Do you need a vector database to ask questions about a folder of documents? [draft]
The default answer to ’let the agent answer from my documents’ is an embedding pipeline and a vector database. For a folder of contracts, reports or mail, an agent with full-text search and a local index gets you grounded, cited answers with far less machinery.
grep can't read PDF: give your coding agent a document index [draft]
Coding agents search a repository with grep, and grep sees nothing inside a PDF datasheet or a Word specification. A local document index over MCP gives the agent the same search-then-read loop for the documents next to the code.
Grounded answers from your files: quote the passage, not the model's memory [draft]
Pasting documents into a chat fills the context with text the question does not need, and the answer drifts away from the source. Fragments from a local index give the agent only the matching passages, so every answer can cite what it rests on.
GroupDocs.Search for .NET 26.10 – October 2026 Release Highlights
GroupDocs.Search for .NET 26.10 is a major release: a redesigned index format makes indexes smaller, cheaper in RAM and faster to open, and speeds up search, deletion, optimization and image search.
GroupDocs.Watermark for Python 26.10 – October 2026 Release Highlights
GroupDocs.Watermark for Python 26.10 introduces a custom logger, stricter attribute handling, engine and runtime upgrades, and several bug fixes.
Project memory for AI agents: one index per project, kept current [draft]
An agent that re-reads a project’s documents at the start of every session wastes time and context. A named, persistent index is the agent’s memory of the folder: built once, refreshed with one call when files change, separate for each project.
Search your email archive with an AI agent: PST, MSG, EML and the attachments inside [draft]
The answer to ‘what did the supplier say about prices?’ is often in an attachment, inside a message, inside an archive. GroupDocs.Search.Mcp indexes archives message by message and attachment by attachment, and tells the agent exactly where a match was found.
Why an AI agent should not read 300 contracts to answer one question [draft]
An agent that answers ‘which contracts mention X’ by opening every file burns its context and still misses things. An index answers which files match in one call, and the agent reads only the clauses that matter.
GroupDocs.Redaction for Java 26.10 – October 2026 Release Highlights
GroupDocs.Redaction for Java 26.10 introduces OOXML compliance, custom PDF redaction, and new spreadsheet template support.
GroupDocs.Redaction for Node.js 26.10 – October 2026 Release Highlights
The October 2026 release brings OOXML compliance, new Excel template formats, and custom PDF page‑area redaction to GroupDocs.Redaction for Node.js.
