Looking for an example with Chroma for managing documents. Retrieving Document 1 and Generating Embedding: The text of Document 1 is retrieved from ChromaDB and an embedding is generated using LlamaIndex.

ChatGPT Interaction : An request is sent to the ChatGPT API to obtain a summary of Document 1.

Adding Document 2: The text and embedding of Document 2 are added to ChromaDB.

ChatGPT Interaction: An optional request is sent to the ChatGPT API to generate a comparison between Document 1 and Document 2.

Updating Document 1: The text of Document 1 is updated, a new embedding is generated, and it is stored in ChromaDB.

ChatGPT Interaction: An optional request is sent to the ChatGPT API to summarize the changes made to Document 1.

Deleting Document 2: Document 2 is deleted from ChromaDB.

I want to see all the prompts that LlamaIndex send and the answer it receives? Any examples?

How to use Ollama for Embeddings?

I'm using index.as_chat_engine(..). It does not only use data from my vectorstore but uses also data from elsewhere. Can I avoid that? I use chat_mode="condense_plus_context" and this prompt \nInstruction: Use the previous chat history, or the context above, to interact and help the user. Don't use any other informations." But it is not correct.

Is it possible to query OpenAI with LlamaIndex directly without using Rag?