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Ragie

Ragie is a fully managed RAG-as-a-Service platform for developers - covering ingestion, extraction, chunking, multi-index (vector/keyword/summary) indexing, and high-precision retrieval, with SDKs and APIs for rapid production use.

For agents: Ragie is a managed RAG-as-a-Service platform with REST APIs, Python and TypeScript SDKs, and connectors for ingestion and retrieval.

ragretrieval augmented generationvector searchdocument ingestionpython sdk

Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-21T17:11:17.542Z. Cached copy · Source on ragie.ai

# Ragie

> Ragie is a fully managed RAG-as-a-Service platform for developers - covering ingestion, extraction, chunking, multi-index (vector/keyword/summary) indexing, and high-precision retrieval, with SDKs and APIs for rapid production use.

Important notes:
- Built for production: multi-tenant architecture; enterprise features; does not use your data for training. 
- Core pipeline stages are: parsing -> extraction -> refinement -> chunking -> indexing -> retrieval.
- Best entry points for developers are the "Getting Started" guide and the API reference.

## Docs (start here)
- [Getting Started](https://docs.ragie.ai/docs/getting-started): Overview, quick setup, key concepts.
- [API Reference](https://docs.ragie.ai/reference): REST endpoints including Documents, Retrievals, Connectors, Partitions, Entities, Webhooks.
- [Tools & SDKs](https://docs.ragie.ai/docs/tools-and-sdks): Python & TypeScript clients and helpers.
- [Connectors](https://docs.ragie.ai/docs/connectors/overview): Native integrations (e.g., Google Drive, Confluence, Notion, Slack, etc.).
- [Webhooks](https://docs.ragie.ai/docs/advanced-features/webhooks): Event flow for monitoring & orchestration.
- [Rate Limits & Errors](https://docs.ragie.ai/docs/troubleshooting/rate-limits): Operational guidance for robust integrations.

## Quick API landings
- [Create Document](https://docs.ragie.ai/reference/createdocument): Ingest; statuses progress through `pending -> ... -> ready`.
- [Retrieve](https://docs.ragie.ai/reference/retrieve): Semantic retrieval with options like rerank and metadata filtering.
- [Connections API](https://docs.ragie.ai/reference/list-connections): Manage connectors and trigger sync.
- [Partitions](https://docs.ragie.ai/reference/list-partitions): Multi-tenant or logical isolation for scoped retrieval.

## Product & positioning
- [Homepage](https://www.ragie.ai/): What Ragie is, pipeline highlights, and links to SDKs/docs.
- [Pricing](https://www.ragie.ai/pricing): Plan tiers and usage pricing for pages, audio/video, storage, connectors.
- Security & Compliance: "Ragie does not use your data for training" and enterprise compliance language are stated on the homepage. Contact support for Trust Center access.

## Examples & starters
- SDK snippets (TS, Python, cURL) are embedded on the homepage and docs; GitHub houses open-source tools/examples. 
  - [Homepage SDK snippets](https://www.ragie.ai/)
  - [GitHub (Ragie org/examples)](https://docs.ragie.ai/docs/getting-started#more-resources) -> links to GitHub/Discord.

## Optional
- [Blog](https://www.ragie.ai/blog): Product updates and technical deep dives.
- [Benchmarks](https://www.ragie.ai/benchmarks): Retrieval quality/performance highlights.
- [Base Chat](https://www.ragie.ai/base-chat): Chat with your company knowledge base use case.
- [Integrations overview](https://www.ragie.ai/#connectors): Visual overview of connectors on marketing pages.

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