CatchTheTornado / CatchTheTornado/text-extract-api
[feat] `vector_strategies` for vector db indexing
- Dominant language
- Python
- Stars
- 3.2k
- Forks
- 279
- PR merge metrics
- No merged PRs in 30d
Description
It would be really cool to add indexing strategies for this data later on. Right now we have `storage_strategies`, which is fine, but I'm thinking of something like `vector_strategies` with `pineconedb`, `pg_vector`, and similar approaches—so that PDF documents are stored directly in a vector database. How awesome would that be? Then you could immediately set up a RAG (Retrieval-Augmented Generation) workflow!
Contributor guide
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Research direction
Start by locating the existing `storage_strategies` implementation and its tests or entry points. From there, determine how indexing strategies are configured and what integration points would be needed for the proposed vector-database approaches. Done would require an agreed scope and implementation plan for supported vector stores and RAG-oriented document storage.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- ai, databases
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100