| Name | mtllm JSON |
| Version |
0.3.3
JSON |
| download |
| home_page | None |
| Summary | MTLLM Provides Easy to use APIs for different LLM Providers to be used with Jaseci's Jaclang Programming Language. |
| upload_time | 2024-11-07 16:29:12 |
| maintainer | Jason Mars |
| docs_url | None |
| author | Jason Mars |
| requires_python | None |
| license | MIT |
| keywords |
llm
jaclang
jaseci
mtllm
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
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# MTLLM API library
[](https://pypi.org/project/mtllm/) [](https://github.com/Jaseci-Labs/mtllm/actions/workflows/test.yml)
The MTLLM Python library provides convenient access to a large number of easy to use and customizable APIs to be used in Jaseci's [Jaclang](https://github.com/Jaseci-Labs/jaclang) by llm feature.
The Library provides automatic output fixing, output type validation, different prompting techniques, and more.
## Documentation
The documentation on how to use this library with Jaseci's Jaclang can be found [here](https://jaseci-labs.github.io/mtllm/).
## Installation
> [!IMPORTANT]
> Though this is can be used with python projects, it is primarily intended to be used with Jaseci's Jaclang.
```sh
# install from PyPI
pip install mtllm
```
## Usage
Refer the Documentation for detailed usage instructions.
### Using Different LLMs
```py
import:py from mtllm.llms, OpenAI;
glob llm = OpenAI();
can "Translate English to French"
translate(word: "English Word": str) -> "French Word": str by llm();
```
Based on your LLM of choice, make sure to set the `API Key` in the environment variable. For example, for OpenAI:
```sh
export OPENAI_API_KEY="your-api-key"
```
### Using Tools
```py
import:py from mtllm.llms, OpenAI;
import:py from mtllm.tools, wikipedia;
glob llm = OpenAI();
can "Answer History Questions"
history_qa(question: "History Question": str) -> "Detailed Answer": str by llm(tools=[wikipedia]);
```
## Contributing
Please read [CONTRIBUTING.md](CONTRIBUTING.md) for details on our code of conduct, and the process for submitting pull requests to us.
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"description": "# MTLLM API library\n\n[](https://pypi.org/project/mtllm/) [](https://github.com/Jaseci-Labs/mtllm/actions/workflows/test.yml)\n\nThe MTLLM Python library provides convenient access to a large number of easy to use and customizable APIs to be used in Jaseci's [Jaclang](https://github.com/Jaseci-Labs/jaclang) by llm feature.\nThe Library provides automatic output fixing, output type validation, different prompting techniques, and more.\n\n## Documentation\n\nThe documentation on how to use this library with Jaseci's Jaclang can be found [here](https://jaseci-labs.github.io/mtllm/).\n\n## Installation\n\n> [!IMPORTANT]\n> Though this is can be used with python projects, it is primarily intended to be used with Jaseci's Jaclang.\n\n```sh\n# install from PyPI\npip install mtllm\n```\n\n## Usage\n\nRefer the Documentation for detailed usage instructions.\n\n### Using Different LLMs\n```py\nimport:py from mtllm.llms, OpenAI;\n\nglob llm = OpenAI();\n\ncan \"Translate English to French\"\ntranslate(word: \"English Word\": str) -> \"French Word\": str by llm();\n```\n\nBased on your LLM of choice, make sure to set the `API Key` in the environment variable. For example, for OpenAI:\n\n```sh\nexport OPENAI_API_KEY=\"your-api-key\"\n```\n### Using Tools\n```py\nimport:py from mtllm.llms, OpenAI;\nimport:py from mtllm.tools, wikipedia;\n\nglob llm = OpenAI();\n\ncan \"Answer History Questions\"\nhistory_qa(question: \"History Question\": str) -> \"Detailed Answer\": str by llm(tools=[wikipedia]);\n```\n\n## Contributing\nPlease read [CONTRIBUTING.md](CONTRIBUTING.md) for details on our code of conduct, and the process for submitting pull requests to us.\n",
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