Basically, I see it like this:
- pinecone (or similar like qdrant, chroma, etc) are good for storing lots of vectors/embeddings
- llama index is used to connect your LLM app to your data, by building customizable and powerful indexes over your data. Use it when you need to build and persist a knowledge base.
- langchain itself is more of a chat interface. It can use llama index as a "Tool", so that you can create and use your indexes from llama index for specific use cases inside langchain
- the LLM is used in both langchain and llama index, to generate natural language for conversations and queries.
- separately, you have an embedding model, which generates embeddings for documents that helps with search