Using external vector DBs

[markokow]

One of my struggle when using PaperQA is the inference time every time I do queries. Is there a means I could do to import external vector DBs rather than just relying on NumpyVectorStore? caching is not an option since I will be needing the embedded results on a scheduled basis and it is stored in cloud to save memory.

The documentation is lacking on the part of providing external embeddings, I would appreciate a working sample use case

[dosubot]

Hey @markokow, I'm here to help you out with any bugs or questions you have while waiting for a human maintainer. Let's squash those bugs together! 🤖

PaperQA provides an interface for plugging in custom vector stores, allowing you to use different vector stores and embedding models. By default, it uses Numpy as a dense vector store, but you can specify a custom embedding model when adding new papers to the Docs object. Here's an example of how you can use a custom embedding model:

from paperqa import Docs, HybridEmbeddingModel, SparseEmbeddingModel, LiteLLMEmbeddingModel

model = HybridEmbeddingModel(
    models=[LiteLLMEmbeddingModel(), SparseEmbeddingModel(ndim=1024)]
)
docs = Docs()
for doc in ("myfile.pdf", "myotherfile.pdf"):
    docs.add(doc, embedding_model=model)

This setup allows you to integrate different vector stores and embedding models as needed. However, specific support or plugins for external vector databases like Pinecone, Weaviate, or FAISS are not explicitly mentioned in the available documentation ^[1].

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