* feat: Add installers for linux, windows, and macos
* docs: Update README
* pre-commit fix styles
* Update installers and README
* Remove env vars check and fix paths
* Update installers:
* Remove start.py and move install and launch part back to .sh/.bat
* Add conda deactivate
* Make messages more informative
* Improve kotaemon based on insights from projects (#147)
- Include static files in the package.
- More reliable information panel. Faster & not breaking randomly.
- Add directory upload.
- Enable zip file to upload.
- Allow setting endpoint for the OCR reader using environment variable.
* feat: Add installers for linux, windows, and macos
* docs: Update README
* pre-commit fix styles
* Update installers and README
* Remove env vars check and fix paths
* Update installers:
* Remove start.py and move install and launch part back to .sh/.bat
* Add conda deactivate
* Make messages more informative
* Make macOS installer runable and improve Windows, Linux installers
* Minor fix macos commands
* installation should pause before exit
* Update Windows installer: add a new label to exit function with error
* put install_dir to .gitignore
* chore: Add comments to clarify the 'end' labels
---------
Co-authored-by: Duc Nguyen (john) <trungduc1992@gmail.com>
Co-authored-by: ian <ian@cinnamon.is>
* add albert to git-secret
* update readme
* Limit llama-index version
* Langchain upgrade their wikipedia tool name
---------
Co-authored-by: trducng <trungduc1992@gmail.com>
- 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)
```
