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