Metadata-Version: 2.1
Name: langdict
Version: 0.0.1rc1
Summary: The Simplest LLM Application Framework
Author-email: Dongjun Lee <djlee.hb@gmail.com>
Maintainer-email: Dongjun Lee <djlee.hb@gmail.com>
License: MIT License
        
        Copyright (c) 2024 LangDict
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
        
Project-URL: Homepage, https://langdict.github.io/
Project-URL: Documentation, https://langdict.github.io/docs
Project-URL: Repository, https://github.com/LangDict/langdict.git
Project-URL: Bug Tracker, https://github.com/LangDict/langdict/issues
Keywords: LLM,RAG,Agent,Compund AI Systems,LangDict
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3 :: Only
Requires-Python: <4.0,>=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Provides-Extra: dev
Provides-Extra: test
Requires-Dist: pytest; extra == "test"


<p align="center">
    <img src="images/logo.png" style="inline" width=600>
</p>

<h4 align="center">
    The Simplest LLM Application Framework
</h4>

---

# LangDict

LangDict is a framework for developing Compound AI System using only `Python dictionary`. This framework provides an intuitive usage for developing LLM Application for production.

Developing an LLM Application simply means adding API calls. Therefore, LangDict was created with the design philosophy that LLM Applications can be constructed from specifications, not complex functionality.

Create your own LLM Application with minimal understanding of other libraries and frameworks.

LangDict focuses on the intuitive interface, modularity, extensibility, and reusability of [PyTorch](https://github.com/pytorch/pytorch)'s `nn.Module`. Agent and Compound AI Systems can be easily developed from a composition of these modules.


## Features

<details>
  <summary>LLM Applicaiton framework for simple, intuitive, dictionary-based development</summary>

```python
chitchat = LangDict.from_dict({
    "messages": [
        ("system", "You are a helpful AI bot. Your name is {name}."),
        ("human", "Hello, how are you doing?"),
        ("ai", "I'm doing well, thanks!"),
        ("human", "{user_input}"),
    ],
    "llm": {
        "model": "gpt-4o-mini",
        "max_tokens": 200
    },
    "output": {
        "type": "string"
    }
})
# format placeholder is key of input dictionary
chitchat({
    "name": "LangDict",
    "user_input": "What is your name?"
})
```

</details>

<details>
  <summary>Stream / Batch / Async compatibility</summary>

```python
rag = RAG()

single_inputs = {
    "conversation": [{"role": "user", "content": "How old is Obama?"}]
}
# invoke
rag(single_inputs)

# stream
rag(single_inputs, stream=True)

# batch
batch_inputs = [{ ...  }, { ...}, ...]
rag(batch_inputs, batch=True)
```

</details>

<details>
  <summary>Modularity: Extensibility, Modifiability, Reusability</summary>

```python
class RAG(Module):

    def __init__(self, docs: List[str]):
        super().__init__()
        self.query_rewrite = LangDictModule.from_dict({ ... })  # Module
        self.search = SimpleKeywordSearch(docs=docs)  # Module
        self.answer = LangDictModule.from_dict({ ... })  # Module

    def forward(self, inputs: Dict):
        query_rewrite_result = self.query_rewrite({
            "conversation": inputs["conversation"],
        })
        doc = self.search(query_rewrite_result)
        return self.answer({
            "conversation": inputs["conversation"],
            "context": doc,
        })
```

</details>

<details>
  <summary>Easy to change trace options (Console, Langfuse)</summary>

```python
# Apply Trace option to all modules
rag = RAG()

# Console Trace
rag.trace(backend="console")

# Langfuse
rag.trace(backend="langfuse")
```

</details>


## Quick Start

Install LangDict:

```python
$ pip install langdict
```

### Example

Chitchat (`LangDict`)
- Create LLM functions based on your specification.

```python
from langdict import LangDict


chitchat_spec = {
    "messages": [
        ("system", "You are a helpful AI bot. Your name is {name}."),
        ("human", "Hello, how are you doing?"),
        ("ai", "I'm doing well, thanks!"),
        ("human", "{user_input}"),
    ],
    "llm": {
        "model": "gpt-4o-mini",
        "max_tokens": 200
    },
    "output": {
        "type": "string"
    }
}
chitchat = LangDict.from_dict(chitchat_spec)
chitchat({
    "name": "LangDict",
    "user_input": "What is your name?"
})
>>> 'My name is LangDict. How can I assist you today?'
```

`Module` (+ `LangDictModule`, `stream`, `trace`)
- Develop Compound AI System based on modules and get observability with a single line of code.

```python
from typing import Any, Dict, List

from langdict import Module, LangDictModule


class RAG(Module):

    def __init__(self, docs: List[str]):
        super().__init__()
        self.query_rewrite = LangDictModule.from_dict(query_rewrite_spec)
        self.search = SimpleRetriever(docs=docs)  # Module
        self.answer = LangDictModule.from_dict(answer_spec)

    def forward(self, inputs: Dict[str, Any]):
        query_rewrite_result = self.query_rewrite({
            "conversation": inputs["conversation"],
        })
        doc = self.search(query_rewrite_result)
        return self.answer({
            "conversation": inputs["conversation"],
            "context": doc,
        })

rag = RAG()
inputs = {
    "conversation": [{"role": "user", "content": "How old is Obama?"}]
}

rag(inputs)
>>> 'Barack Obama was born on August 4, 1961. As of now, in October 2023, he is 62 years old.'

# Stream
for token in rag(inputs, stream=True):
    print(f"token > {token}")
>>>
token > Bar
token > ack
token >  Obama
token >  was
token >  born
token >  on
token >  August
token >  
token > 4
...

# Trace
rag.trace(backend="langfuse")
```

## Dependencies

LangDict requires the following:

- [`LangChain`](https://github.com/langchain-ai/langchain) - LangDict consists of PromptTemplate + LLM + Output Parser.
    - langchain
    - langchain-core
- [`LiteLLM`](https://github.com/BerriAI/litellm) - Call 100+ LLM APIs in OpenAI format.

### Optional

- [`Langfuse`](https://github.com/langfuse/langfuse) - If you use langfuse with the Trace option, you need to install it separately.
