Question: Is there a framework that provides built-in connectors for various data sources, embedding models, and vector databases, and allows custom connectors?

If you want a heavy-duty framework with built-in connectors for data sources, embedded models and vector databases, Airbyte could be just what you're looking for. This open-source data integration service lets you move data from more than 300 sources of structured and unstructured data to many destinations. It's got a Connector Builder for custom connectors, too, and supports prominent services like OpenAI and dbt. Airbyte also has automated schema evolution and strong security, so it's good for big and small-scale data integration jobs.

Another good choice is Neum AI, an open-source framework for building and operating data infrastructure for Retrieval Augmented Generation (RAG) and semantic search. Neum AI has scalable pipelines to handle millions of vectors and keeps them up to date as the underlying data changes. It has built-in connectors for many data sources and models, and supports real-time data embedding and indexing for RAG pipelines. It's good for big-scale and real-time data use cases, and it integrates well with services like Supabase.

If you want a more general-purpose data management service, you should look at Supabase. This open-source alternative to Firebase offers a Postgres database, user authentication, instant APIs, real-time subscriptions and storage. Supabase supports frameworks like Next.js and Flutter, and has built-in vector embeddings for machine learning model integration. It also has a data management dashboard and several pricing tiers, including a free option, so it should be useful for different needs and scales.

Last, LLMStack is an open-source service that lets developers create AI applications using pre-trained language models. It can import various data files and link them to LLM models for more advanced AI applications. LLMStack also has a no-code builder and supports vector databases for high-performance data storage, so it's good for creating chatbots, AI assistants and automating workflows. It can run in the cloud or on-premise, depending on your needs.