Replies: 2 comments
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🤖 Based on the information available, LlamaIndex does indeed support SQL databases for docstore. This is evident from the def __init__(
self,
nodes: Optional[Sequence[BaseNode]] = None,
index_struct: Optional[SQLStructTable] = None,
service_context: Optional[ServiceContext] = None,
sql_database: Optional[SQLDatabase] = None,
table_name: Optional[str] = None,
table: Optional[Table] = None,
ref_doc_id_column: Optional[str] = None,
sql_context_container: Optional[SQLContextContainer] = None,
**kwargs: Any,
) -> None:
"""Initialize params."""
if sql_database is None:
raise ValueError("sql_database must be specified")
self.sql_database = sql_database The As for the support of PostgreSQL as a unified storage for Embeddings (VectorStores) and the docstore index, there is a solved issue titled Add postgres vector database support which indicates that Postgres vector database support has been added to LlamaIndex. You can find the documentation and usage examples at this link: Postgres Vector Store. I hope this information is helpful. If you have further questions or need more clarification, feel free to ask. Sources
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@matthias I would gladly welcome a PR for this! The KVStore abstractions are really pretty simple, so this integration would be an easy PR :) |
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Are there any plans to be able to use SQL databases (in addition to KV stores) for docstore
Would be awesome to be able to use Supabase / PostgreSQL as a unified storage for Emebddings (VectorStores) and the docstore index.
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