Integrations

QvikChat is built on top of Firebase Genkit (opens in a new tab) and LangChain (opens in a new tab). This allows you to easily extend the functionality of QvikChat using any of the Genkit plugins or LangChain integrations.

Genkit Plugins

Given that QvikChat is built on top of Firebase Genkit (opens in a new tab), you can easily extend the functionality of QvikChat using any of the Genkit plugins. For example, you may want to add the Ollama plugin (opens in a new tab) to use a self-hosted open-source LLM model. For more information on adding plugins and configuring Genkit with QvikChat, check the documentation on Genkit.

LangChain Integrations

Embedding Models: QvikChat uses the text-embedding-004 (opens in a new tab) model through the Gemini API as the default embedding model for generating embedding for data when using RAG. You can easily use a custom embedding model by simply providing an instance of any LangChain-supported embedding model (opens in a new tab) as the embeddingModel when configuring an endpoint using the retrieverConfig or in the configuration parameters of the getDataRetriever method. To learn more check Embedding Models (opens in a new tab).

Vector Stores: By default, QvikChat uses an in-memory vector store, but you can easily provide an instance of any hosted vector store supported by LangChain (JS) (opens in a new tab). For more information, check the documentation on Vector Store (opens in a new tab).

Data Loaders: Out of the box, QvikChat provides support for loading data from text, PDF, JSON, CSV, or a code of a supported programming language. However, if you want to load a file not supported by QvikChat by default, you can simply provide an instance of any LangChain-supported data loader (opens in a new tab) as the dataLoader parameter to the retrieverConfig or in the configurations parameters of the getDataRetriever method. For more info, check Data Loaders (opens in a new tab).

To learn more about LangChain integrations, check the LangChain section.

Firebase

QvikChat provides support for using Firebase Firestore as a chat history store, cache store and API key store. To learn how to setup Firebase, check the Firebase section.