How-To: Use Chromadb to Store Similarity Index

ChromadbStore allows to use Chroma vector database as a store inside the SimilarityIndex. With this feature, when someone searches for 'Show my flights to the USA' and we have 'United States' stored in our database as the country's value, the system will recognize the similarity and convert the query from 'get_flights(to="USA")' to 'get_flights(to="United States")'.

Prerequisites

To use Chromadb with db-ally you need to install the chromadb extension

pip install dbally[chromadb]

Let's say we have already implemented our SimilarityFetcher

class DummyCountryFetcher(SimilarityFetcher):
    async def fetch(self):
        return ["United States", "Canada", "Mexico"]

Next, we need to define Chromadb.Client. You can run Chromadb on your local machine

import chromadb

chroma_client = chromadb.PersistentClient(path="/path/to/save/to")

or set up Chromadb in the client/server mode

chroma_client = chromadb.HttpClient(host='localhost', port=8000)

Next, you can either use one of dbally embedding clients, such as LiteLLMEmbeddingClient

from dbally.embeddings.litellm import LiteLLMEmbeddingClient

embedding_client=LiteLLMEmbeddingClient(
    model="text-embedding-3-small",  # to use openai embedding model
    api_key="your-api-key",
)

or Chromadb embedding functions

from chromadb.utils import embedding_functions

embedding_client = embedding_functions.DefaultEmbeddingFunction()

to define your ChromadbStore.

store = ChromadbStore(
    index_name="myChromaIndex",
    chroma_client=chroma_client,
    embedding_function=embedding_client,
)

After this setup, you can initialize the SimilarityIndex

from typing import Annotated

country_similarity = SimilarityIndex(store, DummyCountryFetcher())

and update it and find the closest matches in the same way as in built-in similarity indices .