Christophe Bornet 42d049f618 core[minor]: Add Graph Store component (#23092) ... This PR introduces a GraphStore component. GraphStore extends VectorStore with the concept of links between documents based on document metadata. This allows linking documents based on a variety of techniques, including common keywords, explicit links in the content, and other patterns. This works with existing Documents, so it’s easy to extend existing VectorStores to be used as GraphStores. The interface can be implemented for any Vector Store technology that supports metadata, not only graph DBs. When retrieving documents for a given query, the first level of search is done using classical similarity search. Next, links may be followed using various traversal strategies to get additional documents. This allows documents to be retrieved that aren’t directly similar to the query but contain relevant information. 2 retrieving methods are added to the VectorStore ones : * traversal_search which gets all linked documents up to a certain depth * mmr_traversal_search which selects linked documents using an MMR algorithm to have more diverse results. If a depth of retrieval of 0 is used, GraphStore is effectively a VectorStore. It enables an easy transition from a simple VectorStore to GraphStore by adding links between documents as a second step. An implementation for Apache Cassandra is also proposed. See https://github.com/datastax/ragstack-ai/blob/main/libs/knowledge-store/notebooks/astra_support.ipynb for a notebook explaining how to use GraphStore and that shows that it can answer correctly to questions that a simple VectorStore cannot. **Twitter handle:** _cbornet		2024-07-05 12:24:10 -04:00
..
__init__.py	core[minor]: Add Graph Store component (#23092)	2024-07-05 12:24:10 -04:00
test_cassandra.py	core[minor]: Add Graph Store component (#23092)	2024-07-05 12:24:10 -04:00