What is Document Store?
Document Store
A Document Store is a database designed to store, retrieve, and manage document-oriented information, typically formatted as JSON, XML, or PDF structures. In RAG architectures, it holds the raw text associated with vector embeddings.
Why It Matters for AI Builders
Helps AI builders design and scale robust architectures; mastering the implementation of Document Store improves latency, accuracy, and operational efficiency for rag pipelines, knowledge management backends, and unstructured content retrieval.
Detailed Deep Dive
A document store is a type of non-relational database designed to store, retrieve, and manage semi-structured data, typically in formats like JSON, XML, or BSON. In AI retrieval and RAG architectures, document stores act as the primary repository for raw text documents, metadata, and citation sources, working in tandem with vector databases to retrieve and display source text back to users.
Frequently Asked Questions
Q:Why does a RAG system need both a vector database and a document store?
The vector database searches for the matching IDs using mathematical embeddings. The document store retrieves the actual readable text associated with those IDs to feed to the LLM.
Q:Give examples of databases acting as document stores.
MongoDB, Elasticsearch, and PostgreSQL JSONB columns.
Quick Facts
- CategoryData Infrastructure
- Key ApplicationRAG pipelines, knowledge management backends, and unstructured content retrieval.
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