![](LLMFY-LOGO.webp)

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# LLMFY

`LLMfy` is a flexible and developer-friendly framework designed to streamline the creation of applications powered by large language models (LLMs). It provides essential tools and abstractions that simplify the integration, orchestration, and management of LLMs across various use cases, enabling developers to focus on building intelligent, context-aware solutions without getting bogged down in low-level model handling. With support for modular components, prompt engineering, and extensibility, LLMfy accelerates the development of AI-driven applications from prototyping to production.

See complete documentation at [https://llmfy.readthedocs.io/](https://llmfy.readthedocs.io/)

## How to install

- Prerequisites:
  - Install [pydantic](https://pypi.org/project/pydantic) — ✅ required, 
  - Install [openai](https://pypi.org/project/openai) to use OpenAI models — 🔸 optional.
  - Install [boto3](https://pypi.org/project/boto3/) to use AWS Bedrock models — 🔸 optional.
  - Install [numpy](https://pypi.org/project/numpy/) to use Embedding, `FAISSVectorStore` — 🔸 optional.
  - Install [faiss-cpu](https://pypi.org/project/faiss-cpu/) to use `FAISSVectorStore` — 🔸 optional.
  - Install [typing_extensions](https://pypi.org/project/typing-extensions/) to use state in `FlowEngine` — 🔸 optional.
  - Install [redis](https://pypi.org/project/redis/) to use `RedisCheckpointer` — 🔸 optional.
  - Install [SQLAlchemy](https://pypi.org/project/SQLAlchemy/) to use `SQLCheckpointer` — 🔸 optional. `SQLCheckpointer` supports both sync and async drivers for multiple databases:
      - PostgreSQL (async: [asyncpg](https://pypi.org/project/asyncpg/), sync: [psycopg2](https://pypi.org/project/psycopg2/)) — 🔸 optional.
      - MySQL (async: [aiomysql](https://pypi.org/project/aiomysql/), sync: [pymysql](https://pypi.org/project/PyMySQL/)) — 🔸 optional.
      - SQLite (async: [aiosqlite](https://pypi.org/project/aiosqlite/), sync: built-in) — 🔸 optional.

### Using pip
```sh
pip install llmfy
```
### Using requirements.txt
- Add into requirements.txt
```txt
llmfy
```
- Then install
```txt
pip install -r requirements.txt
```

### Using github 

#### From a specific branch
```sh
# main
pip install git+https://github.com/irufano/llmfy.git@main

# dev
pip install git+https://github.com/irufano/llmfy.git@dev
```

#### From a tag
```sh
# example tag version 0.4.3
pip install git+https://github.com/irufano/llmfy.git@v0.4.3
```

#### Github in requirements.txt

```txt
git+https://github.com/irufano/llmfy.git@dev
```

## How to use
### OpenAI models
To use `OpenAIModel`, add below config to your env:
- `OPENAI_API_KEY`

### AWS Bedrock models
To use `BedrockModel`, add below config to your env:
- `AWS_ACCESS_KEY_ID` 
- `AWS_SECRET_ACCESS_KEY` 
- `AWS_BEDROCK_REGION`

## Example
### LLMfy Example
```python
from llmfy import (
    OpenAIModel,
    OpenAIConfig,
    LLMfy,
    Message,
    Role,
    LLMfyException,
)

def sample_prompt():
    info = """Irufano adalah seorang software engineer.
    Dia berasal dari Indonesia.
    Kamu bisa mengunjungi websitenya di https:://irufano.github.io"""

    # Configuration
    config = OpenAIConfig(temperature=0.7)
    llm = OpenAIModel(model="gpt-4o-mini", config=config)

    SYSTEM_PROMPT = """Answer any user questions based solely on the data below:
    <data>
    {info}
    </data>
    
    DO NOT response outside context."""

    # Initialize framework
    framework = LLMfy(llm, system_message=SYSTEM_PROMPT, input_variables=["info"])

    try:
        messages = [Message(role=Role.USER, content="apa ibukota china")]
       
        response = framework.invoke(messages, info=info)
        print(f"\n>> {response.result.content}\n")

    except LLMfyException as e:
        print(f"{e}")


if __name__ == "__main__":
    sample_prompt()
```

## Develop as Contributor

### Build package
```sh
uv build
```

### Trigger build and deploy to PyPI
```sh
# TAG_NAME must start with "v" (e.g., v1.0.0)
git tag -a [TAG_NAME] -m "[TAG_MESSAGE]"

# push tag to remote
git push origin [TAG_NAME]
```

### Mkdocs run on local
```sh
# Serve on local
mkdocs serve

# Build docs
mkdocs build
```