Stop building dumb chatbots: The RAG + Scality RING solution

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This blog serves as a how-to guide for building an intelligent chatbot. Whether your goal is implementing a chatbot for customer support, research assistance, or enterprise intelligence, Scality RING architecture ensures you’re equipped with a solution that’s scalable, reliable, and AI-ready. Scality RING S3-compatible object storage isn’t just about storing data—it’s also the backbone for AI-driven applications. By integrating RING with vector databases like Milvus, AI frameworks like LangChain, and fine-tuned models like GPT-3.5, you can unlock the full potential of RAG workflows for intelligent chatbots.

What is RAG, and why does it matter?

Retrieval-augmented generation (RAG) is a technique that delivers better generative AI results by enabling companies to automatically provide the most current and relevant proprietary data to an existing LLM. In traditional chatbots, responses rely heavily on static pre-trained LLM models or keyword-based searches, often resulting in outdated, vague, or irrelevant answers. RAG changes this by introducing a dynamic retrieval layer to bring live, relevant data into the conversation. This produces higher-quality results and enhanced customer experiences by ensuring AI models draw from the most relevant, trusted data available.

How RAG works and why it is transformative

RAG and object storage supercharge chatbot intelligence. Object storage architecture integrates with RAG, providing a transformative AI architecture workflow that combines real-time data retrieval with generative AI to deliver accurate, relevant, and context-aware responses.

How it works:

• Real-time retrieval: The RAG workflow leverages Langchain AI to retrieve the latest, most relevant data from storage systems like S3-compatible Scality RING, which creates embeddings and stores in vector databases such as Milvus. This ensures the chatbot is always working with updated and domain-specific information.
• Context-aware augmentation: RAG combines the user’s query with retrieved data through the framework by Langchain AI to pass this enriched context to a generative AI model, such as GPT-3.5, creating precise, personalized responses.

Why RAG is transformative:

• Up-to-date knowledge: Chatbots powered by RAG no longer rely on static, pre-trained knowledge. They provide real-time responses by retrieving live data.
• Meaningful understanding: RAG enables semantic retrieval, ensuring responses align with the query’s intent, not just its keywords.

Why is object storage ideal for AI workflows?

No other storage solution addresses the complex needs of demanding AI workflows like object storage. It offers massive scalability, seamlessly handling everything from terabytes to petabytes of data, and supports the diverse data landscape of AI, including structured, semi-structured, and unstructured data like images, videos, audio files, documents, text, and log files — all within a single, unified system. 

Object storage also ensures data integrity and security through immutability and protection against corruption or unauthorized access. As the foundation for data lakes, object storage enables efficient storage, management, and retrieval of extensive datasets essential for AI model training and analytics. Optimized for high performance, it accelerates data access and retrieval, which is crucial for AI model training and inference.

In fact, analysts have recently been touting the virtues of object storage as a powerhouse for AI workloads. Among them, principal analyst at ESG states in Tech Target:
“…this is a good time for many organizations to take a fresh look at where object storage could play, especially if they are looking to modernize their overall data and storage architecture for their large-scale data lakes, analytics, or AI initiatives”.

Exploring critical technology components in the RAG workflow with Scality RING

To build a highly intelligent chatbot with RAG and Scality RING, we recommend this powerful AI technology stack. By leveraging real-time, domain-specific data, this combination ensures more informative, insightful, and relevant chatbot interactions. Together, these components transform raw data into actionable intelligence:

• Milvus vector database
• LangChain AI framework
• *GPT-3.5 fine-tuned language model 

*Note: GPT 3.5 was chosen because it’s complex enough to respond to queries based on the test data. However, you can use later models such as GPT 4-0, 0-1, 0-1 mini, LLAMA or any other models that suit your workload.

Milvus Vector Database: Why it’s essential and how it complements Scality RING

In RAG workflows, vector databases are indispensable for semantic retrieval. Unlike traditional databases, vector databases like Milvus store and search high-dimensional data representations (embeddings) that capture the meaning behind text, enabling intelligent and context-aware retrieval.

Key strengths of vector databases:

Semantic search: Vector embeddings represent data in a way that captures its meaning, not just its literal terms. This enables the system to retrieve content that matches the user query semantically.

Example:

• Query: “How to manage type 2 diabetes?”
• Result: Retrieves documents on “insulin sensitivity” or “low-GI diets,” even if the phrase “manage type 2 diabetes” isn’t present.

Scalability for growth: Vector databases handle massive datasets without performance degradation, making them ideal for dynamic and ever-growing knowledge bases.

Benefits of Milvus vector database:

When evaluating vector databases for our RAG architecture, Milvus stood out due to its exceptional performance and seamless integration with other components of the pipeline.

1. Millisecond query speeds: Maintains fast and accurate real-time data retrieval even under heavy workloads
2. Precision across diverse data: Delivers contextually relevant results and semantic search for varied datasets
3. Scalable architecture: Accommodates millions of embeddings, making it a future-proof solution for applications requiring constant knowledge-base expansion
4. Seamless ecosystem integration: Provides direct integration with LangChain, making it simple to connect embeddings to the RAG pipeline and ensures compatibility with Scality RING S3-compatible storage

The power of combining Milvus and Scality RING S3-compatible object storage

By combining Milvus and Scality RING, you eliminate the gap between raw data storage and intelligent retrieval. Each component complements the other:

1. S3-compatible RING: Acts as the backbone for raw data storage, ensuring reliability, scalability, and efficient access to documents, research papers, or structured files
2. Milvus: Brings intelligent retrieval capabilities through semantic search, enabling the system to surface actionable insights from raw data stored in RING

Together, they unite the strengths of scalable data storage and high-performance retrieval, making RAG workflows both dynamic and efficient.

LangChain: The orchestrator of intelligent workflows with Scality RING

While S3 and Milvus handle storage and retrieval, LangChain acts as the AI orchestrator, seamlessly managing the data flow between components. Its modular design simplifies the creation of RAG pipelines, making it easier to integrate storage, retrieval, and generative AI.

LangChain’s key roles:

1. Query embedding creation: Converts user queries into vector embeddings using pre-trained models like OpenAI’s GPT
2. Retrieval management: Sends embeddings to Milvus for similarity search and fetches raw documents from RING based on document IDs
3. Contextual augmentation: Combines user queries with retrieved content, creating an enriched input for generative AI models like GPT-3.5
4. Response generation: Facilitates the final step, delivering precise, context-aware responses back to the user

LangChain simplifies complex interactions, ensuring that each component works in harmony.

Why fine-tuning GPT-3.5 was essential

While GPT-3.5 provides a strong foundation, its general-purpose knowledge isn’t enough for domain-specific applications. Fine-tuning bridges this gap, aligning the model with domain-specific data to improve accuracy and relevance.

Benefits of fine-tuning:

• Domain expertise: Tailored to specific datasets, the model understands nuanced terms and generates actionable insights.
  Example: Fine-tuning enables GPT-3.5 to provide structured responses to queries like “What is insulin resistance?” rather than offering vague generalities.
• Better recall and precision: Fine-tuned models synthesize retrieved data more effectively, ensuring the chatbot delivers focused and contextually relevant answers.
• Improved user satisfaction: Responses are sharper, clearer, and more aligned with user expectations.

How it all comes together: The RAG chatbot workflow

With Scality RING, Milvus, LangChain, and fine-tuned GPT-3.5, we’ve built a seamless, high-performing pipeline for intelligent chatbots. Here’s how it works:

1. User query submission: A user submits a question (e.g., “What are the symptoms of diabetes?”).
2. Query embedding creation: LangChain generates a vector embedding for the query.
3. Document retrieval: Milvus identifies relevant document IDs, and RING fetches the associated raw content.
4. Augmentation: LangChain combines the user query with retrieved content for additional context.
5. Response generation: Fine-tuned GPT-3.5 generates a precise, human-like answer.
6. Response delivery: The chatbot delivers the answer back to the user.

Code sample showing integration of the technology components with RING

The following sample code shows the integration of RING, Milvus and GPT 3.5.

Conclusion: Unlock the power of RAG with Scality

Once again, building an intelligent chatbot requires more than just AI models—it demands a scalable, reliable, and AI-ready foundation. 

With Scality RING’s S3-compatible object storage, you gain the backbone needed to support advanced RAG workflows. By integrating with Milvus, LangChain, and GPT-3.5, RING enables chatbots that deliver smarter, more context-aware responses. Whether for customer support, research, or enterprise intelligence, this technology stack ensures your chatbot is equipped to handle real-world demands and unlock new possibilities.

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Additional resources

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AI data lakes with Scality RING

Sequoia Genomics AI data lake

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