AI answers look right. But they’re often incomplete or just wrong. Short answers. Few sources. Not accurate. AnySearch pulls real-time data from multiple trusted sources into one view, so you get answers you can actually trust.

The future is being discussed online right now. So why are so many AI answers still based on old summaries? Most search tools give you conclusions. AnySearch shows you the conversation. Fresh discussions. New videos. Recent posts. Live sources. Because when you're asking about tomorrow, yesterday's answers aren't enough.

Introducing the Open Knowledge Format (OKF), an open specification that formalizes the LLM-wiki pattern into a portable, interoperable format. AI is only as smart as the context we give it. As we build more advanced, agentic AI systems, they need accurate metadata and context to be useful. But in most organizations, that context is locked inside fragmented data catalogs, isolated wikis, scattered code comments, or the minds of senior engineers. Every time a new AI agent is built, teams are forced to solve the exact same context-assembly problem from scratch. To solve this, we've announced OKF, a vendor-neutral, open specification that formalizes the "LLM-wiki pattern" into a portable, interoperable format. It provides a standardized way to represent the enterprise knowledge that modern AI systems rely on. — Just markdown: readable in any editor, renderable on GitHub, indexable by any search tool — Just files: shippable as a tarball, hostable in any git repo, mountable on any filesystem — Just YAML frontmatter: for the small set of structured fields that need to be queryable: type, title, description, resource, tags, and timestamp We’ve also shipped reference implementations to help you hit the ground running, including an enrichment agent for BigQuery, a static HTML visualizer, and live sample bundles on @github → ➕ Knowledge Catalog can now natively ingest OKF! Stop reinventing data models and building bespoke integrations for every new AI tool. Here's more about how OKF works →

# Elasticsearch Features and Practical Usage 🧩 Express "a keyword plus many filters" in a single JSON document. Query DSL and its `bool` query are the de facto standard for search backends, and how you use the filter clause decides your performance. 🏷️ Title: Query DSL (JSON query language) 🔗 URL: 📘 Overview Query DSL is a JSON-style query language used through the `_search` API. It expresses searching, filtering, and aggregations, with queries built as an abstract syntax tree of interconnected clauses. It is the de facto foundation of search backend implementations. ⚙️ How It Works Two distinctions are key. ・Clause types: standalone "leaf queries" (`match`, `term`, `range`, and so on) and "compound queries" (`bool`, `dis_max`) that wrap them. ・Context: query context asks "how well does this match?" and computes `_score`. Filter context asks a binary "does this match?", skips scoring, runs faster, and is automatically cached. The central `bool` query has four clauses: `must` (must match, scored), `should` (optional, boosts score, governed by `minimum_should_match`), `filter` (must match, unscored, cached), and `must_not` (excludes, filter context). 🛠️ Practical Usage For a job search, put the keyword query in `must` and the refinements in `filter`. ``` { "query": { "bool": { "must": [ { "multi_match": { "query": "backend engineer", "fields": ["title", "description"] } } ], "filter": [ { "term": { "location": "tokyo" } }, { "terms": { "employment_type": ["fulltime", "contract"] } }, { "range": { "salary": { "gte": 5000000 } } } ] } } } ``` The keyword should influence the score, so it goes in `must`; location, employment type, and salary need no scoring, so they go in `filter`. Filter clauses get cached, making repeated queries fast. 💡 Use Cases This pattern fits any search app with "full-text plus many structured filters", such as e-commerce, jobs, or real estate. Splitting conditions between `must` (rank by relevance) and `filter` (plain match/no-match) gives you both relevance ranking and strict narrowing in one request. ⚠️ Caveats The biggest pitfall is confusing `term` and `match`. `term` matches exactly without analysis, so using it on an analyzed `text` field usually returns zero results. Use `match` for `text`, and `term` for `keyword` and structured fields like status or dates. Always put non-scoring conditions in `filter` to benefit from caching and reduced CPU. #Elasticsearch# #QueryDSL#