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 environments
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-1.2.2.tar.gz (8.0 kB view details)

Uploaded Source

Built Distribution

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

ensembleai-1.2.2-py3-none-any.whl (10.5 kB view details)

Uploaded Python 3

File details

Details for the file ensembleai-1.2.2.tar.gz.

File metadata

  • Download URL: ensembleai-1.2.2.tar.gz
  • Upload date:
  • Size: 8.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.12.2

File hashes

Hashes for ensembleai-1.2.2.tar.gz
Algorithm Hash digest
SHA256 ac211f39cbf5821b5b439094ff4a292042790c673c1aed07e25e754c5d4cc3cc
MD5 15d08d5605a905e3b7d95a3da5be153e
BLAKE2b-256 3355e71fafa3cbc1f0675367855bbf91b4fb36a8ded5785c658566f2e074f4a7

See more details on using hashes here.

File details

Details for the file ensembleai-1.2.2-py3-none-any.whl.

File metadata

  • Download URL: ensembleai-1.2.2-py3-none-any.whl
  • Upload date:
  • Size: 10.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.12.2

File hashes

Hashes for ensembleai-1.2.2-py3-none-any.whl
Algorithm Hash digest
SHA256 642c1c3cecc11b454a9d8457fcd269959085aaf404bcaaad28be1970e826f19d
MD5 c91c112b5215ddc8de25d600547de73e
BLAKE2b-256 cb28a501487279d61afe3006692a15c29bfc3c4f42014b7131a5f6abcdf2cf5b

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