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Genkit

Dev Local Vector Store Plugin

Dev Local Vector Store

The Dev Local Vector Store plugin provides a local, file-based vector store for development and testing purposes. It is not intended for production use.

Installation

Terminal window
pip3 install genkit-plugin-dev-local-vectorstore

Configuration

To use this plugin, specify it when you initialize Genkit:

from genkit.ai import Genkit
from genkit.plugins.dev_local_vectorstore import DevLocalVectorStore
from genkit.plugins.google_genai import VertexAI # Assuming VertexAI is used for embedder


ai = Genkit(
    plugins=[
        VertexAI(), # Ensure the embedder's plugin is loaded
        DevLocalVectorStore(
            name='my_vectorstore',
            embedder='vertexai/text-embedding-004', # Example embedder
        ),
    ],
    # Define a default model if needed
    # model='vertexai/gemini-1.5-flash',
)

Configuration Options

  • name (str): A unique name for this vector store instance. This is used as the retriever argument to ai.retrieve.
  • embedder (str): The name of the embedding model to use. Must match a configured embedder in your Genkit project.
  • embedder_options (dict, optional): Options to pass to the embedder.

Usage

Indexing Documents

The Dev Local Vector Store automatically creates indexes. To populate with data you must call the static method .index(name, documents):

from genkit.ai import Genkit
from genkit.plugins.dev_local_vectorstore import DevLocalVectorStore
from genkit.plugins.google_genai import VertexAI # Assuming VertexAI is used for embedder
from genkit.types import Document


# Assuming 'ai' is configured as shown in the Configuration section
# ai = Genkit(...)


data_list = [
    'This is the first document.',
    'This is the second document.',
    'This is the third document.',
    "This is the fourth document.",
]


genkit_docs = [Document.from_text(text=item) for item in data_list]
# Ensure the vector store name matches the one in the Genkit config
await DevLocalVectorStore.index('my_vectorstore', genkit_docs)

Retrieving Documents

Use ai.retrieve and pass the store name configured in the DevLocalVectorStore constructor.

from genkit.types import Document
# Assuming 'ai' is configured as shown in the Configuration section
# ai = Genkit(...)


docs = await ai.retrieve(
    query=Document.from_text('search query'),
    retriever='my_vectorstore', # Matches the 'name' in DevLocalVectorStore config
)
# print(docs) # Process the retrieved documents