Principle:Deepset ai Haystack In Memory Document Storage
| Knowledge Sources | |
|---|---|
| Domains | Information_Retrieval, Data_Storage |
| Last Updated | 2026-02-11 00:00 GMT |
Overview
A storage mechanism that holds documents entirely in RAM for fast read/write access without persistence.
Description
In-memory document storage provides an ephemeral document store that keeps all documents in the application's working memory. Unlike disk-based or database-backed stores, it offers near-instantaneous retrieval and indexing at the cost of durability. Data is lost when the process terminates. This pattern is widely used in prototyping, testing, and small-scale applications where persistence is not required. In-memory stores typically support both keyword-based (BM25) and vector-based (embedding similarity) retrieval by maintaining parallel index structures.
Usage
Use this principle when building pipelines that require fast document retrieval without the overhead of external database connections. Ideal for prototyping RAG pipelines, running integration tests, or processing small document collections that fit in memory. Not suitable for production workloads requiring data persistence or handling datasets larger than available RAM.
Theoretical Basis
In-memory document stores combine two retrieval paradigms:
Keyword Retrieval (BM25): Documents are tokenized and indexed using the BM25 algorithm, which scores documents based on term frequency and inverse document frequency:
Vector Retrieval: Documents with precomputed embeddings are retrieved using similarity functions (dot product or cosine) against a query embedding vector.
Pseudo-code:
# Abstract storage pattern (NOT real implementation)
store = create_in_memory_store(similarity="dot_product")
store.write(documents)
# BM25 retrieval path
keyword_results = store.bm25_search(query="search terms", top_k=10)
# Embedding retrieval path
vector_results = store.embedding_search(query_vector=embed(query), top_k=10)