Document store handles storing and indexing Documents. It supports the following interfaces:
- add: add 1 or more documents into document store
- get: get a list of documents
- get_all: get all documents in a document store
- delete: delete 1 or more document
- save: persist a document store into disk
- load: load a document store from disk
Design the base interface of vector store, and apply it to the Chroma Vector Store (wrapped around llama_index's implementation). Provide the pipelines to populate and retrieve from vector store.
This change provides the base interface of an embedding, and wrap the Langchain's OpenAI embedding. Usage as follow:
```python
from kotaemon.embeddings import AzureOpenAIEmbeddings
model = AzureOpenAIEmbeddings(
model="text-embedding-ada-002",
deployment="embedding-deployment",
openai_api_base="https://test.openai.azure.com/",
openai_api_key="some-key",
)
output = model("Hello world")
```
