The State of Retrieval-Augmented Generation in 2025: A Strategic Framework for Decision-Making
As of mid-2025, Retrieval-Augmented Generation (RAG) has definitively transitioned from a novel technique for mitigating model hallucinations to a cornerstone of the enterprise AI stack. The conversation has matured beyond simple question-answering, driven by a deeper understanding of where true value lies. The market is now defined by a set of critical strategic choices that organizations must navigate: the evolution of RAG into an agentic component, the central conflict between open-source flexibility and managed platform velocity, and the ascendant importance of data quality over marginal model improvements. Understanding this landscape is the prerequisite for making sound architectural and investment decisions.
The Retrieval-Augmented Generation (RAG) market has experienced explosive growth, reaching $1.2 billion in 2024 with projections indicating a 49.1% compound annual growth rate through 2030. This transformation reflects a fundamental shift: 73.34% of RAG implementations now occur in large organizations, while breakthrough research published in 2025 promises performance improvements of 10-50% across various metrics. This comprehensive analysis examines the current RAG landscape, comparing frameworks for both local and enterprise deployment while integrating the latest research developments.
The New RAG Paradigm: From Standalone Tool to Core Agent Component
The most significant shift in the RAG landscape of 2025 is its conceptual reframing. RAG is no longer viewed as an end in itself but as a foundational component—the "long-term memory"—for sophisticated, multi-step AI agents.1 This evolution is not merely semantic; it reflects a fundamental change in the complexity of problems being solved with AI. While early RAG successfully addressed the "knowledge cutoff" problem for static question-answering, enterprises now demand systems that can perform complex, dynamic tasks: analyzing sales data, comparing it against real-time market trends via an API, and drafting a summary report, for example. Such tasks require more than just knowledge retrieval; they demand planning, tool use, state management, and reasoning.
This has given rise to the era of "Agentic RAG." The RAG pipeline provides the verifiable, up-to-date knowledge, while an agentic framework provides the orchestration to use that knowledge in a multi-step workflow. This is why the discourse has shifted from "RAG" to "Agent systems," with RAG acting as the memory system that supports agentic reasoning.1
This trend is evident across the ecosystem. The development of LangGraph, a library built upon the popular LangChain framework, is a direct response to this need. It moves beyond simple, linear "chains" to allow for the creation of cyclical graphs that can model stateful, multi-agent workflows, enabling conditional logic and collaborative task handling between different AI agents.3 This architecture is essential for building systems that can reflect, retry, and dynamically alter their course of action—capabilities that are impossible with a simple, one-shot RAG pipeline.
Commercial platforms have also embraced this agent-centric worldview. Amazon Bedrock explicitly positions its Knowledge Bases as a component to be used by "agents," which can intelligently identify the appropriate knowledge source based on user input and integrate it into a larger task.2 Similarly, OpenAI's strategic investments, such as its 2025 acquisition of the database-as-a-service company Supabase, are widely interpreted as a move to equip its agentic frameworks with more robust and accessible memory management capabilities, building upon its earlier acquisition of the real-time indexing company Rockset to bolster RAG.1
The implication for technology leaders is clear: the evaluation criteria for a RAG framework have fundamentally changed. It is no longer sufficient to assess a framework solely on its retrieval accuracy. The critical question in 2025 is how well the framework supports the construction of stateful, multi-turn, tool-using agentic architectures. A framework that only excels at simple RAG is now considered incomplete.
The Central Conflict: Open-Source Orchestration vs. Managed Platforms
The second defining characteristic of the 2025 RAG market is its bifurcation into two distinct paths, representing a classic "Build vs. Buy" strategic decision. This choice between open-source orchestration frameworks and managed RAG-as-a-Service platforms is the most critical decision an organization will make, as it dictates cost, control, velocity, and security posture.
The "Build" Path: Open-Source Orchestration Frameworks:
This path is defined by modular, flexible, and highly controllable open-source libraries like LangChain, LlamaIndex, Haystack, and DSPy.6 These frameworks provide the "Lego bricks" for constructing custom RAG and agentic systems. Their primary value proposition is control and flexibility. Developers can swap out any component—the LLM, the embedding model, the vector store, the reranker—to optimize for performance or cost.9 LangChain, for instance, boasts an ecosystem of over 600 integrations, ensuring that organizations can evolve their stack without vendor lock-in.10
However, this flexibility comes at a cost. The responsibility for production deployment, scaling, monitoring, and, most critically, security, falls entirely on the development team. Implementing enterprise-grade security for an open-source RAG stack is a non-trivial undertaking, requiring careful management of permissions, input/output sanitization, and network security to mitigate risks like data corruption or unauthorized access to confidential information.11 Deploying these systems at scale often requires significant MLOps and DevOps expertise, involving technologies like Kubernetes, Docker, and Terraform.14
The "Buy" Path: Managed RAG Platforms:
This path is represented by end-to-end commercial platforms like Cohere, Amazon Bedrock Knowledge Bases, and Google Vertex AI Search.5 Their value proposition is speed, security, and reduced operational overhead. These platforms abstract away the immense complexity of the underlying RAG pipeline, offering a "fully managed" experience that handles everything from data ingestion and chunking to indexing and serving.5
For enterprises, the "Buy" path offers a fast track to deploying secure, scalable RAG applications. These platforms come with enterprise-grade security and governance controls built-in, including SOC 2 and HIPAA compliance, VPC deployment options, and clear data privacy guarantees.17 This allows teams to focus on the application logic rather than the underlying infrastructure. The trade-off is a degree of vendor lock-in and less granular control over the individual components of the RAG pipeline.22
This "Build vs. Buy" decision is not technical but strategic. As one mid-2025 analysis advises, enterprises should "Buy 'context' AI solutions and focus internal development on 'core' AI capabilities that create unique competitive advantages".22 For a generic internal HR chatbot ("context"), a managed platform is the logical choice for its speed and security. For a proprietary drug discovery engine that forms the company's core intellectual property ("core"), the granular control and customizability of the "Build" path may be essential. Any evaluation of RAG frameworks must therefore be conducted through both of these strategic lenses.
Evaluating the Stack: Why Data Quality Outweighs Model Choice in 2025
The final strategic pillar of the 2025 landscape is the decisive shift in focus from the generative model to the data retrieval pipeline. In the early days of generative AI, performance was largely dictated by the capability of the LLM itself. By 2025, the top-tier foundation models have become so powerful that they are approaching the status of "interchangeable commodities" for many RAG use cases.22 The marginal performance gain from switching between, for example, GPT-4o, Claude 4, and Gemini 2.0 is often less significant than the gain from improving the quality of the context provided to the model.
The performance of any RAG system is ultimately "capped by the quality of the data it can access".22 This has led to a "quality-in, quality-out" philosophy becoming a key differentiator. The new bottleneck for RAG performance is not generation, but retrieval. A system that feeds a powerful LLM noisy, irrelevant, or poorly structured context will inevitably produce a poor response. Conversely, a system that can provide a highly precise, relevant, and concise context can enable even a smaller, more cost-effective model to deliver outstanding results.
This realization elevates the importance of the components that handle the data pipeline: ingestion, parsing, chunking, embedding, retrieval, and reranking. Frameworks that excel in these areas are gaining prominence. RAGFlow, for instance, has built its entire identity around "deep document understanding," offering features like template-based chunking and a visual inspector that allows developers to verify the quality of the parsing process before indexing.24 Similarly, high-performing enterprise RAG services are defined by their "precision retrieval tuning," which involves using sophisticated techniques like hybrid search (combining keyword-based and semantic search), reranking algorithms to prioritize the best results, and metadata filtering.8
This changes the fundamental question for evaluators. Instead of asking, "Which framework supports the newest LLM?", the more salient question is, "Which framework provides the most sophisticated and controllable tools for data processing and retrieval?" This new lens prioritizes features like Haystack's advanced hybrid search capabilities, LlamaIndex's focus on optimized indexing, and the ability of flexible frameworks to implement cutting-edge research techniques like Sentence-Window Retrieval. High-quality, domain-specific data, and the pipeline that processes it, has become the most durable competitive advantage in the RAG space.22
Analysis of Leading Open-Source RAG Frameworks
The open-source RAG market in 2025 is not a monolith. It has matured and fragmented into a set of distinct philosophical approaches, each catering to different developer personas, project complexities, and strategic goals. The choice is no longer simply between LangChain and its alternatives; it is a choice between fundamentally different ways of building AI applications. Understanding these philosophies is key to selecting the right tool for the job.
LangChain & LangGraph: The Power of Orchestration and Agentic Design
Core Philosophy: LangChain remains the preeminent orchestration framework, often described as the "duct tape of GenAI" for its ability to connect disparate components into a cohesive application.16 Its core strength lies in its modularity, its "chain of calls" abstraction, and its unparalleled ecosystem of over 600 integrations with model providers, databases, and APIs.3 In 2025, its most important evolution is LangGraph, a library that extends the core framework to enable the creation of stateful, cyclical, and multi-agent systems—the essential architecture for modern agentic RAG.3
Pros: LangChain offers unmatched flexibility and a vast, active community.26 The LangChain Expression Language (LCEL) provides a powerful, declarative syntax for composing complex chains.26 LangGraph provides a clear and robust path for building the sophisticated, multi-step agentic applications that define the 2025 landscape.3
Cons: The framework's power comes with a steep learning curve; its abstractions can sometimes feel opaque or "magic" to new users.8 For simple, linear RAG tasks, its complexity can be overkill compared to more focused frameworks like LlamaIndex.9 It is an orchestration layer, not a retrieval specialist, and works best when paired with a dedicated indexing framework for large-scale retrieval.8
Release Cadence & Community: LangChain maintains a rapid development cycle, with frequent patch releases for bug fixes and new features (often multiple times per week) and minor version bumps that may contain breaking changes every 2-3 months for both its Python and JavaScript libraries.27 It has a massive community, reflected in its high GitHub engagement.7
LlamaIndex: The Data-Centric Retrieval Specialist
Core Philosophy: LlamaIndex is a data framework purpose-built to "connect custom data sources to large language models".7 Where LangChain focuses on the overall orchestration of agents and tools, LlamaIndex dedicates itself to perfecting the data pipeline: ingestion, indexing, and querying.4
Key Features: It provides simple, high-level abstractions for building RAG query engines, often in just a few lines of code.7 Its LlamaHub repository offers a vast collection of data connectors for diverse sources.9 It excels at advanced querying techniques, including sub-queries across multiple documents and sophisticated multi-document summarization.9
Pros: LlamaIndex is widely considered to have a gentler learning curve for standard RAG use cases.9 It is the ideal choice for document-heavy applications where the quality and precision of retrieval are paramount.16 The framework has a strong focus on implementing advanced and novel RAG techniques as they emerge from research.26
Cons: The framework is more "opinionated" in its design, prioritizing ease of use for data-centric tasks over the near-limitless flexibility of LangChain's orchestration capabilities.9 While it supports agentic concepts, its core strength and focus remain on the data-to-query-engine pipeline rather than on the construction of complex, multi-tool agents.8
Release Cadence & Community: Development is extremely active, with a constant stream of releases for its core library and its hundreds of integration packages. Updates often occur multiple times a week, adding new features, LLM support, and bug fixes.31 The community is large, highly engaged, and focused on the data engineering aspects of LLM applications.
Haystack: The Enterprise-Ready, Production-Focused NLP Pipeline
Core Philosophy: Developed by the company deepset, Haystack is an end-to-end open-source framework explicitly designed for building "production-ready" LLM applications.4 It brings a strong NLP engineering discipline to the RAG process, with a focus on enterprise-grade features, scalability, and robust search pipelines.8
Key Features: Haystack's standout feature is its powerful support for hybrid search, seamlessly combining traditional keyword-based search (like BM25) with modern dense vector search to get the best of both worlds.8 It features retriever-reader pipelines for optimized question-answering and has native, first-class support for enterprise search databases like Elasticsearch and OpenSearch.4 It also includes a visual pipeline editor, which can help bridge the gap between engineering and non-technical teams.16
Pros: Haystack is built with production deployment in mind, offering clear paths for scaling using Docker and Kubernetes.15 It is technology-agnostic, supporting models from all major providers as well as self-hosted variants.36 It is an excellent choice for organizations looking to build sophisticated, enterprise-grade semantic search systems.
Cons: The framework's power and production focus can lead to higher setup complexity, often requiring the configuration of external databases like Elasticsearch for full-scale use.8 Its learning curve is considered steeper than others, as it is designed more for NLP engineers and data scientists than for generalist developers or beginners.8 For highly complex, multi-step agentic orchestration, it is less feature-rich than LangChain.8
Release Cadence & Community: Haystack has a mature and stable release cadence, with minor version updates typically occurring on a monthly basis and patch releases in between. This predictable schedule reflects its focus on production stability.36 The project is backed by deepset and has a strong following in the enterprise NLP community.
RAGFlow: The Visual, Low-Code Approach to Deep Document Understanding
Core Philosophy: RAGFlow is a newer, self-hosted open-source engine that champions a "quality-in, quality-out" philosophy for RAG.24 Its core innovation is a visual, low-code interface designed to democratize the creation of RAG pipelines, with a particular emphasis on achieving "deep document understanding" during the parsing and chunking phase.3
Key Features: The framework is centered around a user-friendly, DAG-based visual editor where users can construct and manage their RAG workflows.24 It provides unique tools like template-based chunking tailored to specific document formats and a "chunk visualizer" that allows users to inspect and manually correct the parsing results, ensuring high-quality data enters the vector store.24
Pros: The intuitive, visual design makes RAGFlow exceptionally well-suited for rapid prototyping and for teams with mixed technical skill sets, including business analysts and data scientists.24 Its intense focus on the data quality and parsing stage addresses what is now understood to be a primary bottleneck in RAG performance.24 The self-hosted Docker deployment is straightforward for local setups.25
Cons: As a more recent entrant, RAGFlow's ecosystem of data connectors and third-party integrations is not as extensive as that of LangChain or LlamaIndex.6 Its visual paradigm, while powerful for its intended use cases, may be less flexible for developers looking to implement highly bespoke or unconventional agentic logic that falls outside the provided nodes. Its community is smaller but growing rapidly.7
DSPy: The Programmatic Paradigm Shift
Core Philosophy: Hailing from the Stanford NLP Group, DSPy represents a radical rethinking of how LLM pipelines are built. It stands for "Declarative Self-improving Python" and proposes a shift away from manual, brittle "prompt-hacking" towards a more structured, programmatic, and optimizable approach.6 The central idea is to declare the components and logic of your pipeline and then use a DSPy compiler to automatically optimize the prompts and even the weights of the models involved.24
Key Features: DSPy's architecture is built on three key concepts: Signatures (declaratively defining the input/output of a module), Modules (reusable Pythonic building blocks), and Optimizers/Compilers (which automatically tune the modules to maximize a given metric).24 This separates the "what" (the logic of the pipeline) from the "how" (the specific prompt text), making systems more robust and reproducible.
Pros: This innovative approach has the potential to revolutionize RAG development, moving prompt engineering from a dark art to a systematic, optimizable science.24 It is particularly powerful for complex, multi-step reasoning and tool-using agents, where the interactions between modules are difficult to tune by hand.42 It is backed by strong academic research and a growing community, including support from organizations like Databricks.43
Cons: DSPy represents a new paradigm that requires developers to unlearn old habits of manual prompting. Its ecosystem and tooling are still maturing compared to the established giants. For very simple applications, the overhead of defining signatures and running an optimizer might be unnecessary.
Use Cases: DSPy excels in scenarios where performance is critical and the pipeline is complex, such as multi-hop question answering, agentic tool use, and tasks requiring a high degree of factuality and reasoning.42
Table 1: Open-Source RAG Frameworks - Comparative Feature Matrix
Evaluation of Premier Commercial RAG Platforms
While open-source frameworks offer unparalleled control, the enterprise market in 2025 is increasingly drawn to managed RAG platforms. These closed-source, commercial offerings prioritize speed-to-market, security, and scalability, abstracting away the significant operational burden of building and maintaining a production-grade RAG system. Their value proposition is less about providing individual tools and more about delivering a secure, reliable, end-to-end service.
Cohere: The Enterprise-Grade Platform for High-Fidelity, Secure RAG
Core Philosophy: Cohere is purpose-built for enterprise AI, focusing on delivering high-quality, production-ready models with an emphasis on RAG, data privacy, and deployment flexibility.6 Their strategy is to provide a complete, secure, and trustworthy AI solution that solves real business problems, rather than just offering a raw API.22
Key Features:
Optimized Models: The platform's strength is built on a suite of specialized models. The Command R+ model is highly optimized for complex RAG workflows and multi-step tool use.17 The
Embed v3 model provides state-of-the-art performance for multilingual semantic search, forming the foundation of the retrieval process.17 The
Rerank model acts as a crucial final step, re-ordering retrieved documents to maximize relevance and precision before they are passed to the generator model.6Deployment Flexibility: Cohere's key differentiator is its range of deployment options, designed to meet stringent enterprise security and data residency requirements. In addition to a standard public API, Cohere offers deployment in a customer's Virtual Private Cloud (VPC) on major cloud providers (AWS, GCP, Azure, OCI) or a fully on-premises setup. This allows organizations in regulated industries like finance and healthcare to maintain complete control over their data.6
Security and Compliance: Cohere places a heavy emphasis on enterprise-grade security. The platform is compliant with major standards including SOC 2 Type II, ISO 27001, GDPR, and is HIPAA eligible.47 They provide clear data privacy commitments, ensuring that customer data is not used for training models without explicit consent.17
Pros: The combination of high-quality models and flexible deployment makes Cohere a top choice for precision-critical and regulated industries.16 Its strong multilingual capabilities are a significant advantage for global enterprises.17
Cons: As a vertically integrated platform, using Cohere can lead to vendor lock-in.23 Its token-based pricing model, while standard, can become unpredictable and costly for high-volume applications, making budgeting a challenge.23
Amazon Bedrock Knowledge Bases: The Fully Managed, AWS-Native RAG Engine
Core Philosophy: Amazon Bedrock Knowledge Bases is a fully managed AWS service designed to handle the entire RAG workflow from end to end. It is deeply integrated into the AWS ecosystem, providing a seamless, serverless experience for companies already invested in Amazon's cloud.5
Key Features:
Automated Pipeline: Bedrock automates the most labor-intensive parts of RAG. It automatically fetches data from sources like Amazon S3, SharePoint, or Confluence; chunks the documents using various strategies; converts them to embeddings using models like Amazon Titan; and indexes them into a chosen vector store (including Amazon Aurora, Amazon OpenSearch Serverless, Pinecone, Redis, and others).5
Advanced RAG Integration: Bedrock is incorporating cutting-edge research directly into its managed service. It offers advanced chunking options like semantic and hierarchical chunking. Most notably, when paired with Amazon Neptune Analytics as a vector store, it can automatically create and leverage knowledge graphs, enabling a GraphRAG approach to improve retrieval accuracy for complex queries.18 It also allows for custom chunking logic via AWS Lambda, which can even incorporate components from open-source frameworks like LangChain and LlamaIndex.18
Flexible APIs: The service offers two primary interaction models. The RetrieveAndGenerate API provides a complete, one-shot RAG solution. The Retrieve API, on the other hand, only handles the retrieval step, returning the relevant documents and allowing developers to build their own custom generation or agentic workflows on top.5
Native Security: As an AWS service, it inherits the full suite of AWS security and compliance capabilities, including IAM for granular permissions, VPC for network isolation, AWS KMS for customer-managed encryption keys, and is in scope for major compliance standards like HIPAA, SOC, and PCI DSS.21
Pros: For enterprises heavily invested in AWS, Bedrock Knowledge Bases is an obvious and powerful choice, drastically reducing infrastructure management and MLOps overhead.16 It is secure and scalable by design.
Cons: The platform creates very strong vendor lock-in with the AWS ecosystem. While it offers configuration options, it provides less granular control over the individual pipeline components compared to a fully self-built open-source stack.
Google Vertex AI Search & RAG Engine: The Grounding Engine for the Google Cloud Ecosystem
Core Philosophy: Google's offering in the RAG space is a suite of tools under the Vertex AI platform, designed to help developers build "grounded" generative AI applications. The offering is split into Vertex AI Search (a fully managed, high-level search and retriever API) and the Vertex AI RAG Engine (a more developer-centric framework that balances ease-of-use with customization).19
Key Features:
Ecosystem Integration: The platform's primary strength is its seamless, out-of-the-box integration with Google ecosystem data sources, including Google Drive, Gmail, and Google Cloud Storage.16 This makes it incredibly easy to build internal knowledge search tools for organizations that run on Google Workspace.
Managed Components: Similar to Bedrock, Vertex AI handles the underlying RAG pipeline, including data ingestion, transformation (chunking), embedding generation, and indexing into a corpus.51
Architectural Flexibility: The platform provides a spectrum of choices. A developer can use the simple Vertex AI Search API for a fully managed experience, or use the RAG Engine for more control, allowing integration with different vector databases (like Pinecone, Weaviate, or Google's own Vertex AI Vector Search) and open-source frameworks.19
Google Cloud Security: The platform is built on Google's robust security infrastructure and supports enterprise controls like Customer-Managed Encryption Keys (CMEK) and VPC Service Controls to ensure data isolation and protection.52
Pros: Vertex AI is the ideal choice for companies deeply embedded in the Google Cloud and Google Workspace ecosystems. The tiered offering, from a simple API to a more flexible engine, caters to a range of needs from rapid prototyping to custom application development.19
Cons: Like its AWS counterpart, it fosters deep integration with its parent cloud provider. The array of different products and brands (Vertex AI Search, RAG Engine, Grounding APIs, Generative AI App Builder) can be complex and confusing for newcomers to navigate.51
The Role of Specialized Vector Databases: Pinecone, Weaviate, and Milvus
It is crucial to recognize that the vector database is a specialized and distinct layer in the modern RAG stack. While managed platforms often bundle a vector store, they increasingly allow customers to choose a third-party option. In open-source stacks, the choice of vector database is a primary architectural decision with significant implications for performance, cost, and operational complexity.
Pinecone: A fully managed vector database service renowned for its extremely low-latency, real-time search capabilities at massive scale. It is a popular choice for performance-critical production applications like e-commerce recommendation engines or real-time support bots.16
Weaviate: An open-source vector database that positions itself as a "schema-aware knowledge base." It excels at hybrid search (combining sparse and dense vectors) and supports GraphQL APIs, making it particularly well-suited for applications with structured or multi-modal data (text, images, etc.).16
Milvus/Zilliz: Milvus is one of the most popular and mature open-source vector databases, offering high performance and flexibility for self-hosted deployments. It is a common choice for organizations building their RAG stack on open-source components from the ground up, often deployed on Kubernetes. Zilliz offers a managed cloud version of Milvus.57
The market's treatment of the vector database as a pluggable component underscores its importance. The choice is not an afterthought but a key architectural decision that allows an organization to tailor its data layer to specific needs—for example, pairing the orchestration flexibility of LangChain with the performance SLAs of Pinecone, or the open-source ethos of RAGFlow with a self-hosted Milvus cluster.
Table 2: Enterprise RAG Platforms - A Head-to-Head Comparison
Deployment Scenarios: A Comparative Implementation Guide
Moving from strategic analysis to practical implementation, this section provides comparative guides for the two most common deployment scenarios: a private, local setup for prototyping and a scalable, secure enterprise deployment for production.
Scenario 1: Local Deployment for Prototyping and Privacy
The ability to run powerful RAG systems locally on a developer's machine has become a first-class feature of the open-source ecosystem in 2025. Driven by the need for data privacy, cost-free experimentation, and offline capabilities, local deployment is a viable and increasingly popular choice.61
The Local Stack: A typical local RAG stack consists of three layers:
Local LLMs: Open-weight models like Meta's Llama 3.1, Google's Gemma 2, or models from Mistral are run using user-friendly inference servers. Ollama is a popular choice that bundles model weights and a serving environment into a simple application that runs on a developer's machine and exposes the model via a local API.61
Llamafile offers an even simpler approach, bundling the model and a compatible runtime into a single, executable file that can be run without any installation.61Vector Store: Lightweight, in-memory or file-based vector stores are ideal for local use. Chroma and FAISS are the most common choices, as they are easy to install (pip install) and require no separate server process.3
Orchestration Framework: The choice of framework dictates the ease of setup and development experience.
Framework Comparison for Local Deployment:
LangChain & LlamaIndex: Both frameworks offer excellent support for local deployment. The setup process is typically a pip install of the required libraries. Both have dedicated, well-documented integrations for local tools like Ollama, Llamafile, and Chroma.4 The developer has full control to write Python scripts that load documents from a local folder, chunk them, embed them using a local sentence-transformer model, store them in a local Chroma database, and query them with a local LLM served by Ollama. This provides maximum flexibility for prototyping custom logic.
RAGFlow: RAGFlow offers a different but equally powerful local experience. It is designed to be run via Docker, with a simple docker compose up command to launch the entire application stack, including the backend server, a database, and the user interface.25 This approach simplifies dependency management significantly. A developer can then use the web UI to upload local files, configure the RAG pipeline visually, and interact with the system, all without writing extensive code. This is ideal for users who prefer a GUI-driven workflow or want to stand up a complete RAG application quickly.
The maturation of local RAG tooling is a significant trend. It empowers individual developers, researchers, and small teams to build highly sophisticated and private AI applications without incurring cloud costs. This also enables a powerful "prototype locally, deploy to cloud" development lifecycle, where applications can be built and tested in a secure local environment before being configured for a production deployment.
Scenario 1.1: The local deployment landscape transforms developer capabilities
The democratization of RAG technology has produced seven standout frameworks for local deployment, each addressing distinct developer needs. LightRAG emerges as the performance leader, achieving the fastest processing speeds while using only 25% of traditional storage requirements. Its graph-based indexing system and dual-level retrieval architecture make it ideal for resource-constrained environments, requiring just 2-4 CPU cores and 4-8GB RAM for most applications.
LangChain maintains its position as the ecosystem leader with 105,000 GitHub stars and the most comprehensive integration library. While its complex chaining operations introduce medium latency, the framework's modular architecture and extensive documentation make it the default choice for general applications. Organizations implementing LangChain report 15-25% performance improvements when integrating advanced techniques like Chain-of-Retrieval Augmented Generation (CoRAG).
For developers prioritizing ease of use, LlamaIndex offers the optimal balance between functionality and accessibility. With 40,800 stars and over 300 integration packages, it specializes in data indexing and retrieval while maintaining a gentle learning curve. The framework's native GraphRAG cookbook enables developers to implement cutting-edge graph-based retrieval with minimal effort, achieving 10-20% accuracy improvements.
R2R distinguishes itself through production readiness, delivering the highest ingestion throughput at 160,000+ tokens per second. Its RESTful API approach and Docker-based deployment model appeal to teams seeking immediate scalability without complex configuration. The framework's agentic reasoning capabilities and multimodal content processing position it as the bridge between local development and enterprise deployment.
Specialized use cases find solutions in niche frameworks: RAGFlow excels at document processing with its deep understanding engine and visual web interface, while DSPy revolutionizes prompt engineering through automatic optimization. Stanford's research-backed framework reduces manual tuning requirements by systematically optimizing prompts and weights, though its steep learning curve limits adoption to research-oriented teams.
Scenario 2: Enterprise Deployment for Production at Scale
Deploying a RAG system for enterprise-wide use introduces stringent requirements for scalability, reliability, security, and governance. The architectural choices here are starkly different from a local setup and represent the core "Build vs. Buy" decision.
Architectural Blueprint 1: Open-Source on Kubernetes (The "Build" Path)
Deploying an open-source framework like Haystack, LangChain, or LlamaIndex to production typically involves a container-based architecture on a platform like Kubernetes (K8s).14 A typical deployment would include:
A containerized application (Docker image) running the framework's Python code, exposed as a REST API via a web server like FastAPI.
A scalable vector database like Milvus, Qdrant, or a self-hosted Weaviate instance, deployed as a separate StatefulSet within the K8s cluster to ensure data persistence.
A Kubernetes Ingress controller to manage external access to the API.
Horizontal Pod Autoscalers (HPA) configured to automatically scale the application and model inference pods based on CPU or memory load.
This architecture offers maximum control but requires significant DevOps and MLOps expertise to build, maintain, and secure.
Architectural Blueprint 2: Managed Platform (The "Buy" Path)
Using a commercial platform like Cohere or Amazon Bedrock dramatically simplifies the architecture from the user's perspective. The enterprise's application, running in its own VPC, makes a secure API call to the platform's endpoint.5 The platform vendor is responsible for the entire backend, including the data ingestion pipelines, the vector database, the model serving infrastructure, and all the associated scaling, failover, and security. For even greater security, these platforms can be deployed within the customer's cloud environment via a VPC or on-premises, ensuring that sensitive data never leaves the corporate network boundary.17In-Depth Analysis: Security, Governance, and Compliance
Security is the paramount concern for enterprise adoption, and it is where the difference between the "Build" and "Buy" paths is most pronounced. The following checklist provides a structured comparison.
Table 3: Enterprise RAG Security & Governance Checklist
The analysis reveals that for most enterprises, the most pragmatic and powerful path forward is a hybrid architecture. This approach involves using flexible open-source frameworks like LangChain or LlamaIndex to build the unique business logic and orchestration that constitutes a "core" competitive advantage. However, for the underlying, undifferentiated components—such as the LLM API, the vector database, and security guardrails—it leverages secure, scalable managed services. For example, an application could consist of a LangChain agent running in an AWS Lambda function that makes secure calls to Amazon Bedrock for generation and a managed Pinecone instance for retrieval. This hybrid model offers the best of both worlds: the control and flexibility of open-source for the application's unique logic, and the security, scalability, and reduced operational burden of managed platforms for the commodity infrastructure.
The Research Frontier: How 2025's Innovations are Reshaping RAG
The field of RAG is advancing at a blistering pace, with a remarkably short cycle from academic research to practical implementation. The innovations of 2025 are moving beyond simple retrieval and generation to create systems that are more dynamic, robust, and contextually aware.
Key Research Breakthroughs
Self-Reflection and Correction (Self-RAG): This paradigm, detailed in late 2024 and popularized in 2025, fundamentally changes the RAG pipeline from a static, linear process into a dynamic, self-correcting loop. A Self-RAG model uses special "reflection tokens" to learn to make decisions at inference time. It can decide if retrieval is even necessary for a given query, what to retrieve, and most importantly, critique the retrieved documents for relevance and its own generated response for factual accuracy and grounding.63 This adaptive, self-evaluating behavior makes the system more efficient and significantly more reliable.63
Advanced Retrieval and Chunking Strategies: Recognizing that retrieval is the bottleneck, researchers have developed more sophisticated techniques:
Sentence-Window Retrieval: This technique addresses a key flaw in naive chunking. It calculates vector embeddings on small, precise units (single sentences) to ensure accurate similarity matching. However, when it retrieves a relevant sentence, it passes a larger "window" of surrounding sentences to the LLM as context. This provides the LLM with the necessary local context to resolve ambiguities (like what a pronoun refers to) without diluting the semantic precision of the vector search itself.65
Long RAG: In contrast to breaking documents into ever-smaller chunks, Long RAG is designed to process much longer, more coherent retrieval units, such as entire document sections. This preserves context, reduces computational overhead, and is particularly effective in complex domains like legal or medical analysis where broad context is essential.63
GraphRAG: This approach moves beyond unstructured text by incorporating knowledge graphs. It represents entities and their relationships in a structured way. When a query is made, the system can traverse this graph to find not just directly relevant documents, but also contextually related information, enabling it to answer complex, multi-hop questions that traditional RAG struggles with.18
Handling Ambiguity and Misinformation (MADAM-RAG): Published in April 2025, MADAM-RAG proposes a novel multi-agent debate framework to improve robustness against noisy or conflicting information.67 Each retrieved document is assigned to an individual LLM agent. These agents then "debate" their findings over multiple rounds, with a central aggregator synthesizing the discussion. This process allows the system to surface and present multiple valid answers for ambiguous queries while identifying and filtering out factual inconsistencies and misinformation.67
The Multimodal Frontier (VideoRAG): Pushing beyond text and static images, research from early 2025 introduced VideoRAG, a framework that uses a corpus of videos as its knowledge source.68 It involves the dynamic retrieval of relevant video clips and uses powerful Large Video Language Models (LVLMs) to process both the visual frames and the transcribed audio, generating answers grounded in rich, temporal, multimodal content.68
From Theory to Practice: Assessing Implementation Maturity
The speed at which this research is being productized is a testament to the dynamism of the field. However, a clear hierarchy of adoption has emerged, dictated by the complexity of implementation.
Rapid Adoption (Self-RAG): Techniques that can be implemented as new modules or nodes within existing pipeline structures are adopted fastest. Self-RAG is a prime example. By mid-2025, LlamaIndex already offers a SelfRAGPack that provides a ready-to-use implementation of the core concepts.69
LangChain, through its flexible LangGraph library, provides detailed tutorials on how developers can build their own Self-RAG systems from scratch by adding grading and conditional routing nodes to their graphs.71Platform-Led Adoption (GraphRAG): Techniques that require tight integration between different infrastructure components are often productized first by large platform vendors. GraphRAG falls into this category. Amazon Bedrock has already integrated this capability, automatically leveraging GraphRAG when a user selects Amazon Neptune Analytics (a graph database) as the vector store for their Knowledge Base.18
Emerging Implementation (MADAM-RAG): More complex architectures like MADAM-RAG are on the next horizon. While no framework offers a one-click "MADAM-RAG" implementation yet, the necessary building blocks exist. An advanced developer could construct a similar multi-agent debate system today using a flexible agentic framework like LangGraph.
Bleeding Edge (VideoRAG): Techniques requiring entirely new data pipelines and specialized models, like VideoRAG, remain largely in the research domain. The need for sophisticated video processing, frame selection algorithms, and powerful LVLMs means this is not yet a standard, off-the-shelf feature in mainstream RAG frameworks.
This pattern of adoption reveals a key dynamic: the most impactful near-term innovations are those that refine and enhance the existing RAG graph, not those that require rebuilding it from the ground up. This places a premium on modular and extensible frameworks like LangGraph and DSPy, which are designed to facilitate the rapid experimentation and integration of new research concepts as they emerge.
Table 4: 2025 RAG Research - Adoption by Major Frameworks
Strategic Recommendations and Future Outlook for 2026 and Beyond
Navigating the complex and rapidly evolving RAG landscape of 2025 requires a clear strategic framework. The "best" solution is not a single product but a choice that aligns with an organization's specific goals, skills, and strategic posture. Based on the comprehensive analysis of the current market, deployment realities, and research trends, the following recommendations and future outlook can guide technology leaders in making durable, high-impact decisions.
The RAG Decision Matrix: A Framework for Selection
To move from analysis to action, organizations can use a decision matrix that maps their unique context to the most appropriate starting point. This involves assessing four key dimensions:
Team Skillset & Size: Does the team consist of PhD-level NLP researchers who can fine-tune models and optimize complex pipelines, or full-stack developers who need to ship applications quickly, or business analysts who need a low-code solution?
Project Goal: Is the objective a rapid proof-of-concept to demonstrate value, a robust production application serving thousands of users, or a state-of-the-art research project pushing the boundaries of performance?
Data Complexity & Sensitivity: Is the data simple, unstructured text (like PDFs), or does it involve complex, nested tables and multi-modal content? Does the data contain highly sensitive PII or PHI that is subject to strict regulatory and residency requirements?
Strategic Stance (Build vs. Buy): Does the organization prioritize granular control, customizability, and avoiding vendor lock-in (Build), or does it prioritize speed-to-market, security, and reduced operational overhead (Buy)?
Mapping these factors leads to clear recommendations:
For Rapid Prototyping with Mixed-Skill Teams: RAGFlow's visual interface and straightforward local deployment make it an ideal choice to quickly demonstrate value with minimal coding.
For Data-Centric Applications with High Retrieval Demands: LlamaIndex, with its focus on optimized indexing and retrieval, is the best starting point, especially for applications built around large, complex document sets.
For Complex, Multi-Step Agentic Systems: LangChain and LangGraph provide the most flexible and powerful orchestration engine for building sophisticated agents that use RAG as one of many tools in their arsenal.
For Enterprise-Grade Semantic Search: Haystack, with its production focus and strong hybrid search capabilities, is purpose-built for this use case, especially when integrated with enterprise databases like Elasticsearch.
For Pushing SOTA Performance: DSPy offers a new paradigm for research-oriented teams and those building performance-critical applications, enabling systematic optimization of the entire pipeline.
For Secure, Regulated Enterprise Deployments: Cohere's flexible deployment models (VPC/on-prem) and strong security posture make it a top contender. For organizations already committed to a major cloud, Amazon Bedrock and Google Vertex AI offer a path of least resistance with deep, secure integration.
Winning Architectures: The Dominance of the Hybrid Approach
For the majority of enterprises, the most effective, secure, and pragmatic long-term architecture will be a hybrid model. A purely open-source approach, while offering maximum control, places an immense and often underestimated burden on internal teams for security, compliance, and MLOps. A purely platform-based approach, while fast and secure, can be restrictive, expensive at scale, and lead to undesirable vendor lock-in.
The winning architecture combines the strengths of both. It uses open-source orchestration frameworks like LangChain or LlamaIndex to build the application's unique business logic—the "core" functionality that provides a competitive advantage. Simultaneously, it offloads the undifferentiated heavy lifting of the underlying infrastructure to managed platform services. This "best-of-both-worlds" blueprint might involve:
A LangGraph agent defining the custom workflow, running in a scalable, serverless environment like AWS Lambda or Google Cloud Run.
This agent makes secure API calls to a managed model provider like Amazon Bedrock or Cohere for generation.
Retrieval is handled by a managed vector database like Pinecone or Zilliz Cloud, chosen for its specific performance characteristics.
The entire application is deployed within a secure VPC, with access controlled by IAM and data protected by managed encryption services.
This hybrid approach allows an enterprise to focus its valuable engineering resources on what makes its application unique, while leveraging the security, scalability, and reliability of cloud platforms for the commodity components of the stack.22
The Future Trajectory: 2026 and Beyond
The current trends point towards an even more sophisticated and integrated future for RAG and generative AI.
The Convergence of RAG and Agents is Inevitable: The distinction between "RAG" and "agents" will continue to blur. By 2026, RAG will likely be fully subsumed into the broader concept of "agent memory" or "grounded reasoning".1 The frontier of innovation will shift further up the stack to areas like complex task decomposition, dynamic planning, and collaborative multi-agent systems, all of which will rely on RAG-like mechanisms as a foundational, assumed capability.
From Selling "Tools" to Selling "Outcomes": The market will mature beyond selling frameworks and APIs. The most successful vendors will be those who sell complete, verticalized solutions that solve specific business problems.22 Instead of offering a generic RAG platform, they will offer a "fully automated financial compliance verification agent" or a "proprietary clinical trial analysis assistant," built upon that platform. Value will be captured by those who move closest to the business outcome.
The Rise of the "RAG Compiler": The programmatic, optimization-focused paradigm introduced by DSPy is the logical endgame for high-performance RAG.7 As the low-hanging fruit of basic RAG is exhausted, the need for systematic, metric-driven optimization of complex pipelines will grow. We can expect to see these compiler-like features become more mainstream, integrated into both open-source frameworks and commercial platforms as a key differentiator for performance-critical applications.
Multimodality Becomes Standard: As Large Video Language Models (LVLMs) and other multimodal models become more capable and accessible, RAG over mixed data types—text, tables, images, audio, and video—will transition from a niche research area to a standard enterprise requirement. The ability to ground responses in a video tutorial, a product image, or a recorded meeting will become expected behavior. Platforms and frameworks that build a strong, flexible data foundation capable of handling this multimodal complexity will have a significant long-term advantage.22
Works cited
Halfway Through 2025 - A RAG Progress Report - RAGFlow, accessed July 10, 2025, https://ragflow.io/blog/halfway-through-2025-rag-progress-report
Create an agentic RAG application for advanced knowledge discovery with LlamaIndex, and Mistral in Amazon Bedrock | Artificial Intelligence - AWS, accessed July 10, 2025, https://aws.amazon.com/blogs/machine-learning/create-an-agentic-rag-application-for-advanced-knowledge-discovery-with-llamaindex-and-mistral-in-amazon-bedrock/
25+ Best Open Source RAG Frameworks in 2025 - Signity Solutions, accessed July 10, 2025, https://www.signitysolutions.com/blog/best-open-source-rag-frameworks
Top 5 RAG Frameworks for AI Applications - Analytics Vidhya, accessed July 10, 2025, https://www.analyticsvidhya.com/blog/2025/03/top-rag-frameworks-for-ai-applications/
Knowledge bases for Amazon Bedrock - AWS Prescriptive Guidance, accessed July 10, 2025, https://docs.aws.amazon.com/prescriptive-guidance/latest/retrieval-augmented-generation-options/rag-fully-managed-bedrock.html
Compare the Top 7 RAG Frameworks in 2025 | Pathway, accessed July 10, 2025, https://pathway.com/rag-frameworks/
RAG Frameworks You Should Know: Open-Source Tools for ..., accessed July 10, 2025, https://www.datacamp.com/blog/rag-framework
LlamaIndex vs LangChain vs Haystack vs Llama-Stack: A ... - Medium, accessed July 10, 2025, https://medium.com/@tuhinsharma121/llamaindex-vs-langchain-vs-haystack-vs-llama-stack-a-comparative-analysis-6d03aaa1bc36
LlamaIndex vs. LangChain: Which RAG Tool is Right for You? – n8n ..., accessed July 10, 2025, https://blog.n8n.io/llamaindex-vs-langchain/
One interface, integrate any LLM. - LangChain, accessed July 10, 2025, https://www.langchain.com/langchain
How do I implement security best practices in LangChain? - Milvus, accessed July 10, 2025, https://milvus.io/ai-quick-reference/how-do-i-implement-security-best-practices-in-langchain
Security Overview · langchain-ai/langchain - GitHub, accessed July 10, 2025, https://github.com/langchain-ai/langchain/security
How do I manage security and access control in LlamaIndex? - Milvus, accessed July 10, 2025, https://milvus.io/ai-quick-reference/how-do-i-manage-security-and-access-control-in-llamaindex
How do I deploy LlamaIndex on Kubernetes? - Milvus, accessed July 10, 2025, https://milvus.io/ai-quick-reference/how-do-i-deploy-llamaindex-on-kubernetes
How do I deploy Haystack on Kubernetes or Docker? - Milvus, accessed July 10, 2025, https://milvus.io/ai-quick-reference/how-do-i-deploy-haystack-on-kubernetes-or-docker
The best RAG platforms for building GenAI apps that actually ship ..., accessed July 10, 2025, https://learningdaily.dev/the-best-rag-platforms-for-building-genai-apps-that-actually-ship-8f43236a2530
Cohere AI: Reviews, Prices & Features - Appvizer, accessed July 10, 2025, https://www.appvizer.com/artificial-intelligence/generative-ai/cohere-ai
Foundation Models for RAG - Amazon Bedrock Knowledge Bases, accessed July 10, 2025, https://aws.amazon.com/bedrock/knowledge-bases/
Vertex AI RAG Engine: A developers tool, accessed July 10, 2025, https://developers.googleblog.com/en/vertex-ai-rag-engine-a-developers-tool/
What Great RAG as a Service Looks Like in 2025?, accessed July 10, 2025, https://www.azilen.com/blog/rag-as-a-service/
Amazon Bedrock Security and Privacy - AWS, accessed July 10, 2025, https://aws.amazon.com/bedrock/security-compliance/
Mid-2025 AI Update: What's Actually Working in Enterprise - Gradient Flow, accessed July 10, 2025, https://gradientflow.com/mid-2025-ai-update-whats-actually-working-in-enterprise/
What is Cohere pricing? | Unleash.so, accessed July 10, 2025, https://www.unleash.so/post/what-is-cohere-pricing
15 Best Open-Source RAG Frameworks in 2025 - Apidog, accessed July 10, 2025, https://apidog.com/blog/best-open-source-rag-frameworks/
Get started - RAGFlow, accessed July 10, 2025, https://ragflow.io/docs/dev/
LangChain vs LlamaIndex - Reddit, accessed July 10, 2025, https://www.reddit.com/r/LangChain/comments/1bbog83/langchain_vs_llamaindex/
LangChain release policy, accessed July 10, 2025, https://python.langchain.com/docs/versions/release_policy/
LangChain releases, accessed July 10, 2025, https://js.langchain.com/docs/versions/release_policy
LlamaIndex vs LangChain: Key Differences, Features & Use Cases - Openxcell, accessed July 10, 2025, https://www.openxcell.com/blog/llamaindex-vs-langchain/
LlamaIndex - LlamaIndex, accessed July 10, 2025, https://docs.llamaindex.ai/
run-llama/llama_index: LlamaIndex is the leading framework for building LLM-powered agents over your data. - GitHub, accessed July 10, 2025, https://github.com/run-llama/llama_index
Releases · run-llama/llama_index - GitHub, accessed July 10, 2025, https://github.com/run-llama/llama_index/releases
Getting Started with Building RAG Systems Using Haystack - KDnuggets, accessed July 10, 2025, https://www.kdnuggets.com/getting-started-building-rag-systems-haystack
Building Advanced RAG Applications with Haystack and LangChain: A Comprehensive Guide | by Kirtisalunkhe | Jun, 2025 | Medium, accessed July 10, 2025, https://medium.com/@kirtisalunkhe15/building-advanced-rag-applications-with-haystack-and-langchain-a-comprehensive-guide-8251be04f0cf
Deployment - Haystack, accessed July 10, 2025, https://expediadotcom.github.io/haystack/docs/deployment/deployment.html
haystack-ai·PyPI, accessed July 10, 2025, https://pypi.org/project/haystack-ai/
Release Notes | Haystack - Deepset, accessed July 10, 2025, https://haystack.deepset.ai/release-notes
How to Use RAGFlow(Open Source RAG Engine): A Complete Guide - Apidog, accessed July 10, 2025, https://apidog.com/blog/ragflow/
RAGFlow | RAGFlow, accessed July 10, 2025, https://ragflow.io/
DSPy: The framework for programming—not prompting—language models - GitHub, accessed July 10, 2025, https://github.com/stanfordnlp/dspy
Stanford DSPy - Qdrant, accessed July 10, 2025, https://qdrant.tech/documentation/frameworks/dspy/
Beyond Prompt Engineering: How LLM Optimization Frameworks Like TEXTGRAD and DSPy Are Building the Next Generation of Reliable AI | by Adnan Masood, PhD. | Jun, 2025 | Medium, accessed July 10, 2025, https://medium.com/@adnanmasood/beyond-prompt-engineering-how-llm-optimization-frameworks-like-textgrad-and-dspy-are-building-the-6790d3bf0b34
DSPy 3.0 — and DSPy at Databricks - YouTube, accessed July 10, 2025, https://www.youtube.com/watch?v=grIuzesOwwU
Trust Center - LlamaIndex Inc, accessed July 10, 2025, https://security.llamaindex.ai/controls
Advancing RAG with Command R to Solve Real Business Problems - Cohere, accessed July 10, 2025, https://cohere.com/blog/advancing-rag-with-command-r-to-solve-real-business-problems-2
Security | Deploy AI Securely - Cohere, accessed July 10, 2025, https://cohere.com/security
Security - Cohere.io, accessed July 10, 2025, https://cohere.io/security
Cohere Inc | Trust Center, accessed July 10, 2025, https://trustcenter.cohere.com/
Security best practices to consider while fine-tuning models in Amazon Bedrock - AWS, accessed July 10, 2025, https://aws.amazon.com/blogs/machine-learning/security-best-practices-to-consider-while-fine-tuning-models-in-amazon-bedrock/
Security in Amazon Bedrock - AWS Documentation, accessed July 10, 2025, https://docs.aws.amazon.com/bedrock/latest/userguide/security.html
Vertex AI RAG Engine overview - Google Cloud, accessed July 10, 2025, https://cloud.google.com/vertex-ai/generative-ai/docs/rag-engine/rag-overview
Security controls for Vertex AI - Google Cloud, accessed July 10, 2025, https://cloud.google.com/vertex-ai/docs/general/vertexai-security-controls
Compliance and security controls | AI Applications - Google Cloud, accessed July 10, 2025, https://cloud.google.com/generative-ai-app-builder/docs/compliance-security-controls
Vertex AI Search for commerce documentation - Google Cloud, accessed July 10, 2025, https://cloud.google.com/retail/docs
Vertex AI documentation | Google Cloud, accessed July 10, 2025, https://cloud.google.com/vertex-ai/docs
Retrieval Augmented Generation: Your 2025 AI Guide - Collabnix, accessed July 10, 2025, https://collabnix.com/retrieval-augmented-generation-rag-complete-guide-to-building-intelligent-ai-systems-in-2025/
10 Open-Source LLM Frameworks Developers Can't Ignore in 2025 - Zilliz blog, accessed July 10, 2025, https://zilliz.com/blog/10-open-source-llm-frameworks-developers-cannot-ignore-in-2025
Amazon Bedrock Pricing: How Much Does It Cost? - CloudZero, accessed July 10, 2025, https://www.cloudzero.com/blog/amazon-bedrock-pricing/
Amazon Bedrock Pricing Explained: What You Need to Know - Cloudchipr, accessed July 10, 2025, https://cloudchipr.com/blog/amazon-bedrock-pricing
AI Applications pricing - Google Cloud, accessed July 10, 2025, https://cloud.google.com/generative-ai-app-builder/pricing
Run models locally - Python LangChain, accessed July 10, 2025, https://python.langchain.com/docs/how_to/local_llms/
Security - Haystack, accessed July 10, 2025, https://www.haystackteam.com/more/security
The 2025 Guide to Retrieval-Augmented Generation (RAG) - Eden AI, accessed July 10, 2025, https://www.edenai.co/post/the-2025-guide-to-retrieval-augmented-generation-rag
Self-RAG: Learning to Retrieve, Generate and Critique through Self-Reflection, accessed July 10, 2025, https://selfrag.github.io/
Advanced RAG — Sentence Window Retrieval - Guillaume Laforge, accessed July 10, 2025, https://glaforge.dev/posts/2025/02/25/advanced-rag-sentence-window-retrieval/
Advanced RAG Hacks: Part 2 – Next-Level Techniques for 2025 - YouTube, accessed July 10, 2025, https://www.youtube.com/watch?v=-l5CbGz5VV0
Retrieval-Augmented Generation with Conflicting Evidence, accessed July 10, 2025, https://arxiv.org/pdf/2504.13079
VideoRAG: Retrieval-Augmented Generation over Video Corpus, accessed July 10, 2025, https://arxiv.org/pdf/2501.05874?
Simple self-RAG short form pack - Llama Hub, accessed July 10, 2025, https://llamahub.ai/l/llama-packs/llama-index-packs-self-rag?from=
Demystifying SELF-RAG: A Comprehensive Guide with Examples (Exclusive on LlamaIndex!) - YouTube, accessed July 10, 2025, https://www.youtube.com/watch?v=6MK96ea-3LU
Self-RAG using local LLMs, accessed July 10, 2025, https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_self_rag_local/
Self-RAG, accessed July 10, 2025, https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_self_rag/
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