Best AI tools for retrieval augmented generation (RAG)

[Last updated: March 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. 

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.

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. 

LLMs

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-4.5, o3-mini, o1, o1-mini, GPT-4o
Proprietary models

Advantages:

• Advanced accuracy, emotional intelligence (e.g., GPT-4.5), and reduced hallucinations.
• Versatility across industries.
• Strong ecosystem with API integrations.
  
  Ideal for: Enterprises needing high-performance, general-purpose AI.
  Limitations: Limited customization due to proprietary restrictions.

Anthropic LLMs

Models: Claude 3.5 Sonnet, Claude 3.5 Opus, Claude 3.5 Haiku
Proprietary models

Advantages:

• Strong focus on safety, transparency, and ethical AI usage.
• Minimizes harmful outputs, enhances explainability.
  
  Ideal for: Secure and compliant AI deployment, coding & software development
  Limitations: Less flexible due to closed model nature.

Google LLMs

Models: Gemini Flash 2.0, Gemini 1.5 pro, Gemini 1.0 Ultra
Proprietary models

Advantages:

• Fast, multilingual understanding.
• Integration with Google’s cloud and live knowledge systems.
  
  Ideal for: Real-time responses, seamless Google ecosystem integration.
  Limitations: Limited external collaboration and customization.

Meta LLMs

Models: Llama 3.2 models, Llama 3.1 models
Open source models

Advantages:

• Open-source flexibility for customization, fine-tuning, and collaboration.
  
  Ideal for: Researchers and developers needing adaptable AI.
  Limitations: May lack enterprise support and built-in safety features.

DeepSeek LLMs

Models: DeepSeek-R1
Open source models

Advantages:

• Developed with a focus on scientific and research applications.
• Optimized for high accuracy in specialized domain tasks.
  
  Ideal for: Researchers and scientists who need tailored language processing capabilities.
  Limitations: Currently limited to a single model and relatively new in the LLM ecosystem.

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

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.

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

Embedding models

• 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.

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.

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.

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.

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.

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.

LLM guardrails

• 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 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.