From ac9754a6aedd3e0420402f661e4e7ed647d0090d Mon Sep 17 00:00:00 2001 From: Kye Date: Tue, 22 Aug 2023 13:53:31 -0400 Subject: [PATCH] swarms cleanup --- .github/PULL_REQUEST_TEMPLATE.yml | 2 +- .github/workflows/pull-request-links.yml | 2 +- .github/workflows/unit-test.yml | 4 +- README.md | 4 +- docs/applications/customer_support.md | 8 ++-- docs/applications/marketing_agencies.md | 6 +-- docs/architecture.md | 24 ++++++------ docs/bounties.md | 2 +- docs/contributing.md | 8 ++-- docs/design.md | 18 ++++----- docs/examples/count-tokens.md | 6 +-- docs/examples/index.md | 2 +- docs/examples/load-and-query-pinecone.md | 2 +- docs/examples/load-query-and-chat-marqo.md | 16 ++++---- docs/examples/query-webpage.md | 12 +++--- .../store-conversation-memory-in-dynamodb.md | 6 +-- docs/examples/talk-to-a-pdf.md | 10 ++--- docs/examples/talk-to-a-webpage.md | 12 +++--- docs/examples/talk-to-redshift.md | 12 +++--- docs/examples/using-text-generation-web-ui.md | 14 +++---- docs/faq.md | 2 +- docs/index.md | 4 +- docs/overrides/main.html | 2 +- docs/zeta/index.md | 38 +++++++++---------- mkdocs.yml | 8 ++-- 25 files changed, 112 insertions(+), 112 deletions(-) diff --git a/.github/PULL_REQUEST_TEMPLATE.yml b/.github/PULL_REQUEST_TEMPLATE.yml index 20eb0410..da560b8b 100644 --- a/.github/PULL_REQUEST_TEMPLATE.yml +++ b/.github/PULL_REQUEST_TEMPLATE.yml @@ -22,4 +22,4 @@ Maintainer responsibilities: If no one reviews your PR within a few days, feel free to kye@apac.ai -See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/kyegomez/swarms \ No newline at end of file +See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/kyegomez/zeta \ No newline at end of file diff --git a/.github/workflows/pull-request-links.yml b/.github/workflows/pull-request-links.yml index 4cb674d8..208fad31 100644 --- a/.github/workflows/pull-request-links.yml +++ b/.github/workflows/pull-request-links.yml @@ -15,4 +15,4 @@ jobs: steps: - uses: readthedocs/actions/preview@v1 with: - project-slug: swarms \ No newline at end of file + project-slug: zeta \ No newline at end of file diff --git a/.github/workflows/unit-test.yml b/.github/workflows/unit-test.yml index 994b0c98..cf4f1be8 100644 --- a/.github/workflows/unit-test.yml +++ b/.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 diff --git a/README.md b/README.md index ff5ff8e5..58a0bfe4 100644 --- a/README.md +++ b/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).

MIT License diff --git a/docs/applications/customer_support.md b/docs/applications/customer_support.md index 76114241..a5a62f70 100644 --- a/docs/applications/customer_support.md +++ b/docs/applications/customer_support.md @@ -3,7 +3,7 @@ --- **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. --- @@ -11,7 +11,7 @@ In today's fast-paced digital world, responsive and efficient customer support i 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.** diff --git a/docs/applications/marketing_agencies.md b/docs/applications/marketing_agencies.md index 94e12fbe..f38614bc 100644 --- a/docs/applications/marketing_agencies.md +++ b/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. --- \ No newline at end of file diff --git a/docs/architecture.md b/docs/architecture.md index a9cff880..5f82b283 100644 --- a/docs/architecture.md +++ b/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. --- diff --git a/docs/bounties.md b/docs/bounties.md index e43cd9f5..18161e50 100644 --- a/docs/bounties.md +++ b/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! diff --git a/docs/contributing.md b/docs/contributing.md index 66d50d31..627162ca 100644 --- a/docs/contributing.md +++ b/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. diff --git a/docs/design.md b/docs/design.md index 6567f8e6..c5be4b8d 100644 --- a/docs/design.md +++ b/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. diff --git a/docs/examples/count-tokens.md b/docs/examples/count-tokens.md index b85bfbe3..2ad237ad 100644 --- a/docs/examples/count-tokens.md +++ b/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() diff --git a/docs/examples/index.md b/docs/examples/index.md index eeac58c2..4ed46a1e 100644 --- a/docs/examples/index.md +++ b/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 :) \ No newline at end of file +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 :) \ No newline at end of file diff --git a/docs/examples/load-and-query-pinecone.md b/docs/examples/load-and-query-pinecone.md index b27afa36..18f7cd71 100644 --- a/docs/examples/load-and-query-pinecone.md +++ b/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: diff --git a/docs/examples/load-query-and-chat-marqo.md b/docs/examples/load-query-and-chat-marqo.md index 2b621afd..edaa5076 100644 --- a/docs/examples/load-query-and-chat-marqo.md +++ b/docs/examples/load-query-and-chat-marqo.md @@ -1,10 +1,10 @@ ```python -from swarms import utils -from swarms.drivers import MarqoVectorStoreDriver -from swarms.engines import VectorQueryEngine -from swarms.loaders import WebLoader -from swarms.structures import Agent -from swarms.tools import KnowledgeBaseClient +from zeta import utils +from zeta.drivers import MarqoVectorStoreDriver +from zeta.engines import VectorQueryEngine +from zeta.loaders import WebLoader +from zeta.structures import Agent +from zeta.tools import KnowledgeBaseClient import openai from marqo import Client @@ -31,13 +31,13 @@ query_engine = VectorQueryEngine(vector_store_driver=vector_store) # Initialize the knowledge base tool kb_tool = KnowledgeBaseClient( - description="Contains information about the Zeta Framework from www.swarms.ai", + description="Contains information about the Zeta Framework from www.zeta.ai", query_engine=query_engine, namespace=namespace ) # Load artifacts from the web -artifacts = WebLoader(max_tokens=200).load("https://www.swarms.ai") +artifacts = WebLoader(max_tokens=200).load("https://www.zeta.ai") # Upsert the artifacts into the vector store vector_store.upsert_text_artifacts({namespace: artifacts,}) diff --git a/docs/examples/query-webpage.md b/docs/examples/query-webpage.md index 0ca76747..0171f02e 100644 --- a/docs/examples/query-webpage.md +++ b/docs/examples/query-webpage.md @@ -1,20 +1,20 @@ ```python -from swarms.artifacts import BaseArtifact -from swarms.drivers import LocalVectorStoreDriver -from swarms.loaders import WebLoader +from zeta.artifacts import BaseArtifact +from zeta.drivers import LocalVectorStoreDriver +from zeta.loaders import WebLoader vector_store = LocalVectorStoreDriver() [ - vector_store.upsert_text_artifact(a, namespace="swarms") - for a in WebLoader(max_tokens=100).load("https://www.swarms.ai") + vector_store.upsert_text_artifact(a, namespace="zeta") + for a in WebLoader(max_tokens=100).load("https://www.zeta.ai") ] results = vector_store.query( "creativity", count=3, - namespace="swarms" + namespace="zeta" ) values = [BaseArtifact.from_json(r.meta["artifact"]).value for r in results] diff --git a/docs/examples/store-conversation-memory-in-dynamodb.md b/docs/examples/store-conversation-memory-in-dynamodb.md index cbe74dcc..bb3be374 100644 --- a/docs/examples/store-conversation-memory-in-dynamodb.md +++ b/docs/examples/store-conversation-memory-in-dynamodb.md @@ -1,8 +1,8 @@ To store your conversation on DynamoDB you can use DynamoDbConversationMemoryDriver. ```python -from swarms.memory.structure import ConversationMemory -from swarms.memory.structure import ConversationMemoryElement, Turn, Message -from swarms.drivers import DynamoDbConversationMemoryDriver +from zeta.memory.structure import ConversationMemory +from zeta.memory.structure import ConversationMemoryElement, Turn, Message +from zeta.drivers import DynamoDbConversationMemoryDriver # Instantiate DynamoDbConversationMemoryDriver dynamo_driver = DynamoDbConversationMemoryDriver( diff --git a/docs/examples/talk-to-a-pdf.md b/docs/examples/talk-to-a-pdf.md index 13535dae..bf74062d 100644 --- a/docs/examples/talk-to-a-pdf.md +++ b/docs/examples/talk-to-a-pdf.md @@ -3,11 +3,11 @@ This example demonstrates how to vectorize a PDF of the [Attention Is All You Ne ```python import io import requests -from swarms.engines import VectorQueryEngine -from swarms.loaders import PdfLoader -from swarms.structures import Agent -from swarms.tools import KnowledgeBaseClient -from swarms.utils import Chat +from zeta.engines import VectorQueryEngine +from zeta.loaders import PdfLoader +from zeta.structures import Agent +from zeta.tools import KnowledgeBaseClient +from zeta.utils import Chat namespace = "attention" diff --git a/docs/examples/talk-to-a-webpage.md b/docs/examples/talk-to-a-webpage.md index 23b4e033..229531a4 100644 --- a/docs/examples/talk-to-a-webpage.md +++ b/docs/examples/talk-to-a-webpage.md @@ -1,12 +1,12 @@ This example demonstrates how to vectorize a webpage and setup a Zeta agent with rules and the `KnowledgeBase` tool to use it during conversations. ```python -from swarms.engines import VectorQueryEngine -from swarms.loaders import WebLoader -from swarms.rules import Ruleset, Rule -from swarms.structures import Agent -from swarms.tools import KnowledgeBaseClient -from swarms.utils import Chat +from zeta.engines import VectorQueryEngine +from zeta.loaders import WebLoader +from zeta.rules import Ruleset, Rule +from zeta.structures import Agent +from zeta.tools import KnowledgeBaseClient +from zeta.utils import Chat namespace = "physics-wiki" diff --git a/docs/examples/talk-to-redshift.md b/docs/examples/talk-to-redshift.md index 245c71ae..fc4fe4d6 100644 --- a/docs/examples/talk-to-redshift.md +++ b/docs/examples/talk-to-redshift.md @@ -4,12 +4,12 @@ Let's build a support agent that uses GPT-4: ```python import boto3 -from swarms.drivers import AmazonRedshiftSqlDriver, OpenAiPromptDriver -from swarms.loaders import SqlLoader -from swarms.rules import Ruleset, Rule -from swarms.structures import Agent -from swarms.tools import SqlClient, FileManager -from swarms.utils import Chat +from zeta.drivers import AmazonRedshiftSqlDriver, OpenAiPromptDriver +from zeta.loaders import SqlLoader +from zeta.rules import Ruleset, Rule +from zeta.structures import Agent +from zeta.tools import SqlClient, FileManager +from zeta.utils import Chat session = boto3.Session(region_name="REGION_NAME") diff --git a/docs/examples/using-text-generation-web-ui.md b/docs/examples/using-text-generation-web-ui.md index 4ed3ace0..ed74bbb1 100644 --- a/docs/examples/using-text-generation-web-ui.md +++ b/docs/examples/using-text-generation-web-ui.md @@ -19,15 +19,15 @@ Code snippet using a pre defined 'preset'. 'max_tokens' argument here need to be set with the same value as in the preset in text gen. ```shell -from swarms.structures import Agent -from swarms.drivers import TextGenPromptDriver -from swarms.tokenizers import TextGenTokenizer +from zeta.structures import Agent +from zeta.drivers import TextGenPromptDriver +from zeta.tokenizers import TextGenTokenizer from transformers import PreTrainedTokenizerFast fast_tokenizer = PreTrainedTokenizerFast(tokenizer_file="tokenizer.json") prompt_driver = TextGenPromptDriver( - preset="swarms", + preset="zeta", tokenizer=TextGenTokenizer(max_tokens=300, tokenizer=fast_tokenizer) ) @@ -45,9 +45,9 @@ Code snippet example using params, if params and preset is defined, preset will this params are overriding the current preset set in text gen, not all of them must be used. ```shell -from swarms.structures import Agent -from swarms.drivers import TextGenPromptDriver -from swarms.tokenizers import TextGenTokenizer +from zeta.structures import Agent +from zeta.drivers import TextGenPromptDriver +from zeta.tokenizers import TextGenTokenizer from transformers import PreTrainedTokenizerFast params = { diff --git a/docs/faq.md b/docs/faq.md index 716095f6..64adefbf 100644 --- a/docs/faq.md +++ b/docs/faq.md @@ -1,4 +1,4 @@ -This page summarizes questions we were asked on [Discord](https://discord.gg/gnWRz88eym), Hacker News, and Reddit. Feel free to post a question to [Discord](https://discord.gg/gnWRz88eym) or open a discussion on our [Github Page](https://github.com/kyegomez) or hit us up directly: [kye@apac.ai](mailto:hello@swarms.ai). +This page summarizes questions we were asked on [Discord](https://discord.gg/gnWRz88eym), Hacker News, and Reddit. Feel free to post a question to [Discord](https://discord.gg/gnWRz88eym) or open a discussion on our [Github Page](https://github.com/kyegomez) or hit us up directly: [kye@apac.ai](mailto:hello@zeta.ai). ## 1. How is Zeta different from LangChain? diff --git a/docs/index.md b/docs/index.md index d9abc5e9..76b43579 100644 --- a/docs/index.md +++ b/docs/index.md @@ -4,14 +4,14 @@ Welcome to Zeta's Documentation! Zeta is a modular framework that enables for seamless, reliable, and fluid creation of zetascale AI models. -Zeta is transforming the landscape of AI from siloed AI agents to a unified 'swarm' of intelligence. Through relentless iteration and the power of collective insight from our 1500+ Agora researchers, we're developing a groundbreaking framework for AI collaboration. Our mission is to catalyze a paradigm shift, advancing Humanity with the power of unified autonomous AI agent swarms. +Zeta is transforming the landscape of AI from siloed AI agents to a unified 'swarm' of intelligence. Through relentless iteration and the power of collective insight from our 1500+ Agora researchers, we're developing a groundbreaking framework for AI collaboration. Our mission is to catalyze a paradigm shift, advancing Humanity with the power of unified autonomous AI agent zeta. This documentation covers the fundamentals of the **Zeta** framework and describes how to use **Zeta Tools**. ## Zeta -The Zeta framework provides developers with the ability to create AI systems that operate across two dimensions: predictability and creativity. For predictability, Zeta enforces structures like sequential pipelines, DAG-based workflows, and long-term memory. To facilitate creativity, Zeta safely prompts LLMs with [tools](https://github.com/kyegomez/swarms-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. +The Zeta framework provides developers with the ability to create AI systems that operate across two dimensions: predictability and creativity. For predictability, Zeta enforces structures like sequential pipelines, DAG-based workflows, and long-term memory. To facilitate creativity, Zeta safely prompts LLMs with [tools](https://github.com/kyegomez/zeta-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. Zeta not only helps developers harness the potential of LLMs but also enforces trust boundaries, schema validation, and tool activity-level permissions. By doing so, Zeta maximizes LLMs’ reasoning while adhering to strict policies regarding their capabilities. diff --git a/docs/overrides/main.html b/docs/overrides/main.html index e42c0431..8dbe669d 100644 --- a/docs/overrides/main.html +++ b/docs/overrides/main.html @@ -4,6 +4,6 @@ {% block announce %}

- Star and contribute to Zeta on GitHub! + Star and contribute to Zeta on GitHub!
{% endblock %} \ No newline at end of file diff --git a/docs/zeta/index.md b/docs/zeta/index.md index 098a0485..3f6a7c42 100644 --- a/docs/zeta/index.md +++ b/docs/zeta/index.md @@ -17,10 +17,10 @@ First, configure an OpenAI client by [getting an API key](https://beta.openai.co ### Using pip -Install **swarms** and **swarms-tools**: +Install **zeta** and **zeta-tools**: ``` -pip3 install swarms +pip3 install zeta ``` ### Using Poetry @@ -28,20 +28,20 @@ pip3 install swarms To get started with Zeta using Poetry first create a new poetry project from the terminal: ``` -poetry new swarms-quickstart +poetry new zeta-quickstart ``` -Change your working directory to the new `swarms-quickstart` directory created by Poetry and add the dependencies. +Change your working directory to the new `zeta-quickstart` directory created by Poetry and add the dependencies. ``` -poetry add swarms -poetry add swarms-tools +poetry add zeta +poetry add zeta-tools ``` ## Build a Simple Agent With Zeta, you can create *structures*, such as `Agents`, `Pipelines`, and `Workflows`, that are composed of different types of tasks. First, let's build a simple Agent that we can interact with through a chat based interface. ```python -from swarms import Worker +from zeta import Worker node = Worker( @@ -55,10 +55,10 @@ print(response) ``` Run this script in your IDE and you'll be presented with a `Q:` prompt where you can interact with your model. ``` -Q: write me a haiku about swarms +Q: write me a haiku about zeta processing... [06/28/23 10:31:34] INFO Task de1da665296c4a3799a0f280aff59610 - Input: write me a haiku about swarms + Input: write me a haiku about zeta [06/28/23 10:31:37] INFO Task de1da665296c4a3799a0f280aff59610 Output: Zeta on my board, Keeps me steady, never slips, @@ -71,14 +71,14 @@ Q: If you want to skip the chat interface and load an initial prompt, you can do so using the `.run()` method: ```python -node.run("write me a haiku about swarms") +node.run("write me a haiku about zeta") ``` Agents on their own are fun, but let's add some capabilities to them using Zeta Tools. ### Build a Simple Agent with Tools ```python -from swarms.structures import Agent -from swarms.tools import Calculator +from zeta.structures import Agent +from zeta.tools import Calculator calculator = Calculator() @@ -116,10 +116,10 @@ Here is the chain of thought from the Agent. Notice where it realizes it can use Let's define a simple two-task pipeline that uses tools and memory: ```python -from swarms.memory.structure import ConversationMemory -from swarms.structures import Pipeline -from swarms.tasks import ToolkitTask, PromptTask -from swarms.tools import WebScraper, FileManager +from zeta.memory.structure import ConversationMemory +from zeta.structures import Pipeline +from zeta.tasks import ToolkitTask, PromptTask +from zeta.tools import WebScraper, FileManager # Pipelines represent sequences of tasks. @@ -141,11 +141,11 @@ pipeline.add_tasks( ) pipeline.run( - "Load https://www.apac.ai, summarize it, and store it in swarms.txt" + "Load https://www.apac.ai, summarize it, and store it in zeta.txt" ) ``` Boom! Our first LLM pipeline with two sequential tasks generated the following exchange: -> Q: Load https://swarms.readthedocs.io, summarize it, and store it in swarms.txt -> A: El contenido de https://swarms.readthedocs.io ha sido resumido y almacenado en swarms.txt. +> Q: Load https://zeta.readthedocs.io, summarize it, and store it in zeta.txt +> A: El contenido de https://zeta.readthedocs.io ha sido resumido y almacenado en zeta.txt. diff --git a/mkdocs.yml b/mkdocs.yml index c22892b7..2c0f7356 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -75,13 +75,13 @@ nav: - Checklist: "checklist.md" - Hiring: "hiring.md" - Zeta: - - Overview: "swarms/index.md" - - AutoScaler: "swarms/swarms/autoscaler.md" + - Overview: "zeta/index.md" + - AutoScaler: "zeta/zeta/autoscaler.md" - Workers: - - Overview: "swarms/workers/index.md" + - Overview: "zeta/workers/index.md" - Agents: - Base Models: - - Overview: "swarms/models/index.md" + - Overview: "zeta/models/index.md" - Examples: - Overview: "examples/index.md" - Agents: