Cuadra AI - Connect, Train, and Deploy Your Custom AI Assistant

How RAG Works

Step	Process
1. Upload	Add files to the platform
2. Chunk	Documents split into ~250 token segments
3. Embed	Vector embeddings generated for each chunk
4. Search	User query matched against embeddings
5. Retrieve	Top chunks injected into LLM context

Create Dataset

curl -X POST https://api.cuadra.ai/v1/datasets \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Idempotency-Key: create-ds-001" \
  -d '{"name": "Product Docs", "description": "API guides"}'

import httpx

response = httpx.post(
    "https://api.cuadra.ai/v1/datasets",
    headers={"Authorization": "Bearer YOUR_TOKEN", "Idempotency-Key": "create-ds-001"},
    json={"name": "Product Docs", "description": "API guides"}
)
dataset = response.json()

const response = await fetch('https://api.cuadra.ai/v1/datasets', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer YOUR_TOKEN',
    'Content-Type': 'application/json',
    'Idempotency-Key': 'create-ds-001'
  },
  body: JSON.stringify({ name: 'Product Docs', description: 'API guides' })
});
const dataset = await response.json();

Upload Documents

Adding documents is a two-step process: upload the file, then associate it with a dataset.

Step 1: Upload File

curl -X POST https://api.cuadra.ai/v1/files \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -H "Idempotency-Key: upload-001" \
  -F "file=@product-guide.pdf"

import httpx

with open("product-guide.pdf", "rb") as f:
    response = httpx.post(
        "https://api.cuadra.ai/v1/files",
        headers={"Authorization": "Bearer YOUR_TOKEN", "Idempotency-Key": "upload-001"},
        files={"file": f}
    )
file = response.json()
file_id = file["id"]

const formData = new FormData();
formData.append('file', fileBlob, 'product-guide.pdf');

const response = await fetch('https://api.cuadra.ai/v1/files', {
  method: 'POST',
  headers: { 'Authorization': 'Bearer YOUR_TOKEN', 'Idempotency-Key': 'upload-001' },
  body: formData
});
const file = await response.json();
const fileId = file.id;

Step 2: Associate with Dataset

curl -X POST https://api.cuadra.ai/v1/files/file_abc123/associations \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"datasetId": "ds_xyz789"}'

import httpx

response = httpx.post(
    f"https://api.cuadra.ai/v1/files/{file_id}/associations",
    headers={"Authorization": "Bearer YOUR_TOKEN"},
    json={"datasetId": "ds_xyz789"}
)
print("File added to dataset")

await fetch(`https://api.cuadra.ai/v1/files/${fileId}/associations`, {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer YOUR_TOKEN',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({ datasetId: 'ds_xyz789' })
});
console.log('File added to dataset');

Supported Formats

Format	Extensions	Max Size
PDF	.pdf	50MB
Word	.docx	50MB
Text	.txt, .md	50MB
Data	.csv, .json	50MB

Link to Model

Connect a dataset to enable RAG:

curl -X POST https://api.cuadra.ai/v1/models/model_abc/datasets \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"datasetId": "ds_xyz", "usageType": "rag"}'

import httpx

response = httpx.post(
    "https://api.cuadra.ai/v1/models/model_abc/datasets",
    headers={"Authorization": "Bearer YOUR_TOKEN"},
    json={"datasetId": "ds_xyz", "usageType": "rag"}
)
print("Dataset linked to model")

const response = await fetch('https://api.cuadra.ai/v1/models/model_abc/datasets', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer YOUR_TOKEN',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({ datasetId: 'ds_xyz', usageType: 'rag' })
});
console.log('Dataset linked to model');

Best Practices

Do	Avoid
Clean formatting before upload	Scanned images without OCR
Use descriptive filenames	Duplicate content across files
Split large docs into sections	Mixing unrelated topics
Group related content	PII or sensitive data

Specifications

Spec	Value
Max file size	50MB
Chunk size	~250 tokens
Search latency	40-120ms

FAQ

What file formats work best?

Markdown and plain text yield the best results. PDFs work well if they’re text-based (not scanned images). Use OCR preprocessing for scanned documents.

How often is content re-indexed?

Uploaded files are indexed once at upload. Re-upload to refresh content.

Can I preview what chunks were created?

Not via API currently. Use the Dashboard → Datasets → View to inspect chunks.

How do I improve retrieval quality?

Use specific, descriptive filenames
Add summaries at the start of documents
Remove boilerplate/headers that repeat across pages
Split very long documents into logical sections

What happens if I delete a document?

The document and its chunks are removed. This affects new chats only—existing chat histories retain their context.

Chat API

Use RAG in chat completions

Models

Link datasets to models

Getting Started

API Reference

Guides

Billing

Knowledge Bases

How RAG Works

Create Dataset

Upload Documents

Step 1: Upload File

Step 2: Associate with Dataset

Supported Formats

Link to Model

Best Practices

Specifications

FAQ

What file formats work best?

How often is content re-indexed?

Can I preview what chunks were created?

How do I improve retrieval quality?

What happens if I delete a document?

Chat API

Models

​How RAG Works

​Create Dataset

​Upload Documents

​Step 1: Upload File

​Step 2: Associate with Dataset

​Supported Formats

​Link to Model

​Best Practices

​Specifications

​FAQ

​What file formats work best?

​How often is content re-indexed?

​Can I preview what chunks were created?

​How do I improve retrieval quality?

​What happens if I delete a document?

​Related

Chat API

Models

How RAG Works

Create Dataset

Upload Documents

Step 1: Upload File

Step 2: Associate with Dataset

Supported Formats

Link to Model

Best Practices

Specifications

FAQ

What file formats work best?

How often is content re-indexed?

Can I preview what chunks were created?

How do I improve retrieval quality?

What happens if I delete a document?

Related