CocoIndex增量更新AI检索数据

Incremental engine for long horizon agents 🌟 Star if you like it!

Your agents deserve fresh context. Star us ❤️ → · · · CocoIndex turns codebases, meeting notes, inboxes, Slack, PDFs, and videos into live, continuously fresh context for your AI agents and LLM apps to reason over effectively — with minimal incremental processing. Get your production AI agent ready in 10 minutes with reliable, continuously fresh data — no stale batches, no context gap Incremental · only the delta · Any scale · parallel by default · Declarative · Python, 5 min Deutsch | English | Español | français | 日本語 | 한국어 | Português | Русский | 中文 Built with CocoIndex ❤️ See all 20+ examples · updated every week → Get started pip install -U cocoindex Declare what should be in your target — CocoIndex keeps it in sync forever, recomputing only the Δ. import cocoindex as coco from cocoindex.connectors import localfs, postgres from cocoindex.ops.text import RecursiveSplitter @coco.fn(memo=True) # ← cached by hash(input) + hash(code) async def index_file(file, table): for chunk in RecursiveSplitter().split(await file.read_text()): table.declare_row(text=chunk.text, embedding=embed(chunk.text)) @coco.fn async def main(src): table = await postgres.mount_table_target(PG, table_name="docs") table.declare_vector_index(column="embedding") await coco.mount_each(index_file, localfs.walk_dir(src).items(), table) coco.App(coco.AppConfig(name="docs"), main, src="./docs").update_blocking() Run once to backfill. Re-run anytime — only the changed files re-embed. Building with an AI coding agent? Drop in our CocoIndex skill so your agent writes correct v1 code — concepts, APIs, patterns, all in one file. See Use with AI coding agents for install steps. React — for data engineering See the React ↔ CocoIndex mental model → Incremental engine for long-horizon agents Data transformation for any engineer, designed for AI workloads — with a smart incremental engine for always-fresh, explainable data. Why incremental? Your agents are only as good as the data they see. Batch pipelines drift stale. CocoIndex stays live — and only runs the Δ. What can you build? See all 20+ examples · updated every week → Working starters from the examples tree — clone, plug your source, ship. Building something with CocoIndex? We want to see it. Tag @cocoindex_io on X or drop a link in #showcase on Discord. We'll boost it. 🥥 Community We are so excited to meet you. Every typo fix, new connector, doc tweak, or full-on rewrite makes CocoIndex better. Come hang out — big PRs and small ones, both welcome. 📝 Read the contributing guide · 🐛 good first issues · 💬 Say hi on Discord CocoIndex Enterprise Large corpus — built for enterprise scale. Incremental compute is the only way to keep large corpora fresh without re-embedding them every cycle. CocoIndex scales from a single repo to petabyte-scale stores — parallel by default, delta-only by design. Process once. Reconcile forever. When a source changes, CocoIndex identifies the affected records, propagates the change across joins and lookups, updates the target, and retires stale rows — without touching anything that didn't change. Built on a Rust engine. The core is Rust — production-grade from day zero. Parallel chunking, zero-copy transforms where possible, and failure isolation so one bad record doesn't stall the flow. Apache 2.0 · © CocoIndex contributors 🥥