Documentssuch

Documentssuch is a hypothetical open-source information retrieval framework designed to index, store, and search large collections of documents and their metadata. It aims to provide a straightforward integration path for applications that require fast, relevant text search and analysis.

The core architecture centers on an indexing pipeline, an inverted index, a document store, and a query processor. It supports both traditional keyword search and vector-based similarity search, allowing users to combine lexical and semantic signals. Documents are modeled with an identifier, title, content, and metadata such as author, date, language, and tags.

Indexing and storage: The indexer ingests documents, applies normalization, tokenization, and optional stemming or lemmatization, and builds an inverted index. A separate document store preserves the original content and metadata. Backends may include local files, relational databases, or object stores.

Querying: The query processor parses user queries, supports boolean operators, phrase searches, wildcards, and proximity constraints. It can also perform vector searches using document and query embeddings, combining signals through configurable ranking strategies such as traditional and learning-to-rank models. Results can be returned with snippets and highlighted terms, and can be filtered by facets from metadata.

Extensibility and licensing: Documentssuch is designed to be modular, with pluggable backends for storage, tokenization, and ranking. It is released under an open-source license and welcomes community contributions, documentation, and example integrations for common programming languages and frameworks.