Consider Pinecone as a dedicated vector database to optimize similarity searches, accepting some administration and infrastructure overhead for performance gains.
Use metadata embedding searches to find a relevant pointer, then invoke SQL or graph queries to retrieve full, detailed context in a two-step Retrieval-Augmented Generation workflow.
Use a graph database to represent concepts as nodes and relationships as edges for retrieval-augmented generation, offering semantic search alternatives to vector embeddings.
Implement a robust ETL pipeline for email data that handles cleaning tasks like date normalization, emoji removal, and format standardization before AI ingestion.
Instead of sending raw text to the LLM, pull out key attributes (sender, receiver, body, organization) into JSON to drastically reduce data volume and improve embedding efficiency.
Superlinked offers a vector computer enabling parameter control over vectorized datasets to enhance search effectiveness through customizable vector parameters.
Choose chunk sizes and extraction methods based on data type—plain text, structured documents with charts and relationships, or images—to preserve context and relationships during vectorization.
Define high-level outcomes, chunk and extract data, perform vectorization with appropriate overlaps, add metadata, and store for search to build an effective vector pipeline.
The Doc ETL project mapped U.S. presidential debate transcripts to themes, extracted and deduplicated them, and loaded the results for further analysis.
