Skip to main content

A Python package for AI-MultiModal MultiAgents methods and tools.

Project description

EnsembleAI

EnsembleAI is a framework for building intelligent agents that can perform various tasks such as summarizing Wikipedia articles, analyzing YouTube transcripts, scraping web pages, and more.

Features

  • Wikipedia Summarizer
  • YouTube Transcript Analysis
  • Web Scraper
  • Image Analysis
  • Retrieval-Augmented Generation (RAG)

Installation

pip install ensembleai

Usage

from ensembleai import Agent, LLMModel, Environment, YouTubeTranscriptTool, WikipediaTool, ImageAnalysisTool, WebScrapingTool, RAGTool

# Initialize models and tools
model = LLMModel(name="test-model", api_key="your-api-key")
youtube_tool = YouTubeTranscriptTool(api_key="your-youtube-api-key", keyword="your-keyword")
wikipedia_tool = WikipediaTool(topic="your-topic")
image_tool = ImageAnalysisTool(text="your-text", url="your-image-url")
webscraper_tool = WebScrapingTool(url="your-url")
rag_tool = RAGTool(file_paths=["path/to/your/file.pdf"])

# Create agents
agent1 = Agent(name="Agent1", model_instance=model, role="role1", work="work1")
agent1.add_tool("youtube_transcript", youtube_tool)

agent2 = Agent(name="Agent2", model_instance=model, role="role2", work="work2")
agent2.add_tool("wikipedia", wikipedia_tool)

agent3 = Agent(name="Agent3", model_instance=model, role="role3", work="work3")
agent3.add_tool("image_analysis", image_tool)

agent4 = Agent(name="Agent4", model_instance=model, role="role4", work="work4")
agent4.add_tool("web_scraping", webscraper_tool)

agent5 = Agent(name="Agent5", model_instance=model, role="role5", work="work5")
agent5.add_tool("RAG", rag_tool)

# Define agent connections
agent1.give_to(agent2)
agent2.give_to(agent3)
agent3.give_to(agent4)
agent4.give_to(agent5)

# Create and start the environment
env = Environment(agents=[agent1, agent2, agent3, agent4, agent5])
env.start()

Contributing

Contributions are welcome! Please open an issue or submit a pull request.

License

This project is licensed under the MIT License.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

ensembleai-1.2.1.tar.gz (8.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

ensembleai-1.2.1-py3-none-any.whl (9.8 kB view details)

Uploaded Python 3

File details

Details for the file ensembleai-1.2.1.tar.gz.

File metadata

  • Download URL: ensembleai-1.2.1.tar.gz
  • Upload date:
  • Size: 8.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.12.2

File hashes

Hashes for ensembleai-1.2.1.tar.gz
Algorithm Hash digest
SHA256 8c4733252e36fe3b0da19c02fc184beefb224d8e278f105ef99b3b54ce291c32
MD5 83997ebe7988924f2ed6c4dc9dc72ef3
BLAKE2b-256 927b3ef87fd830c844db5d5654cca5e5669b07a2f2ba59f9e00fab3eea337160

See more details on using hashes here.

File details

Details for the file ensembleai-1.2.1-py3-none-any.whl.

File metadata

  • Download URL: ensembleai-1.2.1-py3-none-any.whl
  • Upload date:
  • Size: 9.8 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.12.2

File hashes

Hashes for ensembleai-1.2.1-py3-none-any.whl
Algorithm Hash digest
SHA256 d42fb89065d356c6a610979c42200d0186709daacfc4a311f45c2dddd5f4e376
MD5 d1d24e0de95ff38460790626b86a289a
BLAKE2b-256 51504624e21c69d56a7a2ef62ba28cc492ddce523b6fd47bcc2c7c4c839048dc

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page