A simple HTTP interface for querying and managing your Pinecone vector database. Search through your documents using natural language queries via REST API endpoints.

1. Set Environment Variables

   • PINECONE_KEY: Your Pinecone API key
   • OPENAI_KEY: Your OpenAI API key

2. Use the API

   # Search your documents
   curl -X POST https://your-val-url.web.val.run/query/ \
     -H "Content-Type: application/json" \
     -d '"machine learning applications"'

Find the most relevant documents for your query.

POST /query/

Request Body:

"your search query here"

or

{
  "query": "your search query here"
}

Response:

{
  "matches": [
    {
      "id": "doc-123",
      "score": 0.95,
      "metadata": {
        "text": "Machine learning is transforming..."
      }
    }
  ]
}

Upload new documents to your index.

POST /upsert/

Request Body:

{
  "texts": [
    "First document content",
    "Second document content"
  ]
}

Response:

{
  "success": true,
  "count": 2
}

Remove all documents from your index.

POST /empty/

Response:

{
  "success": true,
  "message": "Index cleared"
}

Check if the service is running and configured properly.

GET /health/

Response:

{
  "status": "healthy",
  "timestamp": "2025-12-20T19:21:17.737Z",
  "config": {
    "pineconeKey": "✓ Set",
    "openaiKey": "✓ Set"
  }
}

const API_URL = 'https://your-val-url.web.val.run';

// Search for documents
async function search(query) {
  const response = await fetch(`${API_URL}/query/`, {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify(query)
  });
  return response.json();
}

// Add new documents
async function addDocuments(texts) {
  const response = await fetch(`${API_URL}/upsert/`, {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({ texts })
  });
  return response.json();
}

// Example usage
const results = await search('artificial intelligence trends');
await addDocuments(['AI is revolutionizing healthcare', 'Machine learning in finance']);

import requests

API_URL = 'https://your-val-url.web.val.run'

# Search documents
def search(query):
    response = requests.post(f'{API_URL}/query/', json=query)
    return response.json()

# Add documents
def add_documents(texts):
    response = requests.post(f'{API_URL}/upsert/', json={'texts': texts})
    return response.json()

# Example usage
results = search('climate change solutions')
add_documents(['Solar energy is becoming more efficient', 'Wind power costs are decreasing'])

# Search
curl -X POST https://your-val-url.web.val.run/query/ \
  -H "Content-Type: application/json" \
  -d '"renewable energy technologies"'

# Add documents
curl -X POST https://your-val-url.web.val.run/upsert/ \
  -H "Content-Type: application/json" \
  -d '{"texts": ["Document 1", "Document 2"]}'

# Clear index
curl -X POST https://your-val-url.web.val.run/empty/

# Health check
curl https://your-val-url.web.val.run/health/

The API returns standard HTTP status codes:

• 200 - Success
• 400 - Bad Request (missing required fields)
• 405 - Method Not Allowed (wrong HTTP method)
• 500 - Server Error (API key issues, service problems)

Error responses include helpful messages:

{
  "error": "Query text is required",
  "hint": "Make sure PINECONE_KEY and OPENAI_KEY environment variables are set"
}

The service uses these default settings:

• Index: wikipedia-simple-text-embedding-ada-002-250k
• Model: text-embedding-ada-002
• Dimensions: 1536
• Results: Top 10 matches per query

To customize these settings, modify the PineconeIndex configuration in the code.