RAG API Documentation

curl -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  https://app.yardee.ai/api/v1/knowledgebases/

// Success Response
{
  "knowledge_bases": [...],
  "count": 5
}

// Error Response
{
  "error": "Invalid API key",
  "details": "The provided API key is not valid or has expired"
}

GET /api/v1/knowledgebases/

curl -H "Authorization: Bearer YOUR_API_KEY" \
  https://app.yardee.ai/api/v1/knowledgebases/

Response:
{
  "knowledge_bases": [
    {
      "id": 1,
      "name": "Product Documentation",
      "description": "API-accessible product docs",
      "api_enabled": true,
      "created_at": "2025-01-10T09:00:00Z"
    }
  ],
  "count": 1
}

POST /api/v1/knowledgebases/

curl -X POST https://app.yardee.ai/api/v1/knowledgebases/ \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type": "application/json" \
  -d '{
    "name": "Customer Support KB",
    "description": "Knowledge base for customer support queries"
  }'

Response:
{
  "id": 2,
  "name": "Customer Support KB",
  "description": "Knowledge base for customer support queries",
  "api_enabled": true,
  "created_at": "2025-01-20T10:30:00Z"
}

PUT /api/v1/knowledgebases/{id}/

curl -X PUT https://app.yardee.ai/api/v1/knowledgebases/2/ \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type": "application/json" \
  -d '{
    "name": "Updated KB Name",
    "description": "Updated description"
  }'

POST /api/v1/knowledgebases/{id}/search/

curl -X POST https://app.yardee.ai/api/v1/knowledgebases/1/search/ \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "What is your return policy?",
    "top_k": 5,
    "chat_history": [
      {"role": "user", "content": "Do you have a policy for returns?"},
      {"role": "assistant", "content": "Yes, we do. What would you like to know?"}
    ]
  }'

Response:
{
  "query": "What was the total sales for the North region?",
  "knowledge_base_id": 1,
  "total_results": 2,
  "results": [
    {
      "chunk": null,
      "content": "The total sales for the North region was $150,000.",
      "similarity_score": null,
      "document_title": "sales_data.csv",
      "metadata": {
        "type": "agentic_search",
        "source": "sales_data.csv"
      }
    },
    {
      "chunk": {
        "id": 123,
        "content": "Our return policy allows returns within 30 days...",
        "chunk_index": 0,
        "document_id": 45
      },
      "content": "Our return policy allows returns within 30 days...",
      "similarity_score": 0.8945,
      "document_title": "Return Policy Guide.pdf",
      "metadata": {"page": 1, "section": "returns"}
    }
  ],
  "parameters": {
    "top_k": 5,
    "similarity_threshold": 0.1,
    "use_mmr": true
  }
}

{
  "query": "customer support escalation process",
  "top_k": 10,                    // Number of results (default: 5)
  "similarity_threshold": 0.3,    // Minimum similarity score (default: 0.1)
  "use_mmr": true,               // Use MMR for diversity (default: true)
  "metadata_filters": {          // Filter by document metadata
    "department": "support",
    "status": "active"
  },
  "chat_history": [              // Optional: For structured data queries only
    {"role": "user", "content": "Show me sales data"},
    {"role": "assistant", "content": "Here's the sales information..."},
    {"role": "user", "content": "What about the top performers?"}
  ]
}

// Querying a CSV for specific data
{
  "query": "How many users signed up in May?",
  "top_k": 1
}

// Agentic Search Response
{
  "query": "How many users signed up in May?",
  "total_results": 1,
  "results": [
    {
      "chunk": null,
      "content": "There were 1,234 users who signed up in May.",
      "document_title": "user_signups.csv",
      "metadata": { "type": "agentic_search", "source": "user_signups.csv" }
    }
  ]
}

POST /api/v1/knowledgebases/{id}/documents/upload/

curl -X POST https://app.yardee.ai/api/v1/knowledgebases/1/documents/upload/ \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -F "file=@support_guide.pdf"

Response:
{
  "id": 67,
  "filename": "support_guide.pdf",
  "file_size_display": "2.5 MB",
  "uploaded_at": "2025-01-20T15:45:00Z",
  "processing_status": "processing",
  "chunk_count": 0,
  "uploaded_by_email": "user@company.com"
}

GET /api/v1/knowledgebases/{id}/documents/

curl -H "Authorization: Bearer YOUR_API_KEY" \
  https://app.yardee.ai/api/v1/knowledgebases/1/documents/

Response:
{
  "documents": [
    {
      "id": 67,
      "filename": "support_guide.pdf",
      "file_size_display": "2.5 MB",
      "uploaded_at": "2025-01-20T15:45:00Z",
      "processing_status": "completed",
      "chunk_count": 45,
      "uploaded_by_email": "user@company.com",
      "folder_path": "/documents/"
    }
  ],
  "count": 1
}

DELETE /api/v1/documents/{id}/

curl -X DELETE https://app.yardee.ai/api/v1/documents/67/ \
  -H "Authorization: Bearer YOUR_API_KEY"

Response: 204 No Content

import requests

class YardeeVectorAPI:
    def __init__(self, api_key):
        self.api_key = api_key
        self.base_url = "https://app.yardee.ai/api/v1"
        self.headers = {
            "Authorization": f"Bearer {api_key}",
            "Content-Type": "application/json"
        }
    
    def search(self, kb_id, query, top_k=5, chat_history=None):
        url = f"{self.base_url}/knowledgebases/{kb_id}/search/"
        payload = {"query": query, "top_k": top_k}
        if chat_history:
            payload["chat_history"] = chat_history
        
        response = requests.post(url, json=payload, headers=self.headers)
        response.raise_for_status()
        return response.json()
    
    def upload_document(self, kb_id, file_path):
        url = f"{self.base_url}/knowledgebases/{kb_id}/documents/upload/"
        
        with open(file_path, 'rb') as f:
            files = {'file': f}
            
            response = requests.post(url, files=files, 
                                   headers={"Authorization": f"Bearer {self.api_key}"})
        
        response.raise_for_status()
        return response.json()

# Usage
api = YardeeVectorAPI("sk-your-api-key-here")

# Basic search
results = api.search(kb_id=1, query="How do I reset my password?")
print(f"Found {len(results['results'])} relevant chunks")

# Search with conversation history (structured data only)
chat_history = [
    {"role": "user", "content": "Show me Q1 sales data"},
    {"role": "assistant", "content": "Here's the Q1 sales from your CSV..."}
]
results = api.search(kb_id=1, query="Q2 comparison", chat_history=chat_history)
print(f"Found {results['total_results']} contextual results")

class YardeeVectorAPI {
  constructor(apiKey) {
    this.apiKey = apiKey;
    this.baseUrl = 'https://app.yardee.ai/api/v1';
  }

  async search(kbId, query, topK = 5, chatHistory = null) {
    const url = `${this.baseUrl}/knowledgebases/${kbId}/search/`;
    const payload = { query, top_k: topK };
    if (chatHistory) {
      payload.chat_history = chatHistory;
    }

    const response = await fetch(url, {
      method: 'POST',
      headers: {
        'Authorization': `Bearer ${this.apiKey}`,
        'Content-Type': 'application/json',
      },
      body: JSON.stringify(payload),
    });

    if (!response.ok) {
      throw new Error(`API Error: ${response.status}`);
    }

    return response.json();
  }

  async uploadDocument(kbId, file) {
    const formData = new FormData();
    formData.append("file", file);

    const response = await fetch(`${this.baseUrl}/knowledgebases/${kbId}/documents/upload/`, {
      method: 'POST',
      headers: {
        'Authorization': `Bearer ${this.apiKey}`,
      },
      body: formData,
    });

    return response.json();
  }
}

// Usage
const api = new YardeeVectorAPI("sk-your-api-key-here");

// Basic search
const results = await api.search(1, "What are your business hours?");
console.log(`Found ${results.total_results} results`);

// Search with conversation context (structured data only)
const chatHistory = [
  { role: "user", content: "Show me revenue data" },
  { role: "assistant", content: "Here's the revenue from your data..." }
];
const contextualResults = await api.search(1, "profit margins", 5, chatHistory);
console.log(`Found ${contextualResults.total_results} contextual results`);