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24/7 Managed Support|
YAKKAY Technologies - AI and Automation Solutions
Retrieval-Augmented Generation

Build secure, explainable generative AI applications

Reduce hallucination and make conversational apps more reliable, without compromising data privacy.

rag-implementation.py
import yakkay_rag
from langchain.chains import RetrievalQA

# Initialize YAKKAY RAG Client
client = yakkay_rag.Client(
api_key="yk_***"
)

# Connect to secure private data
vector_store = client.connect(
environment="private-cloud",
collection="enterprise_docs"
)

# Execute reliable semantic search
response = vector_store.query(
"Summarize Q3 performance"
)
print(response.answer)

RAG with YAKKAY

Retrieval-Augmented Generation (RAG) empowers LLMs with external knowledge bases to improve accuracy and relevance. YAKKAY's architecture allows you to scale effortlessly while keeping your data securely isolated within your own environment, ensuring that your GenAI apps are always fed with the most relevant and timely data.

Keep data safe within your own environment

YAKKAY can be self-hosted or we can securely manage it for you within your own VPC environment.

Quickly integrate and test different LLMs

Connect to different LLMs with a single line of code to see exactly what works best for your use case.

Serve the best answers to your users

Retrieve accurate and contextual answers in milliseconds using hybrid search under the hood.

GUIDE

Advanced RAG Techniques

A guide to help improve the performance of your Retrieval-Augmented Generation applications.

YAKKAY LABS

Advanced RAG Techniques

Master semantic search, chunking strategies, and hybrid retrieval for enterprise AI.

The AI database for a new generation of software