Contributing¶
Setting up¶
-
Clone the repo
-
Install the environment
-
Create a conda environment (python >= 3.10 is recommended)
-
Or run the installer (one of the
scripts/run_*
scripts depends on your OS), then you will have all the dependencies installed as a conda environment atinstall_dir/env
.
-
-
Pre-commit
-
Test
Package overview¶
kotaemon
library focuses on the AI building blocks to implement a RAG-based QA application. It consists of base interfaces, core components and a list of utilities:
- Base interfaces:
kotaemon
defines the base interface of a component in a pipeline. A pipeline is also a component. By clearly define this interface, a pipeline of steps can be easily constructed and orchestrated. - Core components:
kotaemon
implements (or wraps 3rd-party libraries like Langchain, llama-index,... when possible) commonly used components in kotaemon use cases. Some of these components are: LLM, vector store, document store, retriever... For a detailed list and description of these components, please refer to the API Reference section. - List of utilities:
kotaemon
provides utilities and tools that are usually needed in client project. For example, it provides a prompt engineering UI for AI developers in a project to quickly create a prompt engineering tool for DMs and QALs. It also provides a command to quickly spin up a project code base. For a full list and description of these utilities, please refer to the Utilities section.
mindmap
root((kotaemon))
Base Interfaces
Document
LLMInterface
RetrievedDocument
BaseEmbeddings
BaseChat
BaseCompletion
...
Core Components
LLMs
AzureOpenAI
OpenAI
Embeddings
AzureOpenAI
OpenAI
HuggingFaceEmbedding
VectorStore
InMemoryVectorstore
ChromaVectorstore
Agent
Tool
DocumentStore
...
Utilities
Scaffold project
PromptUI
Documentation Support
Common conventions¶
- PR title: One-line description (example: Feat: Declare BaseComponent and decide LLM call interface).
- [Encouraged] Provide a quick description in the PR, so that:
- Reviewers can quickly understand the direction of the PR.
- It will be included in the commit message when the PR is merged.
Environment caching on PR¶
- To speed up CI, environments are cached based on the version specified in
__init__.py
. - Since dependencies versions in
setup.py
are not pinned, you need to pump the version in order to use a new environment. That environment will then be cached and used by your subsequence commits within the PR, until you pump the version again - The new environment created during your PR is cached and will be available to others once the PR is merged.
- If you are experimenting with new dependencies and want a fresh environment every time, add
[ignore cache]
in your commit message. The CI will create a fresh environment to run your commit and then discard it. - If your PR include updated dependencies, the recommended workflow would be:
- Doing development as usual.
- When you want to run the CI, push a commit with the message containing
[ignore cache]
. - Once the PR is final, pump the version in
__init__.py
and push a final commit not containing[ignore cache]
.
Merge PR guideline¶
- Use squash and merge option
- 1st line message is the PR title.
- The text area is the PR description.