[](https://github.com/dev-jpnobrega/ai-agent/actions/workflows/npm-publish.yml)
# AI Agent
AI Agent simplifies the implementation and use of generative AI with LangChain, was inspired by the project [autogen](https://github.com/microsoft/autogen)
## Installation
Use the package manager [pip](https://pypi.org/project/pip/) to install AI Agent.
```bash
pip install ai_enterprise_agent
```
## Usage
### Simple use
```python
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.
```python
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](CONTRIBUTING.md) for more information
## Contributors ✨
<!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section -->
<!-- prettier-ignore-start -->
<!-- markdownlint-disable -->
<table>
<tr>
<td align="center">
<a href="https://github.com/dev-jpnobrega">
<img src="https://avatars1.githubusercontent.com/u/28389807?s=400&u=2c152fc946efc96badce0cfc743ebcb2585b4b3f&v=4" width="100px;" alt=""/>
<br />
<sub>
<b>JP. Nobrega</b>
</sub>
</a>
<br />
<a href="https://github.com/dev-jpnobrega/ai-agent-py/issues" title="Answering Questions">💬</a>
<a href="https://github.com/dev-jpnobrega/ai-agent-py/master#how-do-i-use" title="Documentation">📖</a>
<a href="https://github.com/dev-jpnobrega/ai-agent-py/pulls" title="Reviewed Pull Requests">👀</a>
<a href="#talk-kentcdodds" title="Talks">📢</a>
</td>
<td align="center">
<a href="https://github.com/tuliogaio">
<img src="https://github.com/tuliogaio.png" width="100px;" alt=""/>
<br />
<sub>
<b>Túlio César Gaio</b>
</sub>
</a>
<br />
<a href="https://github.com/dev-jpnobrega/ai-agent-py/issues" title="Answering Questions">💬</a>
<a href="https://github.com/dev-jpnobrega/ai-agent-py/master#how-do-i-use" title="Documentation">📖</a>
<a href="https://github.com/dev-jpnobrega/ai-agent-py/pulls" title="Reviewed Pull Requests">👀</a>
<a href="#talk-kentcdodds" title="Talks">📢</a>
</td>
</tr>
</table>
<!-- markdownlint-enable -->
<!-- prettier-ignore-end -->
<!-- ALL-CONTRIBUTORS-LIST:END -->
## License
[Apache-2.0](LICENSE)
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"description": "\n[](https://github.com/dev-jpnobrega/ai-agent/actions/workflows/npm-publish.yml)\n\n# AI Agent\n\nAI Agent simplifies the implementation and use of generative AI with LangChain, was inspired by the project [autogen](https://github.com/microsoft/autogen)\n\n\n\n## Installation\n\nUse the package manager [pip](https://pypi.org/project/pip/) to install AI Agent.\n\n```bash\npip install ai_enterprise_agent\n```\n\n## Usage\n\n### Simple use\n```python\n import asyncio\n\n from ai_enterprise_agent.agent import Agent\n from ai_enterprise_agent.interface.settings import (CHAIN_TYPE, DATABASE_TYPE, DIALECT_TYPE,\n LLM_TYPE, PROCESSING_TYPE, VECTOR_STORE_TYPE)\n agent = Agent({\n 'processing_type': PROCESSING_TYPE.single,\n 'chains': [CHAIN_TYPE.simple_chain],\n 'model': {\n \"type\": LLM_TYPE.azure,\n \"api_key\": <api_key>,\n \"model\": <model>,\n \"endpoint\": <endpoint>,\n \"api_version\": <api_version>,\n \"temperature\": 0.0\n },\n \"system\": {\n \"system_message\": \"\"\n },\n })\n\n response = asyncio.run(\n agent._call(\n input={\n \"question\": \"Who's Leonardo Da Vinci?.\",\n \"chat_thread_id\": \"<chat_thread_id>\"\n }\n )\n )\n print(response)\n```\n\n### Using with Orchestrator Mode\nWhen using LLM with Orchestrator Mode the Agent finds the best way to answer the question in your base knowledge.\n```python\n\n agent = Agent({\n 'processing_type': PROCESSING_TYPE.orchestrator,\n 'chains': [CHAIN_TYPE.simple_chain, CHAIN_TYPE.sql_chain],\n 'model': {\n \"type\": LLM_TYPE.azure,\n \"api_key\": <api_key>,\n \"model\": <model>,\n \"endpoint\": <endpoint>,\n \"api_version\": <api_version>,\n \"temperature\": 0.0\n },\n \"database\": {\n \"type\": DIALECT_TYPE.postgres,\n \"host\": <host>,\n \"port\": <port>,\n \"username\": <username>,\n \"password\": <password>,\n \"database\": <database>,\n \"includes_tables\": ['table-1', 'table-2'],\n },\n \"system\": {\n \"system_message\": \"\"\n },\n })\n\n response = asyncio.run(\n agent._call(\n input={\n \"question\": \"How many employees there?\",\n \"chat_thread_id\": \"<chat_thread_id>\"\n }\n )\n )\n print(response)\n```\n\n## Contributing\n\nIf you've ever wanted to contribute to open source, and a great cause, now is your chance!\n\nSee the [contributing docs](CONTRIBUTING.md) for more information\n\n## Contributors \u2728\n\n<!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section -->\n<!-- prettier-ignore-start -->\n<!-- markdownlint-disable -->\n\n<table>\n <tr>\n <td align=\"center\">\n <a href=\"https://github.com/dev-jpnobrega\">\n <img src=\"https://avatars1.githubusercontent.com/u/28389807?s=400&u=2c152fc946efc96badce0cfc743ebcb2585b4b3f&v=4\" width=\"100px;\" alt=\"\"/>\n <br />\n <sub>\n <b>JP. Nobrega</b>\n </sub>\n </a>\n <br />\n <a href=\"https://github.com/dev-jpnobrega/ai-agent-py/issues\" title=\"Answering Questions\">\ud83d\udcac</a>\n <a href=\"https://github.com/dev-jpnobrega/ai-agent-py/master#how-do-i-use\" title=\"Documentation\">\ud83d\udcd6</a>\n <a href=\"https://github.com/dev-jpnobrega/ai-agent-py/pulls\" title=\"Reviewed Pull Requests\">\ud83d\udc40</a>\n <a href=\"#talk-kentcdodds\" title=\"Talks\">\ud83d\udce2</a>\n </td>\n <td align=\"center\">\n <a href=\"https://github.com/tuliogaio\">\n <img src=\"https://github.com/tuliogaio.png\" width=\"100px;\" alt=\"\"/>\n <br />\n <sub>\n <b>T\u00falio C\u00e9sar Gaio</b>\n </sub>\n </a>\n <br />\n <a href=\"https://github.com/dev-jpnobrega/ai-agent-py/issues\" title=\"Answering Questions\">\ud83d\udcac</a>\n <a href=\"https://github.com/dev-jpnobrega/ai-agent-py/master#how-do-i-use\" title=\"Documentation\">\ud83d\udcd6</a>\n <a href=\"https://github.com/dev-jpnobrega/ai-agent-py/pulls\" title=\"Reviewed Pull Requests\">\ud83d\udc40</a>\n <a href=\"#talk-kentcdodds\" title=\"Talks\">\ud83d\udce2</a>\n </td>\n </tr>\n</table>\n\n<!-- markdownlint-enable -->\n<!-- prettier-ignore-end -->\n<!-- ALL-CONTRIBUTORS-LIST:END -->\n\n## License\n[Apache-2.0](LICENSE)\n",
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