestrator functionalities to manage multiple swarms.
+- [ ] Extend swarm orchestrator functionalities to manage multiple zeta.
 - [ ] Create inter-swarm communication protocols.
-- [ ] Implement load balancing mechanisms to distribute tasks across swarms.
+- [ ] Implement load balancing mechanisms to distribute tasks across zeta.
 - [ ] 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.
+- [ ] Design methods for zeta to exchange data.
+- [ ] Implement data reconciliation methods for zeta working on shared tasks.
 - [ ] Test inter-swarm communication for efficiency and data integrity.
 
 ---

docs/bounties.md

@@ -27,7 +27,7 @@ The third phase of our bounty program is the most exciting - this is where we ai
 
 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 Zeta.
 
-**To participate in our bounty program, visit the [Zeta Bounty Program Page](https://swarms.ai/bounty).** Let's build the future together!
+**To participate in our bounty program, visit the [Zeta Bounty Program Page](https://zeta.ai/bounty).** Let's build the future together!
 
 
 

docs/contributing.md

@@ -27,19 +27,19 @@ Join the Zeta community on Discord to connect with other contributors, coordinat
 
 
 ## Report and Issue
-The easiest way to contribute to our docs is through our public [issue tracker](https://github.com/kyegomez/swarms-docs/issues). Feel free to submit bugs, request features or changes, or contribute to the project directly. 
+The easiest way to contribute to our docs is through our public [issue tracker](https://github.com/kyegomez/zeta-docs/issues). Feel free to submit bugs, request features or changes, or contribute to the project directly. 
 
 ## Pull Requests
 
 Zeta docs are built using [MkDocs](https://squidfunk.github.io/mkdocs-material/getting-started/). 
 
-To directly contribute to Zeta documentation, first fork the [swarms-docs](https://github.com/kyegomez/swarms-docs) repository to your GitHub account. Then clone your repository to your local machine.
+To directly contribute to Zeta documentation, first fork the [zeta-docs](https://github.com/kyegomez/zeta-docs) repository to your GitHub account. Then clone your repository to your local machine.
 
 From inside the directory run: 
 
 ```pip install -r requirements.txt```
 
-To run `swarms-docs` locally run: 
+To run `zeta-docs` locally run: 
 
 ```mkdocs serve```
 
@@ -68,7 +68,7 @@ Follow the typical PR process to contribute changes.
 
 We have a growing list of tasks and issues that you can contribute to. To get started, follow these steps:
 
-1. Visit the [Zeta GitHub repository](https://github.com/kyegomez/swarms) and browse through the existing issues.
+1. Visit the [Zeta GitHub repository](https://github.com/kyegomez/zeta) and browse through the existing issues.
 
 2. Find an issue that interests you and make a comment stating that you would like to work on it. Include a brief description of how you plan to solve the problem and any questions you may have.
 

docs/design.md

@@ -56,14 +56,14 @@ Zeta is designed with a philosophy of simplicity and reliability. We believe tha
 
 ## Overview
 
-The goal of the Swarm Architecture is to provide a flexible and scalable system to build swarm intelligence models that can solve complex problems. This document details the proposed design to create a plug-and-play system, which makes it easy to create custom swarms, and provides pre-configured swarms with multi-modal agents.
+The goal of the Swarm Architecture is to provide a flexible and scalable system to build swarm intelligence models that can solve complex problems. This document details the proposed design to create a plug-and-play system, which makes it easy to create custom zeta, and provides pre-configured zeta with multi-modal agents.
 
 ## Design Principles
 
 - **Modularity**: The system will be built in a modular fashion, allowing various components to be easily swapped or upgraded.
 - **Interoperability**: Different swarm classes and components should be able to work together seamlessly.
-- **Scalability**: The design should support the growth of the system by adding more components or swarms.
-- **Ease of Use**: Users should be able to easily create their own swarms or use pre-configured ones with minimal configuration.
+- **Scalability**: The design should support the growth of the system by adding more components or zeta.
+- **Ease of Use**: Users should be able to easily create their own zeta or use pre-configured ones with minimal configuration.
 
 ## Design Components
 
@@ -79,13 +79,13 @@ Pre-configured swarm classes with multi-modal agents can be provided for ease of
 
 ### Tools and Agents
 
-Tools and agents are the components that provide the actual functionality to the swarms. They can be language models, AI assistants, vector stores, or any other components that can help in problem solving.
+Tools and agents are the components that provide the actual functionality to the zeta. They can be language models, AI assistants, vector stores, or any other components that can help in problem solving.
 
 To make the system plug-and-play, a standard interface should be defined for these components. Any new tool or agent should implement this interface, so that it can be easily plugged into the system.
 
 ## Usage
 
-Users can either use pre-configured swarms or create their own custom swarms.
+Users can either use pre-configured zeta or create their own custom zeta.
 
 To use a pre-configured swarm, they can simply instantiate the corresponding swarm class and call the run method with the required objective.
 
@@ -100,19 +100,19 @@ To create a custom swarm, they need to:
 ```python
 # Using pre-configured swarm
 swarm = PreConfiguredSwarm(openai_api_key)
-swarm.run_swarms(objective)
+swarm.run_zeta(objective)
 
 # Creating custom swarm
 class CustomSwarm(AbstractSwarm):
  # Implement required methods
 
 swarm = CustomSwarm(openai_api_key)
-swarm.run_swarms(objective)
+swarm.run_zeta(objective)
 ```
 
 ## Conclusion
 
-This Swarm Architecture design provides a scalable and flexible system for building swarm intelligence models. The plug-and-play design allows users to easily use pre-configured swarms or create their own custom swarms.
+This Swarm Architecture design provides a scalable and flexible system for building swarm intelligence models. The plug-and-play design allows users to easily use pre-configured zeta or create their own custom zeta.
 
 
 # Swarming Architectures
@@ -143,7 +143,7 @@ Sure, below are five different swarm architectures with their base requirements
 8. **Ant Colony Optimization (ACO) Swarm**: Inspired by ant behavior, this architecture has agents leave a pheromone trail that other agents follow, reinforcing the best paths. It's useful for problems like the traveling salesperson problem.
  - Requirements: Agents (can be language models), a representation of the problem space, a pheromone updating mechanism.
 
-9. **Genetic Algorithm (GA) Swarm**: In this architecture, agents represent potential solutions to a problem. They can 'breed' to create new solutions and can undergo 'mutations'. GA swarms are good for search and optimization problems.
+9. **Genetic Algorithm (GA) Swarm**: In this architecture, agents represent potential solutions to a problem. They can 'breed' to create new solutions and can undergo 'mutations'. GA zeta are good for search and optimization problems.
  - Requirements: Agents (each representing a potential solution), a fitness function to evaluate solutions, a crossover mechanism to breed solutions, and a mutation mechanism.
 
 10. **Stigmergy-based Swarm**: In this architecture, agents communicate indirectly by modifying the environment, and other agents react to such modifications. It's a decentralized method of coordinating tasks.

docs/examples/count-tokens.md

@@ -1,11 +1,11 @@
 To count tokens you can use Zeta events and the `TokenCounter` util:
 
 ```python
-from swarms import utils
-from swarms.events import (
+from zeta import utils
+from zeta.events import (
  StartPromptEvent, FinishPromptEvent,
 )
-from swarms.structures import Agent
+from zeta.structures import Agent
 
 
 token_counter = utils.TokenCounter()

docs/examples/index.md

@@ -1,3 +1,3 @@
 This section of the documentation is dedicated to examples highlighting Zeta functionality.
 
-We try to keep all examples up to date, but if you think there is a bug please [submit a pull request](https://github.com/kyegomez/swarms-docs/tree/main/docs/examples). We are also more than happy to include new examples :)
+We try to keep all examples up to date, but if you think there is a bug please [submit a pull request](https://github.com/kyegomez/zeta-docs/tree/main/docs/examples). We are also more than happy to include new examples :)

docs/examples/load-and-query-pinecone.md

@@ -3,7 +3,7 @@ import hashlib
 import json
 from urllib.request import urlopen
 from decouple import config
-from swarms.drivers import PineconeVectorStoreDriver
+from zeta.drivers import PineconeVectorStoreDriver
 
 
 def load_data(driver: PineconeVectorStoreDriver) -> None: