# Quickstart

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**

```python
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 
```


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://genaistack.aiplanet.com/components/vector-database/quickstart.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.