NeumAI
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NeumTry
Neum AI is a best-in-class framework to manage the creation and synchronization of vector embeddings at large scale.
Support using embedding services through url and api key or similar. Would allow neum to be more open and less vendor locked to the currently supported services.
Querying requirements across RAG fall not only onto unstructured data that has been embedded and added to an vector database. It also falls onto structured data sources where semantic search...
Currently support unified (re-rank results into single list) and separate (results for each pipeline returned separately) searches for a collection . Adding smart search which will do a smart routing...
When sink is queried using search API, if the retrieved information is correct (based on feedback or by running results against a different model), we could re-ingest the retrieved query...
As chat histories get longer, passing the entire history on every call is not a good practice. More so, user expects information from several messages ago to be available as...
file_id is a unique identifier for each file processed by a pipeline. file_id = pipeline_id + cloudFile_id Necessary to be able to leverage delete, update and augment capabilities.
Right now we support a list of FilterConditions which are automatically AND to each other. More complex nesting might be necessary.
Given a query to the search interface for a sink, generate the FilterConditions automatically using the metadata fields available for a sink.