> For the complete documentation index, see [llms.txt](https://docs.flashback.tech/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.flashback.tech/guides/explore-use-cases/ai-llm/rag-knowledge-assistant-over-multi-cloud-storage.md).

# RAG knowledge assistant over multi-cloud storage

## The Problem

Documentation and internal knowledge are spread across buckets/providers and updated by many teams. Centralizing everything first can delay AI feature delivery.

## The Flashgate Pattern

Use Flashgate to unify access:

* Cloud documents are accessed via repository storage endpoints.
* AI generation runs via repository OpenAI-compatible endpoints.
* One control plane handles keys, observability, and policies.

This allows phased RAG adoption without a single-provider lock-in.

## Prerequisites

* Storage resources connected in Flashgate (S3/GCS/Azure-compatible).
* AI LLM provider connected in Flashgate.
* Repository configured with both storage and AI resources.
* Chunking/indexing pipeline in your application stack.

References:

* [Cloud Storage use cases](/guides/explore-use-cases/explore-use-cases.md)
* [Build a Repository](/guides/setup-the-cloud-and-ai-gateway/start-with-cloud-storage-1.md)

## Architecture flow

1. **Ingest** documents from storage buckets through Flashgate endpoint.
2. **Chunk + embed** each segment (embedding model/service of your choice).
3. **Index** vectors in your search backend (pgvector, OpenSearch, Pinecone, etc.).
4. **Retrieve** top-k chunks at query time.
5. **Generate** final answer via Flashgate AI endpoint with retrieved context.

## Minimal implementation snippets

### 1) Read files from storage endpoint (Python, S3-compatible)

```python
import boto3
from botocore.client import Config

s3 = boto3.client(
    "s3",
    endpoint_url="https://s3-us-east-1-aws.flashback.tech",
    aws_access_key_id="FB_KEY_ID",
    aws_secret_access_key="FB_KEY_SECRET",
    config=Config(signature_version="s3v4")
)

obj = s3.get_object(Bucket="knowledge-bucket", Key="handbook/security.md")
content = obj["Body"].read().decode("utf-8")
```

### 2) Build answer with retrieved chunks (Node.js)

```ts
import OpenAI from "openai";

const client = new OpenAI({
  baseURL: process.env.FB_OPENAI_BASE_URL,
  apiKey: process.env.FB_API_KEY_SECRET
});

export async function answer(question: string, chunks: string[]) {
  const context = chunks.join("\n\n---\n\n");

  const res = await client.chat.completions.create({
    model: "gpt-4.1-mini",
    temperature: 0,
    messages: [
      { role: "system", content: "Answer only using provided context. If unknown, say unknown." },
      { role: "user", content: `Question: ${question}\n\nContext:\n${context}` }
    ]
  });

  return res.choices[0].message.content;
}
```

## Operational recommendations

* Version your chunking strategy and re-index process.
* Add metadata filters (workspace, doc type, language, confidentiality).
* Log retrieval scores and prompt/response latency separately.
* Apply AI policy rules to reduce prompt injection and sensitive output risks.

## Expected outcome

A portable RAG stack where storage and generation are decoupled from any single cloud provider, while still exposed through stable Flashgate APIs.


---

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