This database can give you a quick headstart with the persist option. If you dont specify any arguments a default persistent storage will be used.

Supported Arguments:

host: Optional[str] = None
port: Optional[int] = None
persist_path: Optional[str] = None
search_method: Optional[SearchMethod] = SearchMethod.SIMILARITY_SEARCH
search_options: Optional[dict] = Field(default_factory=dict)

Supported Search Methods:

  • similarity_search

    • Search Options:

      • k : The top k elements for searching

  • max_marginal_relevance_search

    • Search Options

      • k: Number of Documents to return. Defaults to 4.

      • fetch_k: Number of Documents to fetch to pass to MMR algorithm.

      • lambda_mult: Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. Defaults to 0.5.


A Vectordb definitely needs a embedding function and you connect these two components through a stack.

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()
# Will use default persistent settings for a quick start
chromadb = ChromaDB.from_kwargs()
chroma_stack = Stack(model=None, embedding=embedding, vectordb=chromadb)

# Add your documents
            page_content="Some page content explaining something", metadata={"some_metadata": "some_metadata"}
# Search for content in your vectordb

You can also use different search_methods and search options when trying out more complicated usecases

chromadb = ChromaDB.from_kwargs(
    search_options={"k": 2, "fetch_k": 10, "lambda_mult": 0.3}

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