README.md

# kotaemon

Quick and easy AI components to build Kotaemon - applicable in client
project.

## Install

```shell
pip install kotaemon@git+ssh://git@github.com/Cinnamon/kotaemon.git
```

## Contribute

### Setup

```shell
# Create conda environment (suggest 3.10)
conda create -n kotaemon python=3.10
conda activate kotaemon

# Install all
pip install -e ".[dev]"

# Test
pytest tests
```

### Code base structure

- documents: define document
- loaders
[AUR-385, AUR-388] Declare BaseComponent and decide LLM call interface (#2) - Use cases related to LLM call: https://cinnamon-ai.atlassian.net/browse/AUR-388?focusedCommentId=34873 - Sample usages: `test_llms_chat_models.py` and `test_llms_completion_models.py`: ```python from kotaemon.llms.chats.openai import AzureChatOpenAI model = AzureChatOpenAI( openai_api_base="https://test.openai.azure.com/", openai_api_key="some-key", openai_api_version="2023-03-15-preview", deployment_name="gpt35turbo", temperature=0, request_timeout=60, ) output = model("hello world") ``` For the LLM-call component, I decide to wrap around Langchain's LLM models and Langchain's Chat models. And set the interface as follow: - Completion LLM component: ```python class CompletionLLM: def run_raw(self, text: str) -> LLMInterface: # Run text completion: str in -> LLMInterface out def run_batch_raw(self, text: list[str]) -> list[LLMInterface]: # Run text completion in batch: list[str] in -> list[LLMInterface] out # run_document and run_batch_document just reuse run_raw and run_batch_raw, due to unclear use case ``` - Chat LLM component: ```python class ChatLLM: def run_raw(self, text: str) -> LLMInterface: # Run chat completion (no chat history): str in -> LLMInterface out def run_batch_raw(self, text: list[str]) -> list[LLMInterface]: # Run chat completion in batch mode (no chat history): list[str] in -> list[LLMInterface] out def run_document(self, text: list[BaseMessage]) -> LLMInterface: # Run chat completion (with chat history): list[langchain's BaseMessage] in -> LLMInterface out def run_batch_document(self, text: list[list[BaseMessage]]) -> list[LLMInterface]: # Run chat completion in batch mode (with chat history): list[list[langchain's BaseMessage]] in -> list[LLMInterface] out ``` - The LLMInterface is as follow: ```python @dataclass class LLMInterface: text: list[str] completion_tokens: int = -1 total_tokens: int = -1 prompt_tokens: int = -1 logits: list[list[float]] = field(default_factory=list) ``` 2023-08-29 15:47:12 +07:00			`# kotaemon`

			`Quick and easy AI components to build Kotaemon - applicable in client`
			`project.`

			`## Install`

			```shell
			`pip install kotaemon@git+ssh://git@github.com/Cinnamon/kotaemon.git`
			```

			`## Contribute`

			`### Setup`

			```shell
			`# Create conda environment (suggest 3.10)`
			`conda create -n kotaemon python=3.10`
			`conda activate kotaemon`

			`# Install all`
			`pip install -e ".[dev]"`

			`# Test`
			`pytest tests`
			```

			`### Code base structure`

Initiate repository 2023-08-16 14:56:48 +07:00			`- documents: define document`
			`- loaders`