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 Flashback Pattern

Use Flashback 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 Flashback (S3/GCS/Azure-compatible).

• AI LLM provider connected in Flashback.

• Repository configured with both storage and AI resources.

• Chunking/indexing pipeline in your application stack.

References:

• Cloud Storage use cases

• Build a Repository

Architecture flow

1. Ingest documents from storage buckets through Flashback 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 Flashback AI endpoint with retrieved context.

Minimal implementation snippets

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

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)

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 Flashback APIs.