Best AI tools for retrieval augmented generation (RAG)

[Last updated: Oct. 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.
• GraphRAG: Microsoft's knowledge graph-based retrieval (major 2024 release, now widely adopted)
• Corrective RAG (CRAG): Self-grading retrieval with quality thresholds

• Adaptive RAG: Dynamic strategy selection based on query complexity

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. GPT-5 launched in August 2025, followed by Claude Sonnet 4.5 in September. For production RAG applications, stick with major AI providers that integrate with your cloud ecosystems.

OpenAI LLMs

GPT-5 is OpenAI's smartest model with a 400K token context window. It leads on SWE-bench Verified and comes in three sizes: GPT-5, GPT-5-mini, and GPT-5-nano at $1.25/$10 per million tokens. 

Best for: High-performance AI, documentation generation, security analysis.

Anthropic LLMs

Claude Sonnet 4.5 (September 2025) is the world's top coding model. It works autonomously for 30+ hours on complex tasks and leads SWE-bench Verified. Pricing: $3/$15 per million tokens.

Best for: Safety-focused AI, long-running autonomous tasks, system architecture planning.

Google LLMs

Gemini 2.5 Pro and Flash are production-ready, with Flash-Lite as the most cost-efficient option. Gemini 2.5 Pro tops LMArena and scores 63.8% on SWE-bench Verified. Features 1-2 million token context windows and Deep Think reasoning mode.

Best for: Full-stack development, multimodal tasks, database optimization.

Mistral LLMs

Offers Codestral, Mistral Large, and Pixtral Large (proprietary) plus open-source options. Strong performance-to-cost ratio with specialized coding models.

Best for: Low-latency applications, edge deployment, specific language support.

Open source LLMs

Meta's Llama 4 Scout: 10 million token context window with mixture-of-experts architecture.

DeepSeek-R1 and V3: Competitive performance on reasoning and code generation (note: security considerations for enterprise).

Best for: Custom deployments, cost control, specific compliance requirements.

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.
• LangGraph: Framework for building agentic RAG systems with multi-agent collaboration
  
  

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

GraphRAG and knowledge graph tools

• Microsoft GraphRAG: Open-source graph-based RAG combining text extraction, network analysis, and LLM summarization
• Neo4j: Graph database commonly used with GraphRAG implementations
• LangGraph: Framework for building agentic RAG systems with multi-agent collaboration

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

• Hugging Face FinePDFs: Largest public PDF corpus with 3 trillion tokens across 475 million documents in 1733 languages. Excellent for long-context RAG applications, especially in legal and educational domains.

• 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.
• TruLens: Popular open-source evaluation framework for RAG. 
• 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.