Best AI tools for retrieval augmented generation (RAG)

[Last updated: June 2025]

If you want to build AI apps with the latest LLMs, but you want it to give expert answers for your business context, you need retrieval augmented generation (RAG). 

The choice of tools you use to build RAG largely depends on the specific needs of your implementation – e.g.  the complexity of the retrieval process, the nature of the data, and the desired output quality.

Why use RAG?

RAG extends your LLM's ability to give users immediate access to accurate, real-time, and relevant answers. So when one of your employees or customers asks your LLM a question, they get answers trained on your secure business data.

Instead of paying to finetune the LLM, which is time consuming and expensive, you can build RAG pipelines to get these kinds of results faster:

• LLMs that answer complex questions: RAG allows LLMs to tap into external knowledge bases and specific bodies of information to answer challenging questions with precision and detail.
• LLMs that generate up-to-date content: By grounding outputs in real-world data, RAG-powered LLMs can create more factual and accurate documents, reports, and other content.
• Increase LLM response accuracy: RAG augments answer generation with real-time data that’s relevant to your industry, customers, and business – so your chatbot is less likely to hallucinate to fill in missing information. 

Popular RAG techniques 

These advanced techniques move beyond basic RAG implementations to address specific challenges like context preservation, complex queries, and multi-modal content. By understanding and implementing these specialized methods, developers can significantly improve their RAG systems' performance and user experience.

• Contextual RAG: Enhanced RAG that provides contextual information with each chunk.
• Speculative RAG: A hybrid approach that drafts multiple responses and selects the best one.
• Self-querying RAG: Allows models to generate their own retrieval queries.
• HyDE (Hypothetical Document Embeddings): Technique to improve retrieval quality.
• Agents with RAG: Combining agentic systems with RAG for more powerful applications.
• Multi-modal RAG: RAG systems that can process and retrieve from both text and images.
• Recursive Retrieval RAG: Implements multiple rounds of retrieval for complex information needs.
• RAG with Reranking: Uses secondary models to improve the relevance of retrieved documents.

Here’s a popular GitHub repository showcasing these advanced RAG techniques.

List of the best tools for RAG

The stack for RAG is developing and changing constantly because the technology is so new.

For example, Langchain and LlamaIndex are popular entry points to RAG technology but developers have preferences between the two.  And many have given up on using Langchain or LlamaIndex at all to simplify their own designs.  

Here’s a breakdown of the best AI tools for RAG. It’s a mix of open source and closed.

Developer production tools

• Vellum.ai: Streamlines AI application deployment and scaling, focusing on infrastructure management and optimization.
• Vercel v0: AI-powered UI generator that creates React components with Tailwind CSS from natural language prompts. Offers versioning capabilities, preview functionality, and easy inline editing for rapid development of front-end interfaces for RAG applications.
• n8n: A powerful workflow automation platform that combines AI capabilities with business process automation. Particularly valuable for RAG implementations, it enables building custom knowledge chatbots by connecting to various data sources, integrating vector databases, and orchestrating LLM interactions through visual workflows.

Cloud platforms for RAG

• Azure AI: Use a single AI platform to build, evaluate, and deploy generative AI solutions and custom copilots.
  

  Microsoft has several built-in implementations for using Azure AI Search in a RAG solution.

  • Azure AI Studio, use a vector index and retrieval augmentation.
  • Azure OpenAI Studio, use a search index with or without vectors.
  • Azure Machine Learning, use a search index as a vector store in a prompt flow.
  
  
  
• AWS Bedrock: Fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like OpenAI, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon via a single API.
  
  AWS comes with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI. 
  
  Amazon AWS is one of the best places to build serverless RAG solutions.
  
  
• Google Cloud Vertex AI: Deliver generative AI powered experiences quickly, efficiently, and responsibly, powered by Google’s Gemini LLMs.
  
  Vertex AI search acts as an out-of-the-box RAG system. 
  
  
• Nvidia DGX Cloud: NVIDIA DGX Platform incorporates the best of NVIDIA software, infrastructure, and expertise in a modern, unified AI development and training solution. 
  
  You can build RAG chatbots with this NVIDIA workflow based on NeMo Retriever and NIM microservices. 

Best LLMs for RAG applications

Your choice of LLMs for RAG will change every few months. If you're building RAG for production applications, it's important to choose one of the big AI companies integrated with cloud ecosystems.

OpenAI LLMs

Models: GPT-4.1 Mini, GPT-4.1 Nano, GPT-4o, o3, o3-mini, o4-mini
Proprietary models

Advantages:

• GPT-4.1 models offer improved efficiency while maintaining strong performance in code optimization and security analysis
• GPT-4o excels at multimodal tasks, understanding code alongside visual elements for rapid prototyping
• o3 Series is specifically designed for reasoning-intensive tasks, particularly powerful for algorithmic optimization
• Robust documentation generation capabilities for creating comprehensive and well-structured documentation
• Excellent test coverage generation for identifying edge cases and generating exhaustive test suites
• Strong framework adaptation abilities to quickly adapt to different frameworks and libraries
• Advanced security vulnerability identification with suggested mitigation strategies
• Superior refactoring capabilities for implementing complex code restructuring
• Industry standard performance across multiple benchmarks

Ideal for: Enterprises needing high-performance, general-purpose AI with strong security awareness, complex refactoring projects, and thorough documentation generation.

Anthropic LLMs

Models: Claude Sonnet 4, Claude 3.7 Sonnet, Claude 3 Opus
Proprietary models

Advantages:

• State-of-the-art performance on software engineering tasks (70.3% accuracy on SWE-bench Verified)
• Hybrid reasoning with extended step-by-step thinking that's visible to users
• Extensive output capacity supporting up to 128K tokens
• Strong multimodal capabilities for understanding code in context of visual elements
• Exceptional system architecture insights for planning large refactors
• Advanced pattern recognition for identifying code patterns and suggesting optimizations
• Strong focus on safety, transparency, and ethical AI usage

Ideal for: Secure and compliant AI deployment, complex coding tasks, system architecture planning, and enterprise development workflows.

Google LLMs

Models: Gemini 2.5 Pro, Gemini 2.5 Flash, Gemini 2.0 Flash, Gemini 2.0 Flash-Lite
Proprietary models

Advantages:

• Deep Think reasoning mode that considers multiple hypotheses before responding
• Leading performance on LiveCodeBench and strong results on difficult coding benchmarks
• Exceptional multimodal understanding with 84% score on MMMU for multimodal reasoning
• Balanced full-stack expertise across frontend and backend development
• Configurable thinking budgets for controlling the balance between response quality and latency
• Superior database query optimization capabilities
• Excellent cross-language code translation abilities
• Fast multilingual understanding and processing

Ideal for: Full-stack development projects, visual programming contexts, database-heavy applications, and tasks requiring balanced capabilities across different programming languages.

Mistral LLMs

Models: Devstral, Mixtral 8x22B, Mixtral Vision, Codestral, Small World
Proprietary and open source models

Advantages:

• Specialized coding model optimized for low-latency tasks
• High-complexity analytical capabilities
• Multimodal capabilities combining vision and text
• Strong performance-to-cost ratio

Ideal for: Applications requiring specific language support, edge deployment, or specialized capabilities.

Open Source LLMs for RAG

Meta Llama 4 Models

Models: Llama 4 Scout, Llama 4 Maverick, Llama 4 Behemoth (preview)
Open source models

Advantages:

• First open-weight natively multimodal models with unprecedented context length support
• Built using mixture-of-experts (MoE) architecture for more efficient compute
• Llama 4 Scout offers industry-leading 10 million token context window
• Strong performance on coding, reasoning, multilingual, and image benchmarks
• Advanced image grounding capabilities for more precise visual question answering

Ideal for: Developers building multimodal applications requiring both text and image understanding, applications needing extremely long context windows, and reasoning-heavy use cases.

DeepSeek Models

Models: DeepSeek-R1, DeepSeek-V3, DeepSeek-Coder-V2, DeepSeek-VL
Open source models

Advantages:

• Developed with a focus on scientific and research applications.
• Optimized for high accuracy in specialized domain tasks.
• Excellent reasoning and code generation capabilities.

Ideal for: Researchers and scientists who need tailored language processing capabilities.

Nvidia LLMs

Models: Nemotron-4 340B, Nemotron-4 340B Instruct, Nemotron-4 340B Reward
Open source models

Advantages:

• Base model for synthetic data generation
• Fine-tuned for English conversational AI
• Strong integration with NVIDIA's enterprise AI ecosystem

Ideal for: Organizations already invested in NVIDIA's AI infrastructure.

For an in-depth look at the capabilities of each model, check out our guide to the most powerful LLMs.

RAG frameworks and libraries

• LangChain: A toolkit designed to integrate language models with external knowledge sources. Bridges the gap between language models and external data, useful for both the retrieval and augmentation stages in RAG.
• LlamaIndex: Specializes in indexing and retrieving information, aiding the retrieval stage of RAG. Facilitates efficient indexing, making it suitable for applications requiring rapid and relevant data retrieval.
• DSPy: A declarative programming framework for optimizing RAG in large language models.
• Pathway: Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG.

Reminder that some engineers have given up on Langchain and LlamaIndex and build their own RAG framework to simplify their designs.

RAG embedding models

• OpenAI text-embedding-3-large/small: The latest embedding models from OpenAI that have replaced Ada 002 with significant improvements in performance.
• Google Gemini Embeddings: Google's embedding models designed specifically for RAG applications.
• Mistral Embed: Mistral's embedding model that complements their LLM offerings.
• OpenAI's Ada 002: One of the original embedding models for RAG used for text search, code search, and sentence similarity tasks that gets comparable performance on text classification.
• Cohere embed v3 models: Embed v3 offers state-of-the-art performance per trusted MTEB and BEIR benchmarks.
• e5-large-v2: This open source model available on Hugging Face has 24 layers and the embedding size is 1024.
• BERT-based open source models like bge-large/small: These are becoming increasingly popular for cost-effective embeddings.

RAG data retrieval and search index

• Elasticsearch: A distributed search and analytics engine for textual data retrieval.
• Apache Solr: Supports high-volume web traffic and complex search criteria.
• MongoDB Atlas Vector Search: Perform semantic similarity searches on your data, which can be integrated with LLMs to build AI-powered applications. 
• Azure AI Search: Azure AI Search is a proven solution for information retrieval and accurate, hyper-personalized responses in your Gen AI applications.
• Haystack: Simplifies the integration of retrieval into the generation process, making it easier to construct search systems. An NLP framework that simplifies the building of search systems, integrating well with Elasticsearch and DPR.
• Dense Passage Retrieval (DPR): Optimized for retrieving relevant passages from extensive text.
• ColBERT: A BERT-based ranking model for high-precision retrieval.

RAG vector databases

• FAISS (Facebook AI Similarity Search): Specializes in efficient similarity searches within large datasets, ideal for vector matching.
• Pinecone: A scalable vector search engine designed for high-performance similarity search, crucial for applications requiring precise vector-based retrieval.
• Milvus: Open source vector database built for developing and maintaining AI applications.
• Weaviate: An open-source vector search engine that includes machine learning models for semantic search, making it a robust tool for RAG applications.
• PostgreSQL: A robust open source relational database often used for structured data storage and retrieval.
• Qdrant: An open-source vector database that has gained significant traction for RAG applications.
• Chroma: A lightweight vector database designed specifically for RAG workflows.
• pgvector: PostgreSQL extension for vector similarity search that's increasingly popular for RAG.
• Vespa: An open-source platform for hybrid search and machine learning-powered relevance ranking.

RAG knowledge bases and datasets

• Wikipedia dump: Useful for creating a comprehensive knowledge base that can be indexed in Elasticsearch and used for retrieval.
• Common crawl: Extensive repository for diverse data extraction.
• SQuAD (Stanford Question Answering Dataset): Ideal for fine-tuning your model on question-answering tasks.

RAG document parsing and chunking

• Vertex AI Search: can be optimized for RAG with document chunking to break up your documents into chunks.
• Haystack document splitter: divides a list of text documents into a list of shorter text Documents. Useful for long texts that otherwise wouldn't fit into the maximum text length of language models and can also speed up question answering.
• Unstructured.io: Popular tool for extracting content from various document formats for RAG.
• LlamaHub: Provides data connectors for various data sources to simplify RAG ingestion.

RAG models and fine tuning

• Hugging Face's RAG transformer: Provides a comprehensive collection of pre-trained models, including RAG.
• PyTorch: Flexible for RAG model development and training.
• TensorFlow: End-to-end platform for machine learning models, including RAG applications.

RAG evaluation tools

• RAGAS: An open-source framework for evaluating RAG pipelines.
• Snowflake AI observability in Cortex: Tools for evaluating and improving RAG system performance.
• RAGChecker LlamaIndex Evaluation Framework: RAGChecker is an advanced automatic evaluation framework designed to assess and diagnose Retrieval-Augmented Generation (RAG) systems. It provides a comprehensive suite of metrics and tools for in-depth analysis of RAG performance.

LLM guardrails for RAG applications

• Nvidia NeMo: NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.

How do I hire a team to build RAG applications for LLMs?

To build RAG with the latest, cost-effective tech stack you need AI experts. Hiring internally could take 6-18 months but you need to start building AI solutions, not next year. That’s why Codingscape exists. 

We can assemble a senior AI software engineering team for you in 4-6 weeks. It’ll be faster to get started, more cost-efficient than internal hiring, and we’ll deliver high-quality results quickly. We’ve been busy building RAG capabilities for our partners and helping them complete their AI roadmaps.

Zappos, Twilio, and Veho are just a few companies that trust us to build their software and systems with a remote-first approach.

You can schedule a time to talk with us here. No hassle, no expectations, just answers.