Knowledge Base

Enhance your agents with document-based knowledge using Retrieval-Augmented Generation (RAG).

Stable

Overview

The Knowledge Base allows you to:

Upload documents (PDF, TXT, MD, etc.)
Automatically chunk and embed content
Query semantically for relevant context
Connect to agents for enhanced responses

Quick Start

Dashboard

Navigate to Dashboard → Knowledge for the visual interface.

API

cURL


# Upload a document
curl -X POST "https://cloud.milady.ai/api/v1/knowledge/upload-file" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -F "file=@document.pdf" \
  -F "agentId=agent_abc123"

JavaScript


const formData = new FormData();
formData.append('file', file);
formData.append('agentId', 'agent_abc123');
 
const response = await fetch('https://cloud.milady.ai/api/v1/knowledge/upload-file', {
method: 'POST',
headers: {
'Authorization': 'Bearer YOUR_API_KEY',
},
body: formData,
});
 
const result = await response.json();
console.log('Document uploaded:', result.id);

Python


import requests
 
with open('document.pdf', 'rb') as f:
    response = requests.post(
        'https://cloud.milady.ai/api/v1/knowledge/upload-file',
        headers={'Authorization': 'Bearer YOUR_API_KEY'},
        files={'file': f},
        data={'agentId': 'agent_abc123'},
    )
 
result = response.json()
print(f'Document uploaded: {result["id"]}')

Supported Formats

Format	Extension	Max Size
PDF	`.pdf`	5MB
Text	`.txt`	5MB
Markdown	`.md`	5MB
Word	`.docx`	5MB
JSON	`.json`	5MB
XML	`.xml`	5MB
YAML	`.yaml`	5MB
CSV	`.csv`	5MB

Note: Maximum 5MB per file and 5MB total per batch upload.

Upload Documents

Prepare Your Documents

Ensure documents are in a supported format and within size limits.

Upload via Dashboard or API

Use the Knowledge dashboard or the upload API endpoint.

Wait for Processing

Documents are automatically chunked and embedded.

Connect to Agent

Link the knowledge base to your agent for RAG.

Upload Response


{
  "id": "doc_abc123",
  "filename": "product-manual.pdf",
  "status": "processing",
  "chunks": null,
  "createdAt": "2024-01-15T10:30:00Z"
}

Check Processing Status


curl -X GET "https://cloud.milady.ai/api/v1/knowledge/check?documentId=doc_abc123" \
  -H "Authorization: Bearer YOUR_API_KEY"


{
  "id": "doc_abc123",
  "status": "completed",
  "chunks": 42,
  "tokensUsed": 15234
}

Query Knowledge

Search your knowledge base semantically:


curl -X POST "https://cloud.milady.ai/api/v1/knowledge/query" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "How do I reset my password?",
    "agentId": "agent_abc123",
    "limit": 5
  }'

Response


{
  "results": [
    {
      "content": "To reset your password, navigate to Settings > Security > Reset Password...",
      "score": 0.92,
      "documentId": "doc_abc123",
      "metadata": {
        "page": 15,
        "section": "Account Settings"
      }
    },
    {
      "content": "Password requirements include at least 8 characters...",
      "score": 0.85,
      "documentId": "doc_abc123",
      "metadata": {
        "page": 16,
        "section": "Security"
      }
    }
  ]
}

Managing Documents

List Documents


curl -X GET "https://cloud.milady.ai/api/v1/knowledge?agentId=agent_abc123" \
  -H "Authorization: Bearer YOUR_API_KEY"

Get Document Details


curl -X GET "https://cloud.milady.ai/api/v1/knowledge/doc_abc123" \
  -H "Authorization: Bearer YOUR_API_KEY"

Delete Document


curl -X DELETE "https://cloud.milady.ai/api/v1/knowledge/doc_abc123" \
  -H "Authorization: Bearer YOUR_API_KEY"

RAG Configuration

Chunk Settings

Configure how documents are split:

Setting	Default	Description
`chunkSize`	1000	Characters per chunk
`chunkOverlap`	200	Overlap between chunks
`minChunkSize`	100	Minimum chunk size

Retrieval Settings

Configure query behavior:

Setting	Default	Description
`limit`	5	Number of results
`minScore`	0.7	Minimum relevance score
`rerank`	true	Re-rank results for relevance

Agent Integration

Automatic RAG

When knowledge is connected to an agent, relevant context is automatically retrieved:


// Agent with knowledge base
const response = await fetch("https://cloud.milady.ai/api/v1/chat/completions", {
  method: "POST",
  headers: {
    Authorization: "Bearer YOUR_API_KEY",
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    model: "agent_abc123", // Agent with connected knowledge
    messages: [{ role: "user", content: "How do I reset my password?" }],
  }),
});

The agent automatically queries the knowledge base and includes relevant context in its response.

Manual RAG

Query knowledge and inject context manually:


// 1. Query knowledge
const knowledge = await fetch("https://cloud.milady.ai/api/v1/knowledge/query", {
  method: "POST",
  headers: {
    Authorization: "Bearer YOUR_API_KEY",
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    query: "password reset",
    limit: 3,
  }),
}).then((r) => r.json());
 
// 2. Include in chat
const context = knowledge.results.map((r) => r.content).join("\n\n");
 
const response = await fetch("https://cloud.milady.ai/api/v1/chat/completions", {
  method: "POST",
  headers: {
    Authorization: "Bearer YOUR_API_KEY",
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    model: "gpt-4o",
    messages: [
      { role: "system", content: `Use this context:\n\n${context}` },
      { role: "user", content: "How do I reset my password?" },
    ],
  }),
});

Pricing

See Billing & Credits for current pricing on document uploads, embeddings, queries, and storage.

Best Practices

Quality Content — Use well-structured, accurate documents
Organize — Group related documents for better retrieval
Update Regularly — Keep knowledge current by refreshing documents
Test Queries — Verify retrieval quality before production

Next Steps

Create agents that use knowledge

AI Agents

Integrate RAG into your applications

Chat API

Learn about vector embeddings

Embeddings