Launch your RAG powered ChatBot in Minutes Using MonsterAPI's no-code platform

Retrieval-Augmented Generation (RAG) and businesses are a match made in heaven. ??

RAG is a technique for enhancing the accuracy and reliability of Large Language Models with facts fetched from external sources. In other words, with RAG, an LLM references an authoritative knowledge base outside of its training data sources before generating a response.

Over the recent past, algorithmic advancements like replacing positional encoding with ALiBi, Sparse attention and Flash Attention-2 have come up for extending context-windows of LLMs, which many suspected will reduce the need for RAG. But in actuality, the importance and industry's interest of RAG has only hugely increased.

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? Now we can effortlessly launch our RAG powered ChatBot in Minutes Using MonsterAPI: No Coding Needed!

?? Forget the hassle of configuring GPUs and prepping your environment.

Its all taken care of by Monster API . Its simplified integration of LLM deployment with RAG-pipeline and chat User interface, allows for seamless user interaction with your own documents or knowledge-base.

The image below shows the workflow of RAG Bot having MonsterDeploy in the backend.

As a first step to build the RAG pipeline, Sign up for a Monster API account (monsterapi.ai/login) and get 2500 free credits.

?? Then deploy a latest open source LLM such as Mistral 7B v0.2 or Llama 7B with Monster Deploy's one-click solution.

?? Once the deployment is live, you'll get an LLM API endpoint ready to handle queries. This Rest API endpoint can be integrated into any public web or mobile application.

?? And now, you'll receive an authentication token and URL to access the LLM endpoints. Copy the endpoint URL and authentication key

?? Paste these values into MonsterAPI's ChainLit UI-interface to set up your ChatBot along with RAG and then you are good to go.

?? MonsterAPI's LLM API endpoints are pre-integrated with LlamaIndex framework, thus providing direct support for RAG pipelines.

?? This optimizes data loading and indexing, allowing efficient parsing of large document contexts. The system then sends this context to query your deployed LLM endpoints, ensuring seamless data retrieval and indexing

?? Just copy and paste the API endpoint and auth token of your deployed LLM in MonsterAPI's Chainlit Chat UI for immediate use.

?? Checkout this video for quick walkthrough

? Some of the key advantages of deploying a private LLM endpoint on MonsterAPI

?? Enhanced Security: Keep your model and data secure within your private endpoint, accessible only through your deployment’s auth key.

?? Cost-Effectiveness: MonsterAPI is one of the most cost-effective solutions for deploying and managing LLMs by integrating their affordable GPU cloud optimised for higher throughput (vllm in the backend).

?? Scalability: Your LLM deployments automatically scale bi-directionally on demand, ensuring optimal performance during peak usage.

?? Customization: Tailor your LLM deployment to specific requirements, including GPU and RAM configurations. Start serving text generation requests using models like Llama2, CodeLlama, Falcon 40B or any of your custom/finetuned models.

?? Advanced Monitoring: Gain insights into LLM performance and usage metrics with MonsterAPI's comprehensive monitoring and analytics features.

?? Fine-tuned LLM Deployments: Monster Deploy enables you to deploy fine-tuned LLMs as API endpoints in one click. Thus reducing the need to set up complex GPU servers for fine-tuning and deploying LLMs at scale.

Check out the detailed official blog on How to "Build a Retrieval-Augmented Generation ChatBot in 10 Minutes using MonsterAPI"

Thats a wrap - all the important links are below

After you've signed up on MonsterAPI, apply for Deploy beta access here - https://forms.gle/2vdzBca3B9qWqXXZ6

And get 2500 Free trial credits.

Checkout their API Docs - https://developer.monsterapi.ai/docs/monster-deploy-beta

?? Discord (Monsterapis) : https://discord.com/invite/mVXfag4kZN