The Ultimate Guide to OpenAI RAG (Performance, Costs, & More)

Interested in learning about OpenAI RAG? This guide covers all aspects of this tool, from its performance capabilities to its associated costs.

May 25, 2024

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The Ultimate Guide to OpenAI RAG (Performance, Costs, & More)

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OpenAI RAG is revolutionizing content generation with its incredible capabilities through advanced AI models. This technology, also known as Retrieval Augmented Generation, combines the best of two worlds – deep learning and information retrieval – to deliver high-quality, contextually relevant content. The power of OpenAI RAG lies in its ability to provide comprehensive and detailed answers to questions, making it a game-changer in the AI and content creation landscape. This blog will delve deep into the workings of OpenAI RAG, its benefits, and its future potential. Let's explore this cutting-edge technology together!

What Is Retrieval Augmented Generation?

Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language model, so it references an authoritative knowledge base outside of its training data sources before generating a response. Large Language Models (LLMs) are trained on vast volumes of data and use billions of parameters to generate original output for tasks like answering questions, translating languages, and completing sentences.

RAG extends LLMs' already powerful capabilities to specific domains or an organization's internal knowledge base without retraining the model. It is a cost-effective approach to improving LLM output so it remains relevant, accurate, and useful in various contexts.

Why is Retrieval-Augmented Generation Important?

LLMs are a key artificial intelligence (AI) technology powering intelligent chatbots and other natural language processing (NLP) applications. The goal is to create bots that can answer user questions in various contexts by cross-referencing authoritative knowledge sources. Unfortunately, the nature of LLM technology introduces unpredictability in LLM responses. LLM training data is static and introduces a cut-off date on its knowledge.

Known challenges of LLMs include

Presenting false information when it does not have the answer.

Presenting out-of-date or generic information when the user expects a specific, current response.

Creating a response from non-authoritative sources.

This can lead to inaccurate responses due to terminology confusion, wherein different training sources use the same terminology to talk about different things.

You can think of the Large Language Model as an overenthusiastic new employee who refuses to stay informed about current events but will always answer every question with absolute confidence. Unfortunately, such an attitude can negatively impact user trust, and it is not something you want your chatbots to emulate!

RAG is one approach to solving some of these challenges. It redirects the LLM to retrieve relevant information from authoritative, pre-determined knowledge sources. Organizations have greater control over the generated text output, and users gain insights into how the LLM generates the response.

What are the Benefits of Retrieval-Augmented Generation?

RAG technology brings several benefits to an organization's generative AI efforts.

Cost-effective Implementation

Chatbot development typically begins using a foundation model. Foundation models (FMs) are API-accessible LLMs trained on a broad spectrum of generalized and unlabeled data. The computational and financial costs of retraining FMs for organization or domain-specific information are high. RAG is a more cost-effective approach to introducing new data to the LLM. It makes generative artificial intelligence (generative AI) technology more broadly accessible and usable.

Current Information

Even if the original training data sources for an LLM are suitable for your needs, maintaining relevancy can be challenging. RAG allows developers to provide the latest research, statistics, or news to the generative models. They can use RAG to connect the LLM directly to live social media feeds, news sites, or other frequently updated information sources. The LLM can then provide the latest information to the users.

Enhanced User Trust

RAG allows the LLM to present accurate information with source attribution. The output can include citations or references to sources. Users can also look up source documents if they require further clarification or more detail. This can increase trust and confidence in your generative AI solution.

More Developer Control

With RAG, developers can test and improve their chat applications more efficiently. They can control and change the LLM's information sources to adapt to changing requirements or cross-functional usage. Developers can also restrict sensitive information retrieval to different authorization levels and ensure the LLM generates appropriate responses. In addition, they can also troubleshoot and make fixes if the LLM references incorrect information sources for specific questions. Organizations can implement generative AI technology more confidently for broader applications.

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How Does Rag Work

Rag Llm

Rag Pipeline

Rag Rating

Rag Workflow

What Is OpenAI RAG API?

In the field, let me introduce you to OpenAI's Retrieval Augmented Generation (RAG) model and API. This innovative system combines the best of both worlds - a generator and a retriever working together seamlessly. The retrieval part is crucial, as it provides the generator with context by fetching and selecting relevant information from a knowledge base index. This architecture is beautiful in its simplicity yet powerful in its execution.

The retriever acts as a guide, supplying the generator with the necessary background information to craft well-informed responses. This context is key to ensuring coherent and accurate output. The generator then takes this information and formulates a response, whether it be in the form of text, code, or any other desired output. The collaboration of these two components results in a comprehensive and intelligent system that can be applied to a wide range of tasks and use cases.

5 Industry Use-Cases of OpenAI RAG

1. Development Support: Utilizing the Code Interpreter

One of the key features of the OpenAI RAG is its ability to serve as an on-demand coding assistant. This is particularly beneficial for software development teams or educational settings using multiple programming languages. As an AI language model, RAG can translate code snippets from one programming language to another, helping developers understand new languages and easing the learning curve. This feature is invaluable for assisting programmers in writing code in various languages efficiently and accurately.

2. Enterprise Knowledge Management: Centralized Knowledge Repository

Another practical application of OpenAI RAG is in enterprise knowledge management. Organizations can create a centralized knowledge repository by uploading and processing internal documents, reports, and manuals. Employees can then query the AI assistant to access specific information quickly and effectively. This feature enhances productivity by reducing the time spent searching for information in extensive document sets. It also ensures that employees can access accurate and up-to-date information at their fingertips.

3. Customer Support Automation: Enhancing Customer Experience

OpenAI RAG can be integrated into customer support systems to automate responses to common queries. The AI assistant can quickly and accurately respond to customer inquiries by fetching information from external APIs and databases. This automation feature not only enhancesthe customer experience by providing immediate responses but also reduces the workload on human support staff. Ultimately, this integration streamlines customer support processes and improves overall service quality.

4. Data Analysis: Generating Reports and Manipulating Data

Through its data analysis functions, users can ask OpenAI RAG to perform complex data manipulations and generate reports. By translating natural language queries into structured data analysis tasks, the AI assistant simplifies the data analysis process. This capability is particularly useful for researchers, analysts, and data scientists who need to extract insights from large datasets quickly and efficiently.

5. IT Operation Automation: Streamlining Routine Tasks

IT teams can leverage OpenAI RAG to automate routine operational tasks. By defining functions corresponding to common IT tasks such as system diagnostics, network checks, or software updates, the AI assistant can execute these tasks in response to user commands. This feature significantly reduces the time IT professionals spend on routine maintenance, allowing them to focus on more complex and critical issues within the organization.

Simple Step-By-Step Guide on Getting Started With OpenAI RAG

Creating an OpenAI Platform Account

Creating an account on the OpenAI platform is the first step in using the RAG API. You can easily create an account by following the prompts on the platform.

Retrieving Your API Key

After setting up your account, you must retrieve your API key. This key is crucial for interacting with the API. Access the API keys page on your OpenAI account and create an API key. To copy and securely store this key.

Installing the OpenAI Python Library

To access the OpenAI API from your local machine, install the OpenAI Python library. Use the following command to install the library via pip:

```bash

pip install openai

```

Making Your First API Call

Once your account is set up, and you have installed the OpenAI Python library, you can proceed to make your first API call. The code snippet below showcases how to retrieve a chat completion using the ChatCompletion API:

```python

import openai

def get_chat_completion(prompt, model="gpt-3.5-turbo"):

# Creating a message as required by the API

messages = [{"role": "user", "content": prompt}]

Calling the ChatCompletion API

response = openai.ChatCompletion.create(

model=model,

messages=messages,

temperature=0,

)

Returning the extracted response

return response.choices[0].message["content"]

response = get_chat_completion("Translate into Spanish: As a beginner data scientist, I'm excited to learn about OpenAI API!")

print(response)

```

Exploring Further

After making your first API call successfully, there are further steps to explore the capabilities of the OpenAI RAG API. These steps include

Experimenting with different engines and prompts

Exploring different parameters to understand the API responses.

Delving into the OpenAI documentation to uncover more about the API functionalities.

What Is RAG LLM

RAG LLM Meaning

Rag Model Llm

Rag Use Case

Llm Tech Stack

Rag Fine Tuning

Rag Nlp

Rag Api

Rag Stack

Rag Systems

Rag Evaluation

Rag Service

Rag Use Cases

Rag Software

RAG Architecture LLM

LLM Rag Meaning

RAG LLM Example

OpenAI RAG Performance Gaps and Cost Concerns

OpenAI RAG's pricing model may be justifiable for B2C businesses, as a few GBs of data costing tens of dollars per month is unlikely to be a significant issue for individual users. For B2B businesses dealing with large-scale data, this cost could significantly erode business revenue or even surpass the business's value. For example, creating personalized customer service or an intelligent search system for patents and legal documents could become prohibitively expensive.

Traditional document services; $6 /TB

OpenAI Assistants; $6,144/TB

Costs Analysis Conclusion

OpenAI's new Assistants feature is costing the company more money than it's making due to the high server cost of $0.3/GB/day, compared to the pricing of $0.2/GB/day. Developers who want to use the Assistants must pay over 1,000 times more than traditional document services. Therefore, they must generate business value that's three orders of magnitude higher than conventional document services to justify the cost.

Digging into OpenAI RAG's Solution

Let's pick apart OpenAI RAG's current scheme. Here is some publicly available information:

A maximum of 20 files per assistant

A cap of 512MB per file

A hidden limitation of 2 million tokens per file, uncovered during testing

Only text is supported

Dissecting the OpenAI RAG's Retrieval Service

OpenAI has a substantial user base, which requires the company to maintain a stable system and effectively manage the impact of any disasters. Therefore, during the initial phases of OpenAI RAG's development, they are unlikely to opt for a super-large cluster solution. Instead, OpenAI would likely create a system where each group of users can share a minor vector database instance for better stability.

This architecture seems adequate for supporting trial users. Each physical node can store up to 20 GB of vectors and indices for paying customers, corresponding to roughly 8 GB of original text. Under total capacity, the daily revenue potential is a modest $1.6, which is notably low.

Lack of Customization

While OpenAI's Retrieval offers a convenient out-of-the-box solution, it cannot consistently align with every application's specific needs, especially regarding latency and search algorithm customization. Utilizing a third-party vector database grants developers the flexibility to optimize and configure the retrieval process, catering to production needs and enhancing overall efficiency

Lack of Multi-tenancy

Retrieval is a built-in feature in OpenAI RAG that only supports individual user usage. If you are a developer aiming to serve millions of users with both shared documents and user's private information, the built-in retrieval feature cannot help. Replicating shared documents to each user's Assistant escalates storage costs, while having all users share the same Assistant poses challenges in supporting user-specific private documents.

Use ChatBees’ Serverless LLM to 10x Internal Operations

OpenAI's RAG model is a powerful tool for natural language understanding, but it requires optimization for specific applications. ChatBees specializes in tailoring RAG for internal operations, such as customer support and employee assistance. Our agentic framework automates the selection of optimal strategies to enhance response quality, boosting predictability and accuracy to handle increased query volumes. This optimization enables teams to operate more efficiently and effectively.

The Innovation of Serverless RAG

Serverless RAG introduces a streamlined approach to utilizing RAG's capabilities. It offers simple, secure, and high-performance APIs that seamlessly connect various data sources like PDFs, CSVs, websites, Google Drive, Notion, and Confluence. This integration facilitates quick search, chat, and summarization with the knowledge base without the need for DevOps expertise for deployment and maintenance. The ease of use and accessibility of Serverless RAG drastically improves onboarding processes and sales enablement, enhances customer support, and fosters collaboration in product and engineering teams.

Benefits of Using ChatBees for Internal Operations

ChatBees' optimization of RAG empowers internal operations across diverse industries. The benefits are manifold, from onboarding processes to sales enablement, customer support to product and engineering collaboration. The platform allows quick access to essential materials and resources, facilitates efficient customer interactions, and enhances team productivity by providing rapid access to project data, bug reports, discussions, and resources. By integrating ChatBees into their workflows, businesses can significantly improve their internal operational efficiency.

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