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- Figuring out what exactly needs to be updated, and only updating that without having to recompute everything throughout.
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This makes it fast to reflect any source updates to the target store. If you have concerns with surfacing stale data to AI agents and are spending lots of efforts working on infra piece to optimize the latency, the framework actually handles it for you.
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## Quick Start:
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If you're new to CocoIndex 🤗, we recommend checking out the 📖 [Documentation](https://cocoindex.io/docs) and ⚡ [Quick Start Guide](https://cocoindex.io/docs/getting_started/quickstart). We also have a ▶️ [quick start video tutorial](https://youtu.be/gv5R8nOXsWU?si=9ioeKYkMEnYevTXT) for you to jump start.
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If you're new to CocoIndex, we recommend checking out
- 🎬 [Quick Start Video Tutorial](https://youtu.be/gv5R8nOXsWU?si=9ioeKYkMEnYevTXT)
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### Setup
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1. Install CocoIndex Python library
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pip install -U cocoindex
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```
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2. Setup Postgres with pgvector extension; or bring up a Postgres database using docker compose:
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- Make sure Docker Compose is installed: [docs](https://docs.docker.com/compose/install/)
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- Start a Postgres SQL database for cocoindex using our docker compose config:
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2.[Install Postgres](https://cocoindex.io/docs/getting_started/installation#-install-postgres) if you don't have one. CocoIndex uses it for incremental processing.
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```bash
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docker compose -f <(curl -L https://raw.githubusercontent.com/cocoindex-io/cocoindex/refs/heads/main/dev/postgres.yaml) up -d
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```
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### Start your first indexing flow!
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Follow [Quick Start Guide](https://cocoindex.io/docs/getting_started/quickstart) to define your first indexing flow.
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