Core Concepts of Semantic Caching

# python-examples/simple_cache.py
import os
from dotenv import load_dotenv
from langcache import LangCache

load_dotenv()

# Retrieve LangCache credentials from environment variables
LANGCACHE_API_HOST = os.getenv("LANGCACHE_API_HOST")
LANGCACHE_CACHE_ID = os.getenv("LANGCACHE_CACHE_ID")
LANGCACHE_API_KEY = os.getenv("LANGCACHE_API_KEY")

# Initialize LangCache client
lang_cache = LangCache(
    server_url=f"https://{LANGCACHE_API_HOST}",
    cache_id=LANGCACHE_CACHE_ID,
    api_key=LANGCACHE_API_KEY
)

async def main():
    print("--- LangCache Basic Interaction (Python) ---")

    # 1. Store a prompt and response
    prompt1 = "What is the capital of France?"
    response1 = "Paris"
    print(f"Storing: Prompt='{prompt1}', Response='{response1}'")
    store_key = await lang_cache.set(prompt=prompt1, response=response1)
    print(f"Stored with key: {store_key}")

    # Give a moment for indexing (optional, but good practice for immediate search)
    # In a real app, this might be handled asynchronously by the LangCache service.
    import asyncio
    await asyncio.sleep(1)

    # 2. Search for a semantically similar prompt (Cache Hit expected)
    search_prompt_similar = "Capital city of France?"
    print(f"\nSearching for a similar prompt: '{search_prompt_similar}'")
    search_results_similar = await lang_cache.search(
        prompt=search_prompt_similar,
        similarity_threshold=0.85 # Using the default or a reasonable threshold
    )

    if search_results_similar:
        print(f"Cache Hit! Found response: {search_results_similar[0].response}")
        print(f"Similarity score: {search_results_similar[0].score:.4f}")
    else:
        print("Cache Miss for similar prompt.")

    # 3. Search for a completely different prompt (Cache Miss expected)
    search_prompt_different = "Tell me about quantum physics."
    print(f"\nSearching for a different prompt: '{search_prompt_different}'")
    search_results_different = await lang_cache.search(
        prompt=search_prompt_different,
        similarity_threshold=0.85
    )

    if search_results_different:
        print(f"Cache Hit! Found response: {search_results_different[0].response}")
        print(f"Similarity score: {search_results_different[0].score:.4f}")
    else:
        print("Cache Miss for different prompt. (Expected)")
        # In a real app, you'd call the LLM here and then store the new result.
        response_different = "Quantum physics is the study of matter and energy at the most fundamental level. It aims to describe the properties and behavior of particles like electrons and photons."
        await lang_cache.set(prompt=search_prompt_different, response=response_different)
        print(f"Stored new response for '{search_prompt_different}'.")


if __name__ == "__main__":
    import asyncio
    asyncio.run(main())

// nodejs-examples/simple_cache.js
require('dotenv').config({ path: '../.env' }); // Load .env from parent directory
const { LangCache } = require('@redis-ai/langcache');

// Retrieve LangCache credentials from environment variables
const LANGCACHE_API_HOST = process.env.LANGCACHE_API_HOST;
const LANGCACHE_CACHE_ID = process.env.LANGCACHE_CACHE_ID;
const LANGCACHE_API_KEY = process.env.LANGCACHE_API_KEY;

// Initialize LangCache client
const langCache = new LangCache({
    serverURL: `https://${LANGCACHE_API_HOST}`,
    cacheId: LANGCACHE_CACHE_ID,
    apiKey: LANGCACHE_API_KEY,
});

async function main() {
    console.log("--- LangCache Basic Interaction (Node.js) ---");

    // 1. Store a prompt and response
    const prompt1 = "What is the capital of France?";
    const response1 = "Paris";
    console.log(`Storing: Prompt='${prompt1}', Response='${response1}'`);
    const storeResult = await langCache.set({ prompt: prompt1, response: response1 });
    console.log(`Stored with entry ID: ${storeResult.entryId}`);

    // Give a moment for indexing (optional, but good practice for immediate search)
    await new Promise(resolve => setTimeout(resolve, 1000));

    // 2. Search for a semantically similar prompt (Cache Hit expected)
    const searchPromptSimilar = "Capital city of France?";
    console.log(`\nSearching for a similar prompt: '${searchPromptSimilar}'`);
    const searchResultsSimilar = await langCache.search({
        prompt: searchPromptSimilar,
        similarityThreshold: 0.85 // Using the default or a reasonable threshold
    });

    if (searchResultsSimilar && searchResultsSimilar.results.length > 0) {
        const firstResult = searchResultsSimilar.results[0];
        console.log(`Cache Hit! Found response: ${firstResult.response}`);
        console.log(`Similarity score: ${firstResult.score.toFixed(4)}`);
    } else {
        console.log("Cache Miss for similar prompt.");
    }

    // 3. Search for a completely different prompt (Cache Miss expected)
    const searchPromptDifferent = "Tell me about the theory of relativity.";
    console.log(`\nSearching for a different prompt: '${searchPromptDifferent}'`);
    const searchResultsDifferent = await langCache.search({
        prompt: searchPromptDifferent,
        similarityThreshold: 0.85
    });

    if (searchResultsDifferent && searchResultsDifferent.results.length > 0) {
        const firstResult = searchResultsDifferent.results[0];
        console.log(`Cache Hit! Found response: ${firstResult.response}`);
        console.log(`Similarity score: ${firstResult.score.toFixed(4)}`);
    } else {
        console.log("Cache Miss for different prompt. (Expected)");
        // In a real app, you'd call the LLM here and then store the new result.
        const responseDifferent = "The theory of relativity, developed by Albert Einstein, includes two theories: special relativity and general relativity. Special relativity deals with the relationship between space and time for objects moving at constant speeds, while general relativity extends this to include gravity, describing it as a curvature of spacetime.";
        await langCache.set({ prompt: searchPromptDifferent, response: responseDifferent });
        console.log(`Stored new response for '${searchPromptDifferent}'.`);
    }
}

main().catch(console.error);