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Quick Start Guide

Get started with Vectorcache in under 5 minutes.

Step 1: Create an Account

  1. Visit https://app.vectorcache.com
  2. Sign up with your email or GitHub account
  3. Verify your email address

Step 2: Create a Project

  1. Log in to your dashboard
  2. Click "Create New Project"
  3. Give your project a name (e.g., "My Chatbot")
  4. Click "Create Project"

Step 3: Add Your LLM API Keys

Before caching can work, Vectorcache needs your LLM provider API keys:

  1. Navigate to Settings → LLM Keys
  2. Click "Add LLM Key"
  3. Select your provider (OpenAI, Anthropic, etc.)
  4. Enter your API key
  5. Give it a name for reference

Security

Your LLM API keys are encrypted at rest and never exposed in logs or responses.

Step 4: Get Your Vectorcache API Key

  1. Go to your project dashboard
  2. Navigate to the API Keys tab
  3. Click "Create API Key"
  4. Give it a name (e.g., "Production Key")
  5. Copy the API key immediately - you won't see it again!

Step 5: Install the SDK

npm install vectorcache

pip install vectorcache-python

Step 6: Make Your First Request

import { VectorcacheClient } from 'vectorcache';

const client = new VectorcacheClient({
  apiKey: 'your_api_key_here',
  baseUrl: 'https://api.vectorcache.ai'
});

async function main() {
  const result = await client.query({
    prompt: 'Explain quantum computing in simple terms',
    context: 'Educational content for beginners',
    model: 'gpt-4o',
    similarityThreshold: 0.85
  });

  console.log(`Cache hit: ${result.cache_hit}`);
  console.log(`Response: ${result.response}`);

  if (result.cost_saved) {
    console.log(`Cost saved: $${result.cost_saved}`);
  }
}

main();

import requests

api_key = "your_api_key_here"
base_url = "https://api.vectorcache.ai"

headers = {
    "Authorization": f"Bearer {api_key}",
    "Content-Type": "application/json"
}

data = {
    "prompt": "Explain quantum computing in simple terms",
    "context": "Educational content for beginners",
    "model": "gpt-4o",
    "similarity_threshold": 0.85
}

response = requests.post(
    f"{base_url}/v1/cache/query",
    json=data,
    headers=headers
)

result = response.json()

print(f"Cache hit: {result['cache_hit']}")
print(f"Response: {result['response']}")

if result.get('cost_saved'):
    print(f"Cost saved: ${result['cost_saved']}")

Understanding the Response

Your first request will be a cache miss since nothing is cached yet:

{
  "cache_hit": false,
  "response": "Quantum computing is...",
  "similarity_score": null,
  "cost_saved": 0,
  "llm_provider": "openai"
}

The second request with a similar prompt will be a cache hit:

{
  "cache_hit": true,
  "response": "Quantum computing is...",
  "similarity_score": 0.92,
  "cost_saved": 0.003,
  "llm_provider": "cache"
}

Next Steps

✅ You're now caching LLM responses!

Here's what to explore next:

Troubleshooting

Cache Always Missing

  • Ensure similarity_threshold is not too high (try 0.80-0.85)
  • Verify your prompts are actually similar
  • Check that you're using the same model parameter

Authentication Errors

  • Confirm your API key is correctly formatted
  • Check that the key hasn't been revoked
  • Ensure you're using the correct project API key

LLM API Errors

  • Verify your LLM API keys are valid in Settings → LLM Keys
  • Check that you have sufficient credits with your LLM provider
  • Ensure the model parameter matches your provider (e.g., 'gpt-4o' for OpenAI)

Need more help? Contact support