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0.0.9

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ai-enterprise-agent

AI Agent simplifies the implementation and use of generative AI with LangChain.

agentaiai-agentassistantenterprise
Published30 Apr 2024
Direct dependencies91
Transitive dependenciesNot calculated

README

Version 0.0.9 documentation

Publish new version to NPM

AI Agent

AI Agent simplifies the implementation and use of generative AI with LangChain, was inspired by the project autogen

Installation

Use the package manager pip to install AI Agent.

pip install ai_enterprise_agent

Usage

Simple use

  import asyncio

  from ai_enterprise_agent.agent import Agent
  from ai_enterprise_agent.interface.settings import (CHAIN_TYPE, DATABASE_TYPE, DIALECT_TYPE,
                                  LLM_TYPE, PROCESSING_TYPE, VECTOR_STORE_TYPE)
  agent = Agent({
    'processing_type': PROCESSING_TYPE.single,
    'chains': [CHAIN_TYPE.simple_chain],
    'model': {
      "type": LLM_TYPE.azure,
      "api_key": <api_key>,
      "model": <model>,
      "endpoint": <endpoint>,
      "api_version": <api_version>,
      "temperature": 0.0
    },
    "system": {
      "system_message": ""
    },
  })

  response = asyncio.run(
    agent._call(
      input={
        "question": "Who's Leonardo Da Vinci?.",
        "chat_thread_id": "<chat_thread_id>"
      }
    )
  )
  print(response)

Using with Orchestrator Mode

When using LLM with Orchestrator Mode the Agent finds the best way to answer the question in your base knowledge.


  agent = Agent({
    'processing_type': PROCESSING_TYPE.orchestrator,
    'chains': [CHAIN_TYPE.simple_chain, CHAIN_TYPE.sql_chain],
    'model': {
      "type": LLM_TYPE.azure,
      "api_key": <api_key>,
      "model": <model>,
      "endpoint": <endpoint>,
      "api_version": <api_version>,
      "temperature": 0.0
    },
     "database": {
      "type": DIALECT_TYPE.postgres,
      "host": <host>,
      "port": <port>,
      "username": <username>,
      "password": <password>,
      "database": <database>,
      "includes_tables": ['table-1', 'table-2'],
    },
    "system": {
      "system_message": ""
    },
  })

  response = asyncio.run(
    agent._call(
      input={
        "question": "How many employees there?",
        "chat_thread_id": "<chat_thread_id>"
      }
    )
  )
  print(response)

Contributing

If you've ever wanted to contribute to open source, and a great cause, now is your chance!

See the contributing docs for more information

Contributors ✨


JP. Nobrega
💬 📖 👀 📢
Túlio César Gaio
💬 📖 👀 📢

License

Apache-2.0

Downloads & popularity

Awaiting observations

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Declared dependencies 91

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ai_enterprise_agent-0.0.9-py3-none-any.whl

bdist_wheel · 35.51 KB · Python py3

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ai_enterprise_agent-0.0.9.tar.gz

sdist · 23.2 KB · Python source

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