In the rapidly evolving landscape of generative AI, Retrieval-Augmented Geeration (RAG) has emerged as a cornerstone for building context-aware and factually grounded applications. As organizations move from single-purpose RAG systems to sophisticated Multi-Agent RAG pipelines, the underlying vector database's role becomes increasingly critical. This blog report provides an in-depth comparative analysis of four prominent vector data solutions—Chroma, Meta AI FAISS, Canonical OpenSearch, and Linux Foundation Milvus—from the specific integration perspective of IBM's open-source Data Prep Kit (DPK). It aims to guide architects and data scientists in selecting the most suitable vector store for scalable, secure, and efficient multi-agent systems.

Executive Summary: Strategic Choices for Multi-Agent RAG

This blog report provides a comprehensive analysis of four prominent open-source vector databases—Mera AI FAISS, Chroma, Canonical OpenSearch, and Linux Foundation Milvus—evaluating their suitability for a Multi-Agent Retrieval-Augmented Generation (RAG) system pipeline, specifically from the perspective of their integration with IBM's open-source Data Prep Kit (DPK).

The analysis finds that the optimal choice of a vector database is not a one-size-fits-all decision. Instead, it depends critically on the project's scale, operational requirements, and long-term architectural vision. The primary recommendation is a tiered approach:

• FAISS is a powerful choice for high-performance, single-machine operations on static datasets where raw speed is the paramount concern.

• Chroma excels at rapid prototyping and development for small to mid-sized projects due to its simplicity and all-in-one functionality.

• Canonical OpenSearch is the preferred solution for production-ready, mid-sized enterprise applications that require a robust, distributed platform with advanced hybrid search capabilities.

• Linux Foundation Milvus is the most suitable option for large-scale, mission-critical, and highly resilient multi-agent systems that must handle massive data volumes and high-concurrency workloads.

A key finding of this report is that IBM's Data Prep Kit acts as an architectural decoupler. By standardizing the complex and often labor-intensive data preparation pipeline—including tasks like document chunking, deduplication, and embedding generation—the DPK simplifies the downstream decision-making process. It allows architects and developers to choose a vector database based on its core retrieval and operational features, rather than its native data ingestion tooling, which often varies widely and can introduce significant architectural friction.

1. The Multi-Agent RAG Pipeline: A New Paradigm in Enterprise AI

The traditional Retrieval-Augmented Generation (RAG) system is a linear process where a single query triggers a retrieval step from a vector database, and the retrieved context is then passed to a Large Language Model (LLM) for a single-turn generation. This architecture is effective for simple question-and-answer applications but proves insufficient for complex, real-world problems that require nuanced, multi-faceted information retrieval and synthesis. A new paradigm has emerged to address this limitation: the Multi-Agent RAG system. This model introduces specialized, collaborative agents, each with a specific purpose, such as a Retrieval Agent, a Validation Agent, a Synthesis Agent, and an Orchestrator Agent.1 This transition from a monolithic, single-pass process to a dynamic, collaborative system fundamentally changes the role and requirements of the underlying data infrastructure.

In this new architectural model, the vector database ceases to be a passive component. It transforms into the system’s shared knowledge layer and central nerve center, facilitating seamless communication and dynamic data retrieval between agents.2 For example, one agent might ingest new data into the database in real time, while another agent simultaneously performs a complex query, and a third agent uses the results to synthesize a response. This parallel and collaborative nature places unprecedented demands on the vector database for consistency, concurrency, and performance. The system's robustness is directly tied to the database's ability to handle these complex interactions effectively.

To support this sophisticated collaboration, the underlying data infrastructure must satisfy several key requirements. First, it must ensure data consistency and governance. All agents must operate from a single, trusted source of truth, necessitating a system that can handle data cleansing, PII redaction, and quality checks.6 Second, real-time ingestion and synchronization are essential. The knowledge base must be continuously updated to ensure that all agents are working with the most current information, which means the database must be able to handle continuous data streams and updates without performance degradation.7 Finally, advanced metadata filtering is a non-negotiable feature. As agents become more specialized, their queries will require more than just semantic similarity. They may need to filter documents based on source, date, author, or other structured metadata to provide a more targeted and accurate context for the LLM.9 The effectiveness of a multi-agent system hinges on the database's ability to support these diverse and complex retrieval patterns. The shift from a single, static RAG component to a dynamic, collaborative ecosystem means that the vector database is no longer a simple data store; it is a foundational, scalable, and highly available service that underpins the entire application's functionality.

The Architectural Imperative: A Multi-Agent RAG system transcends simple question-answering. It involves a collaborative ecosystem of specialized AI agents, each potentially possessing unique roles, access permissions, and knowledge domains. This complex architecture imposes stringent requirements on the vector database:

• Scalable Concurrency: The database must handle simultaneous, independent query requests from numerous agents without performance degradation.

• Data Isolation and Multi-Tenancy: It is crucial to logically and securely partition data. Each agent, or group of agents, should only access its designated knowledge base, preventing data leakage and ensuring query relevance.

• Fine-Grained Access Control: Security policies must be enforceable at a granular level, defining which agents can read, write, or manage specific data partitions.

• Advanced Metadata Filtering: Agents need to execute complex queries that combine semantic similarity search with precise filtering based on metadata (e.g., source, timestamp, security clearance).

• High-Throughput Data Ingestion: The system must efficiently process and index large volumes of data prepared by upstream data pipelines.

2. IBM's Data Prep Kit: The Unifying Foundation

Before data can be leveraged in any RAG application, it must undergo a meticulous preparation process. This can often be the most challenging and time-consuming part of building an AI system. To address this, IBM developed the Data Prep Kit (DPK), an open-source toolkit designed to standardize and simplify the complex workflow of preparing unstructured data for LLMs. 13

The DPK is designed to clean, transform, and prepare large-scale datasets for LLM workflows, including RAG. Its key architectural components—data access, transformation, and a flexible runtime—dictate how a vector database must integrate, and the details are explained below. DPK can execute on various runtimes, from a local Python environment to distributed clusters using Ray and Spark.

The ideal vector database, from the DPK integration perspective, should offer:

• Seamless Ingestion from Distributed Runtimes: Native connectors or efficient client libraries that allow worker nodes in a Ray or Spark cluster to directly and performantly load processed and embedded data into the vector database.

• API and Client Library Robustness: Well-documented and feature-rich Python clients that can be easily invoked within DPK's custom transformation modules.

• Schema Flexibility: The ability to accommodate the structured output of DPK, which includes embeddings, source text, and rich metadata, without cumbersome data reshaping.

The DPK is built on a modular architecture composed of three core components:

• Data Access: This component provides a standardized way to read and write data from various storage locations, including local file systems and S3-compatible object storage. It is engineered with checkpointing capabilities, which allow a long-running data preparation job to restart from its last successful point, saving significant time and resources in enterprise environments.6

• Transformation: This is the core of the DPK, offering a library of pre-built "transforms" for common data cleaning and enrichment tasks. These transforms are self-contained, configurable units of work that can be chained together into end-to-end data processing pipelines. The toolkit is also designed to be extensible, allowing developers to create and integrate their own custom transforms without needing deep expertise in distributed computing frameworks.13

• Runtime: The runtime is the execution environment for the data pipeline. A key feature of the DPK is its flexibility to scale from a single laptop to a large data center cluster. It supports various runtimes, including pure Python (for local development), Ray (for distributed computing), and Spark (for large-scale big data processing).13 This flexibility makes the DPK a highly versatile tool that can grow with a project's needs.

The DPK provides several transforms that are particularly essential for a RAG pipeline.13 For document ingestion, it includes transforms like Docling to Parquet and HTML to Parquet to extract and standardize content from various file formats. This is a critical first step, mirroring the functionality of loaders like LangChain's PyPDFLoader.15 The kit also offers Exact dedup (de-duplication) filter and Fuzzy dedup filter transforms to remove redundant information, which is crucial for reducing noise and improving the quality of the LLM's output.13

Text chunking is handled by a dedicated Document chunking for RAG transform, which splits large documents into smaller, context-rich pieces that can fit within an LLM's limited context window.13 Finally, the Text encoder transform is explicitly designed to generate vector embeddings from the processed text, creating the numerical representations that are the lingua franca of vector databases. 13

This modular, pipeline-based approach means that DPK's output—a collection of clean, chunked text and its corresponding vector embeddings, often in the standardized Parquet format—serves as a universal adapter for the vector databases. This has a profound architectural implication: it decouples the data preparation layer from the vector storage layer. Instead of being constrained by a database's potentially limited or proprietary document loaders and embedding functions 20, an organization can use the DPK to perform all the complex data engineering work upstream. This allows the selection of the vector database to be based solely on its core strengths, such as retrieval performance, scalability, and operational features, rather than its data ingestion tooling. This separation of concerns streamlines the overall system design and provides a consistent data foundation regardless of the chosen vector database.

3. Comparative Analysis: Vector Databases for a Unified Pipeline

This section dissects each of the four vector data solutions, evaluating their architecture and features against the demands of a DPK-powered, multi-agent RAG pipeline.

3.1. FAISS: The High-Performance Core

Architectural Overview: FAISS (Facebook AI Similarity Search) is fundamentally a library, not a database.21 It is designed for in-memory, high-performance similarity search and clustering of dense vectors. It offers a suite of algorithms, ranging from brute-force Flat search for exact nearest neighbors to various Approximate Nearest Neighbor Search (ANNS) methods like IVF (Inverted File) and HNSW (Hierarchical Navigable Small World), which trade a small degree of accuracy for massive gains in speed.23 FAISS excels at what it was designed for: raw, lightning-fast vector search.

DPK Integration: The integration with DPK is straightforward. The DPK's Text encoder transform produces the exact input that FAISS requires: a collection of high-dimensional vectors.13 The DPK pipeline can perform all the pre-processing—document extraction, deduplication, and chunking—and then create the final embeddings. This pre-processed data is then loaded directly into a FAISS index object in memory using integration libraries like LlamaIndex's FaissVectorStore 27 or LangChain's FAISS.from_documents method.15 The workflow is a simple, sequential data transfer, with the DPK providing the perfect fuel for the FAISS engine.

Multi-Agent Suitability Analysis: The primary advantage of FAISS in a multi-agent system is its speed for in-memory operations. For a scenario where a single agent needs to perform a lightning-fast search on a relatively static, in-memory dataset, FAISS's performance is unmatched.24 Its ANNS algorithms allow for fine-tuning the balance between search speed and retrieval accuracy, a critical trade-off in many applications.24

However, the core nature of FAISS as a library, rather than a full-fledged database, introduces significant architectural challenges for a multi-agent system. It lacks fundamental database features such as built-in data persistence, a native query language, or support for concurrent multi-user access.29 The FAISS index is a file that must be explicitly saved and loaded from disk, as demonstrated by LangChain's save_local and load_local methods.15 It is not designed for multi-node deployments or real-time data ingestion. For a multi-agent system where different agents might be updating the knowledge base or querying concurrently, FAISS would require a complex, custom-built wrapper to handle persistence, synchronization, and distribution. The raw speed of FAISS is exceptional, but its lack of operational resilience and database features makes it a high-risk solution for a mission-critical, production-ready, distributed pipeline.

Thus, it is crucial to understand that FAISS is not a database; it is a C++ library with Python bindings for highly efficient vector similarity search.

• It provides a toolkit of indexing algorithms (e.g., IVF, HNSW) that can be run on CPU or GPU. It does not handle data storage, metadata, APIs, or security. Developers must build a surrounding service to manage these aspects.

• It integrates directly into the data processing code. Within a Spark or Ray task, a developer can use the FAISS library to build and serialize an index for a specific partition of data. These index files would then need to be stored (e.g., in an object store like S3) and loaded by a separate query service. This offers maximum flexibility but requires significant engineering effort.

• Using raw FAISS is generally unsuitable for multi-agent RAG systems out of the box. It lacks the fundamental database features required: no API endpoint, no concurrent access management, no multi-tenancy, and no access control. An entire database layer would need to be custom-built on top of it to support a multi-agent architecture. This approach is only recommended for expert teams with very specific performance requirements that cannot be met by managed solutions.

3.2. Chroma: The AI-Native Simplicity

Architectural Overview: Chroma itself is an open-source, AI-native vector database designed for developer productivity and happiness.20 It offers a simplified, all-in-one solution with built-in document storage, embedding capabilities, and indexing. Its design is focused on ease of use, providing options for an ephemeral in-memory client, a persistent local client, or a full client-server mode for production.20 Chroma's core features include vector search, full-text search, and advanced metadata filtering, all of which are crucial for dynamic RAG applications.9

DPK Integration: Chroma offers a choice in its integration with the DPK. The database's native ingestion capabilities allow it to accept raw text documents and automatically handle the chunking and embedding process internally using a default model like all-MiniLM-L6-v2.20. This approach simplifies the pipeline by bypassing the DPK's more advanced transforms, allowing for rapid, end-to-end development. Alternatively, a developer can fully leverage the DPK pipeline for its advanced features like deduplication, PII redaction, and custom text encoding. The DPK would output a collection of pre-processed and embedded data, which would then be added to Chroma via its API.34 This approach provides a greater degree of control and maintains a standardized upstream workflow.

Multi-Agent Suitability Analysis: The primary strength of Chroma for a multi-agent system is its simplicity and suitability for rapid prototyping.20 Its persistent and client-server modes make it a good fit for small to mid-sized, multi-agent applications where a single shared repository is needed.20 The native support for document storage and metadata filtering is well-suited for the complex filtering requirements of a multi-agent system. 9

However, this simplicity can become a liability at scale. While Chroma supports different storage backends like DuckDB for local use 9, it may not scale to the level required by large-scale enterprise multi-agent systems that deal with billions of vectors and high-concurrency workloads. Its architecture is less decoupled than that of OpenSearch or Milvus, which could lead to bottlenecks as the system grows. The shared client issue, where multiple instances of the Chroma client may inadvertently share the same in-memory SQLite instance, points to potential architectural immaturity that could impact the reliability of a shared knowledge base in a multi-agent environment.35 The "batteries-included" simplicity of Chroma, which accelerates initial development, can constrain an organization's ability to scale and customize a mission-critical production system.

Thus, the Chroma vector database is designed for simplicity and ease of use, making it a popular choice for rapid prototyping and smaller-scale applications.

• It can run in-memory, as a standalone server, or in a distributed, cloud-native architecture. Its newer data model explicitly supports multi-tenancy through a Tenant -> Database -> Collection hierarchy, providing complete data isolation between tenants.

• Integrating Chroma with DPK is a manual process that requires developers to write custom code. You can use Chroma's Python client (chromadb) within a DPK transform, whether it's running on Python, Ray, or Spark. Because there's no native Spark connector, the developer is responsible for handling the connection and batching logic to manage data ingestion from the Spark worker nodes to the Chroma server.

• Chroma's introduction of tenants and databases makes it viable for multi-agent scenarios where logical data separation is required. However, its access control mechanisms are less mature than those of Milvus or OpenSearch.

• It is best suited for development environments or applications with a limited number of agents where performance under high concurrent loads is not the primary concern.

3.3. Canonical OpenSearch: The Enterprise Unified Search Engine

Architectural Overview: OpenSearch is a widely adopted, open-source search and analytics engine built on Apache Lucene.36 Its robust, distributed architecture is designed for enterprise-scale data processing. It has a powerful vector search plugin (k-NN) that supports efficient indexing algorithms like HNSW and IVF, and its core strength lies in its ability to seamlessly combine traditional lexical search with modern semantic vector search, a capability known as hybrid search.36

DPK Integration: OpenSearch provides a highly flexible ingestion workflow that aligns with the DPK's capabilities. The DPK pipeline can perform all the pre-processing—from data extraction and deduplication to chunking and embedding generation—and the resulting data is then pushed into OpenSearch using its comprehensive API. Alternatively, OpenSearch's native neural search plugin can be configured as an ingest pipeline to automatically generate embeddings for text data at ingest time.38 This allows a developer to shift the DPK's role from generating embeddings to simply preparing the raw documents (e.g., cleaning and chunking) before they are ingested by OpenSearch's native, on-the-fly embedding pipelines. 38

Multi-Agent Suitability Analysis: OpenSearch is uniquely suited for enterprise-scale multi-agent systems due to its heritage as a unified search platform. It already has a mature architecture for real-time indexing, distributed querying, and high availability.10 The platform's native RAG search pipeline processor, introduced in OpenSearch 2.12, is purpose-built for agentic workflows, handling query interception, conversational memory, and LLM integration with models from OpenAI and Amazon Bedrock.38 OpenSearch's RAG tool further supplements user questions with its internal knowledge. 38

The platform's native support for hybrid search is a distinct advantage for multi-agent systems. It allows different agents to use the most effective search method for their specific task. For example, a validation agent might use a precise lexical search to verify a specific phrase, while a retrieval agent uses a vector search to find conceptually similar documents. This synergy is a critical feature for building a robust and flexible multi-agent system. The primary drawback of OpenSearch is its operational complexity; managing and tuning a self-hosted, distributed OpenSearch cluster requires significant expertise in its specific mapping, analyzers, and ML integration.38

Thus, OpenSearch, a fork of Elasticsearch, with its k-Nearest Neighbor (k-NN) plugin, functions as a capable and scalable vector database. Canonical provides enterprise-grade support and management for OpenSearch.

• It is a distributed system designed for horizontal scalability, high availability, and resilience. Its core strength lies in combining traditional full-text search, analytics, and vector search in a single, unified platform.

• Integration from Spark-based DPK pipelines is a well-established pattern. The official Elasticsearch Hadoop connector (elasticsearch-spark) works seamlessly with OpenSearch, allowing Spark DataFrames containing embeddings and metadata to be efficiently written to OpenSearch indexes. This is a mature and highly scalable method for data ingestion.

• It is an excellent choice for multi-agent systems, particularly in environments where existing text-based search is also a requirement.

  • Multi-Tenancy is achieved by assigning different agents to different indexes or by leveraging the OpenSearch Security plugin's built-in multi-tenancy capabilities, which segregate data within Dashboards.

  • The Security plugin offers robust, fine-grained access control, allowing administrators to define roles with specific permissions down to the index, document, and even field level. This is ideal for enforcing which agents can access which knowledge bases.

  • With hybrid search, it can combine vector similarity scores with traditional BM25 text relevance scores is a major advantage for complex retrieval tasks.

3.4. Linux Foundation Milvus: The Cloud-Native Solution

Architectural Overview: Milvus is an open-source vector database engineered from the ground up for massive, cloud-native deployments.42 Its highly decoupled architecture separates its four main layers—Access, Coordinator, Worker Nodes, and Storage—allowing for independent scaling of each component.42 This design is ideal for managing colossal datasets and high-concurrency workloads, ensuring high availability and resilience. It supports a wide range of indexing algorithms, including IVF and HNSW, and offers flexible data modeling with support for scalar and JSON fields, enabling complex data filtering. 44

DPK Integration: Milvus's distributed architecture aligns perfectly with the DPK's scalable runtimes (Ray and Spark), enabling efficient parallel data processing.13 The DPK can be used to perform all data pre-processing, with its Docling to Parquet and Text encoder transforms handling the heavy lifting of extraction and embedding generation. The resulting data, in Parquet format, can then be efficiently imported into Milvus using its BulkWriter tool.46 Milvus's ability to handle dynamic fields through a reserved JSON field is highly beneficial for ingesting the rich metadata produced by the DPK without needing a pre-defined schema.44

Multi-Agent Suitability Analysis: Milvus is the most scalable and resilient option for large-scale, enterprise-wide multi-agent systems. The decoupling of its architecture means that ingestion load and search queries are handled by isolated nodes, ensuring that a heavy data update by one agent does not impact the real-time search performance of another.42 This operational maturity is a critical feature for a mission-critical production system. The platform's support for metadata filtering 11, hybrid search 11, and its robust API with multiple SDKs make it a powerful backend for complex, multi-faceted queries from different agents. Milvus Lite offers a lightweight, local-file-based version for prototyping that can seamlessly scale up to the distributed version, providing a clear path from development to production. While the complexity of managing a self-hosted distributed Milvus cluster can be high, its scalability and resilience are unparalleled among the options analyzed.8

Thus, Milvus is a graduate project of the Linux Foundation AI & Data, designed from the ground up as a distributed, high-performance vector database for large-scale AI applications.

• It features a cloud-native, disaggregated architecture that separates compute and storage. It uses components like query nodes, data nodes, and index nodes that can be independently scaled to meet specific workload demands (e.g., high ingest rate vs. high query rate).

• It provides a dedicated Spark-Milvus connector, making ingestion from DPK on Spark highly efficient. The connector allows Spark DataFrames to be written directly to a Milvus collection, abstracting away the complexity of batching and client-side logic. For Ray or Python runtimes, its pymilvus client is robust and supports high-throughput bulk insertions.

• It is arguably the most feature-rich and architecturally aligned solution for demanding multi-agent RAG systems.

  • It offers the most flexible multi-tenancy models: database-level (high isolation), collection-level, partition-level, and even partition-key level (massive scalability for millions of tenants/agents sharing a single collection).

  • Milvus implements Role-Based Access Control (RBAC), allowing administrators to create roles (e.g., "finance_agent_role") and grant specific privileges (e.g., SELECT, INSERT) on specific collections or databases.

  • It supports a wide variety of index types (HNSW, IVF-FLAT, SCANN) and consistency levels, allowing performance to be fine-tuned to the specific needs of different agents.

4. Comprehensive Feature and Integration Matrix

The matrix provides a structured overview of the comparative analysis. It makes the trade-offs between the four options explicitly clear. For example, a low score for "Operational Complexity" for FAISS and Chroma comes at the cost of limited features and scalability, whereas the high complexity of OpenSearch and Milvus is directly tied to their robust, enterprise-grade capabilities.

4. Detailed Feature Comparison Table

5. Synthesis and Strategic Recommendations

The DPK as the Great Equalizer

A central theme of this analysis is that IBM's Data Prep Kit fundamentally simplifies the architectural decision-making process. By providing a clean, modular, and scalable framework for data preparation—from raw document ingestion to final vector encoding—the DPK standardizes the upstream pipeline. This standardization means that regardless of the vector database chosen, the input data will be consistently clean, chunked, and embedded, allowing organizations to focus on the databases' core retrieval performance, scalability, and operational features. The DPK acts as a unifying foundation, making it far easier to switch between these technologies as business needs evolve.

Strategic Recommendations by Use Case

Choosing the right vector database is a critical architectural decision that will profoundly impact the scalability, security, and performance of a Multi-Agent RAG system. Based on the integration perspective of IBM's Data Prep Kit and the specific demands of a multi-agent architecture, the following strategic recommendations are provided for specific use cases:

• For Expert Teams with Unique Performance Needs, direct use of FAISS offers unparalleled control and performance. However, this path should be avoided unless the team is prepared to build and maintain a complete database service layer around the library.

• For Prototyping and Small Projects: Choose Chroma. For rapid development and proof-of-concept projects, Chroma is the clear winner. Its simplicity, "batteries-included" approach, and all-in-one functionality allow a developer to go from a raw dataset to a working RAG agent in a matter of minutes. The local and persistent clients are perfect for single-server applications and quick iterative development cycles. Its built-in document storage and metadata filtering capabilities are more than sufficient for small-scale multi-agent systems.

• For Mid-Sized or Departmental Systems: Choose Canonical OpenSearch. For a production-ready, mission-critical application that will support a team or a specific department, OpenSearch is the optimal choice. Its foundation as a mature, distributed search engine provides a level of reliability and scalability that the smaller databases cannot match. Its native hybrid search capabilities and purpose-built RAG processors provide a powerful, out-of-the-box solution for complex retrieval patterns. Its robust security and access control features are well-suited for multi-agent systems in corporate environments. While it requires more operational expertise than Chroma, its robust feature set and proven enterprise performance make it a reliable choice for a mid-sized system.

• For Large-Scale, Enterprise-Wide Multi-Agent Systems: Choose Linux Foundation Milvus. For building a RAG system that must handle massive data volumes (billions of vectors) and serve a large number of concurrent users and agents, Milvus is the superior solution. Its highly decoupled, cloud-native architecture is engineered specifically for elastic scalability and high availability. By separating compute and storage, Milvus ensures that ingestion tasks do not interfere with search performance, a critical feature for real-time, high-traffic applications. While its operational complexity is the highest of the four, its unparalleled scalability and resilience make it the only truly viable option for a mission-critical, enterprise-grade multi-agent system.

Ultimately, the decision rests on the specific trade-offs between ease of use, feature requirements, scalability demands, and existing infrastructure. By aligning the capabilities of these powerful vector data solutions with the architectural blueprint of the multi-agent RAG system, organizations can build a robust and intelligent foundation for the next generation of AI applications.

Final Word: Architecting for the Future

The decision of which vector database to use should be viewed as part of a larger, evolving architectural strategy. The DPK provides a modular data preparation framework that allows an organization to start with a simple, developer-friendly setup like Chroma and seamlessly migrate to a more robust, scalable solution like Milvus or OpenSearch as the RAG system grows in complexity and user base. By maintaining a consistent data preparation pipeline with the DPK, an organization can future-proof its RAG architecture, ensuring that the choice of a vector database is not a limiting factor but a strategic decision that aligns with its long-term goals for building sophisticated, intelligent AI applications.

References

1. Building Multi-Agent RAG Systems: A Step-by-Step Implementation Guide. (n.d.). Empathy First Media. Retrieved August 26, 2025, from https://empathyfirstmedia.com/building-multi-agent-rag-systems-step-by-step-implementation-guide/

2. Building an Agentic RAG System over existing knowledge database (with minimum coding required) - Reddit. (n.d.). Reddit. Retrieved August 26, 2025, from https://www.reddit.com/r/Rag/comments/1lwhdib/building_an_agentic_rag_system_over_existing/

3. AI Agents for Data Engineering: Complete Guide to Smart Pipelines. (n.d.). Amnet Digital. Retrieved August 26, 2025, from https://www.amnetdigital.com/blogs/ai-agents-for-data-engineering-automation

4. Kumar, S. (n.d.). MMOA-RAG: optimization algorithm to improve RAG via Multi-Agent Reinforcement Learning | by SACHIN KUMAR | Medium. Medium. Retrieved August 26, 2025, from https://medium.com/@techsachin/mmoa-rag-optimization-algorithm-to-improve-rag-via-multi-agent-reinforcement-learning-52b83e955f62

5. Multi-agent RAG System - Hugging Face Open-Source AI Cookbook. (n.d.). Hugging Face. Retrieved August 26, 2025, from https://huggingface.co/learn/cookbook/multiagent_rag_system

6. What is the Data Prep Kit? | IBM. (n.d.). IBM. Retrieved August 26, 2025, from https://www.ibm.com/think/topics/data-prep-kit

7. How to Prepare Your Data for AI Agents. (n.d.). Pryon. Retrieved August 26, 2025, from https://www.pryon.com/resource/ai-agents-are-coming-but-your-data-isnt-ready

8. Milvus: Your Vector Database Powerhouse – A Deep Dive - DEV Community. (n.d.). DEV Community. Retrieved August 26, 2025, from https://dev.to/simplr_sh/milvus-your-vector-database-powerhouse-a-deep-dive-2d6h

9. Learn How to Use Chroma DB: A Step-by-Step Guide | DataCamp. (n.d.). DataCamp. Retrieved August 26, 2025, from https://www.datacamp.com/tutorial/chromadb-tutorial-step-by-step-guide

10. Vector Search - OpenSearch. (n.d.). OpenSearch. Retrieved August 26, 2025, from https://opensearch.org/platform/vector-search/

11. Retrieval-Augmented Generation (RAG) with Milvus and LlamaIndex .... (n.d.). Milvus. Retrieved August 26, 2025, from https://milvus.io/docs/integrate_with_llamaindex.md

12. Chroma - LangChain.js. (n.d.). LangChain.js. Retrieved August 26, 2025, from https://js.langchain.com/docs/integrations/vectorstores/chroma/

13. data-prep-kit/data-prep-kit: Open source project for data preparation for GenAI applications - GitHub. (n.d.). GitHub. Retrieved August 26, 2025, from https://github.com/data-prep-kit/data-prep-kit

14. Preparing data for a RAG pipeline using Data Prep Kit (DPK) - IBM Developer. (n.d.). IBM Developer. Retrieved August 26, 2025, from https://developer.ibm.com/tutorials/dpk-rag-llms/

15. Hosen, A. (n.d.). Building a RAG System with LangChain, FAISS & DeepSeek-LLM .... Medium. Retrieved August 26, 2025, from https://medium.com/@abir71.hosen/building-a-rag-system-with-langchain-faiss-deepseek-llm-d19d28d95e29

16. Amitkmj. (n.d.). Building RAG Pipelines with FAISS, AWS Bedrock, and LLMs | by Amitkmj - Medium. Medium. Retrieved August 26, 2025, from https://medium.com/@amitkmj78/building-rag-pipelines-with-faiss-aws-bedrock-and-llms-7d68803d7dc5

17. Data preparation for a Q&A application powered by LLMs. (n.d.). dataroots. Retrieved August 26, 2025, from https://dataroots.io/blog/aiden-data-ingestion

18. How to Build a RAG Pipeline: Step-by-Step Guide. (n.d.). Multimodal. Retrieved August 26, 2025, from https://www.multimodal.dev/post/how-to-build-a-rag-pipeline

19. How to Build RAG Pipelines for LLM Projects?. (n.d.). ProjectPro. Retrieved August 26, 2025, from https://www.projectpro.io/article/rag-pipelines/1070

20. Getting Started - Chroma Docs. (n.d.). Chroma Docs. Retrieved August 26, 2025, from https://docs.trychroma.com/getting-started

21. Welcome to Faiss Documentation — Faiss documentation. (n.d.). Faiss. Retrieved August 26, 2025, from https://faiss.ai/

22. Vector DB implementation using FAISS – SQLServerCentral. (n.d.). SQLServerCentral. Retrieved August 26, 2025, from https://www.sqlservercentral.com/articles/vector-db-implementation-using-faiss

23. FAISS Vector Database: A High-Performance AI Similarity Search - ProjectPro. (n.d.). ProjectPro. Retrieved August 26, 2025, from https://www.projectpro.io/article/faiss-vector-database/1009

24. FAISS & RAG: The Dynamic Duo of Knowledge-Powered AI - Level Up Coding. (n.d.). Level Up Coding. Retrieved August 26, 2025, from https://levelup.gitconnected.com/faiss-rag-the-dynamic-duo-of-knowledge-powered-ai-7d6bc2ced781

25. cristianleoo. (n.d.). rag-knowledge-graph/faiss/demo.ipynb at main · cristianleoo/rag .... GitHub. Retrieved August 26, 2025, from https://github.com/cristianleoo/rag-knowledge-graph/blob/main/faiss/demo.ipynb

26. kukreti, N. (n.d.). FAISS: The Low-Level Inner Workings of Vector Search and Optimization | by Nishtha kukreti. Medium. Retrieved August 26, 2025, from https://medium.com/@nishthakukreti.01/faiss-the-low-level-inner-workings-of-vector-search-and-optimization-cd642dec3518

27. Faiss - LlamaIndex. (n.d.). LlamaIndex. Retrieved August 26, 2025, from https://docs.llamaindex.ai/en/stable/api_reference/storage/vector_store/faiss/

28. LlamaIndex Vector_Stores Integration: Faiss - Llama Hub. (n.d.). Llama Hub. Retrieved August 26, 2025, from https://llamahub.ai/l/vector_stores/llama-index-vector-stores-faiss?from=

29. Pre and post processing · facebookresearch/faiss Wiki - GitHub. (n.d.). GitHub. Retrieved August 26, 2025, from https://github.com/facebookresearch/faiss/wiki/Pre--and-post-processing

30. Chroma DB: The Ultimate Vector Database for AI and Machine Learning Revolution. (n.d.). Metadesign Solutions. Retrieved August 26, 2025, from https://metadesignsolutions.com/chroma-db-the-ultimate-vector-database-for-ai-and-machine-learning-revolution/

31. Chroma - Python LangChain. (n.d.). Python LangChain. Retrieved August 26, 2025, from https://python.langchain.com/docs/integrations/vectorstores/chroma/

32. Chroma Docs: Introduction. (n.d.). Chroma Docs. Retrieved August 26, 2025, from https://docs.trychroma.com/

33. chroma-core/chroma: Open-source search and retrieval database for AI applications. (n.d.). GitHub. Retrieved August 26, 2025, from https://github.com/chroma-core/chroma

34. Data Prep Kit - AI Cloud Automation. (n.d.). AI Cloud Automation. Retrieved August 26, 2025, from https://aicloudautomation.net/projects/data-prep-kit/

35. Chroma.from_documents adds docs to same collection creating duplicates #11665 - GitHub. (n.d.). GitHub. Retrieved August 26, 2025, from https://github.com/langchain-ai/langchain/issues/11665

36. Amazon OpenSearch Service vector database capabilities revisited | AWS Big Data Blog. (n.d.). AWS Big Data Blog. Retrieved August 26, 2025, from https://aws.amazon.com/blogs/big-data/amazon-opensearch-service-vector-database-capabilities-revisited/

37. Reduce costs with disk-based vector search - OpenSearch. (n.d.). OpenSearch. Retrieved August 26, 2025, from https://opensearch.org/blog/reduce-cost-with-disk-based-vector-search/

38. Using OpenSearch for Retrieval-Augmented Generation (RAG ...) - OpenSearch. (n.d.). OpenSearch. Retrieved August 26, 2025, from https://opensearch.org/blog/using-opensearch-for-retrieval-augmented-generation-rag/

39. RAG with OpenSearch: A Crash Course for Building RAG Applications - BigData Boutique. (n.d.). BigData Boutique. Retrieved August 26, 2025, from https://bigdataboutique.com/webinar/2025-07-rag-with-opensearch

40. OpenSearch integration :: English - Starting the workshop. (n.d.). AWS Workshop. Retrieved August 26, 2025, from https://opea.awsworkshop.io/module-3/opensearch_intro.html

41. Zero to RAG: A quick OpenSearch vector database and DeepSeek integration guide. (n.d.). OpenSearch. Retrieved August 26, 2025, from https://opensearch.org/blog/deepseek-integration-rag/

42. Milvus Architecture Overview. (n.d.). Milvus. Retrieved August 26, 2025, from https://milvus.io/docs/architecture_overview.md

43. What is Milvus | Milvus Documentation. (n.d.). Milvus. Retrieved August 26, 2025, from https://milvus.io/docs/overview.md

44. Build RAG with Milvus and Docling | Milvus Documentation. (n.d.). Milvus. Retrieved August 26, 2025, from https://milvus.io/docs/build_RAG_with_milvus_and_docling.md

45. Index Explained | Milvus Documentation. (n.d.). Milvus. Retrieved August 26, 2025, from https://milvus.io/docs/index-explained.md

46. Building RAG with Milvus and EmbedAnything | Milvus Documentation. (n.d.). Milvus. Retrieved August 26, 2025, from https://milvus.io/docs/build_RAG_with_milvus_and_embedAnything.md

47. Prepare Source Data | Milvus Documentation. (n.d.). Milvus. Retrieved August 26, 2025, from https://milvus.io/docs/prepare-source-data.md