At present, I have built a vector search database of existing text using chroma vector database, which can quickly search the relevant content that has been converted into vectors.
But now I have 600 megabytes of text content in plain text stored in the elaesticsearch database. I hope to combine chorma and elaesticsearch data content to feedback llama_index related questions.
Specific ideas are as follows:
- I want to search directly in plain text and retrieve the elaesticsearch database to return semantically relevant content.
- Then vector search results based on chroma vector database, and then vectorization conversion based on the text search results of elaesticsearch.
- Combine the results of the two searches.
How should it be done?
