For quickstart, you can rely on the default embedding utils. By default we use "HuggingFaceEmbedding" This eliminates the need to configure embeddings, making the process effortless.
To utilize the vectordb configuration with the default embedding:
=> Vectordb Usage
from langchain.docstore.document import Document as LangDocument
from genai_stack.vectordb.chromadb import ChromaDB
from genai_stack.vectordb.weaviate_db import Weaviate
from genai_stack.embedding.utils import get_default_embedding
from genai_stack.stack.stack import Stack
embedding = get_default_embedding()
chromadb = ChromaDB.from_kwargs()
chroma_stack = Stack(model=None, embedding=embedding, vectordb=chromadb)
# Add your documents
chroma_stack.vectordb.add_documents(
documents=[
LangDocument(
page_content="Some page content explaining something", metadata={"some_metadata": "some_metadata"}
)
]
)
chroma_stack.vectordb.search("page")
# Output
# Your search results
