The Index section shows where the documents associated with the RAG Assistant will be stored as a collection of embedded chunks (using the embeddings model configuration).
Each document will be partitioned using a chunking strategy defined by the following parameters:
- Chunk Size: Controls the max size (in terms of number of characters) of the chunk.
- Chunk Overlap: How much overlap there should be between two consecutive chunks.
Note that the text is split into single characters and measured by the number of characters.
Large chunk overlap may cause the same information to be extracted twice.
The bigger the chunk the more general the embeddings, so it may not match specific queries as expected (low score). On the other hand, a small chunk could have a higher score but may miss important and necessary semantics.
Depending on the target LLM used for answering (those with larger context windows), you can try different sizes of these parameters and check how they work for your documents.
Remember that these strategies have different trade-offs and the best strategy likely depends on the application that you're designing.