A team looking to build an application that uses a large language model (LLM) like OpenAI’s GPT-4 or Meta’s LLama 2 will inevitably run into this issue: How can we ensure that the responses generated by these models align with the specific business context? This is where retrieval augmented generation (RAG) comes in.

RAG brings together large language models (LLMs) and traditional information retrieval systems (like databases). By combining this additional knowledge with its language skills, the AI application can generate more accurate, up-to-date, and relevant responses to the business’s unique requirements.

The good news is that the market is full of tools that make implementing RAG easier. The complexity of the retrieval process, the nature of the data, and the desired output quality will determine the tools you use to develop a RAG pipeline.

Keep reading to learn more about the most popular RAG tools and how to pick the one that best matches your use case.

What are RAG tools?

Retrieval augmented generation improves your LLM’s ability to provide users with immediate access to accurate, timely, and relevant answers. Instead of paying to fine-tune the large language models, which is time-consuming and costly, you can develop RAG pipelines using RAG tools to get similar outcomes faster:

Answering complex questions – RAG lets LLMs to use external knowledge bases and specialized bodies of material to provide precise and detailed answers to challenging queries.
Generating updated content – RAG-powered LLMs can produce more factual and accurate documents, reports, and other content by using real-world data as input.
Improved LLM response accuracy – Retrieval augmented generation achieves this by supplementing answer production with real-time data relevant to industry, clients, and business, making your application less likely to hallucinate to fill in missing knowledge.

RAG in LLMs

We can classify RAG models and tools into three categories:

The first category includes LLMs that have already implemented RAG to increase output accuracy and quality.
The second category comprises RAG libraries and frameworks that LLMs can use.
In the third category, models and libraries can collaborate with one another or with LLMs to generate RAG models.

Here are a few examples of providers that offer RAG:

Tool	Description
ChatGPT Retrieval Plugin	OpenAI provides a retrieval plugin that combines ChatGPT with a retrieval-based system to improve answers. You can create a document database and utilize retrieval techniques to discover relevant information for ChatGPT answers.
HuggingFace Transformer Plugin	HuggingFace includes a transformer that generates RAG models.
Azure Machine Learning	You can add RAG into your AI using Azure AI Studio or code using Azure Machine Learning pipelines.
IBM Watsonx.ai	The model can use the RAG pattern to deliver factually accurate results.
Meta AI	Meta AI integrates retrieval and generation into a single framework. It is intended for applications that need to obtain knowledge from a huge corpus and produce meaningful responses.

RAG libraries and frameworks

The table below presents some of the most popular RAG libraries and frameworks:

Name	Description
FARM	Deepset’s internal framework for creating transformer-based natural language processing pipelines includes RAG.
Haystack	An end-to-end RAG framework for document search by Deepset.
REALM	Retrieval Augmented Language Model (REALM) Training is a Google toolbox for open-domain question answering using RAG.
LangChain	A toolbox for integrating language models with external knowledge sources. It bridges the gap between language models and external data, benefiting RAG’s retrieval and augmentation stages.
LlamaIndex	This framework specializes in indexing and retrieving information, which aids RAG’s retrieval step. It enables efficient indexing, making it ideal for applications that require quick data retrieval.

Integration frameworks

Integration frameworks such as Langchain and Dust make it easier to create context-aware, reasoning-enabled applications based on language models. These frameworks provide modular components and pre-configured chains to satisfy certain application requirements while also allowing for model customization. You can combine these frameworks with vector databases to use RAG in their LLMs.

Vector database

A vector database can contain multidimensional data, allowing Large Language Model (LLM) operations and making it easier to work with such data.

Here are a few examples of vector databases:

Database Name	Description
FAISS (Facebook AI Similarity Search)	This vector database specializes in efficient similarity searches across big datasets, making it perfect for vector matching.
Pinecone	A scalable vector search engine optimized for high-performance similarity searches, ideal for applications that require precise vector-based retrieval.
REALM	Retrieval Augmented Language Model (REALM) Training is a Google toolbox for open-domain question answering using RAG.
Milvus	An open-source vector database designed for developing and maintaining AI applications.
Weaviate	An open-source vector search engine with machine learning models for semantic search.

Other retrieval models

RAG is built on sequence-to-sequence and DPR models, so ML/LLM teams can mix the two to assure retrieval augmented generation. For example, these models include BM25, ColBERT, and DPR (Document Passage Retrieval).

Top 9 RAG tools

1. LangChain

LangChain is an open-source Python package and ecosystem that provides a comprehensive foundation for building applications with large language models (LLMs). It combines a modular and flexible design with a high-level interface, making it ideal for developing retrieval augmented generation systems.

Langchain enables the easy integration of various data sources, including documents, databases, and APIs, which can help with the generation process. This library offers a wide range of functionality and allows users to alter and combine various components to fit unique application requirements, making it easier to create dynamic and resilient language model applications.

Key Features:

Integrates with vector databases such Chroma, Pinecone, and FAISS
Load and retrieve data from databases, APIs, and local files for relevant context
Retrievers include BM25, Chroma, FAISS, Elasticsearch, Pinecone, and others
Loaders for PDF, text, web scraping, and SQL/NoSQL databases
Memory management involves retaining context across conversations to enhance the conversational experience
Generate dynamic prompts using templated structures
Customize prompts based on retrieved data to improve context

2. LlamaIndex

LlamaIndex (previously GPT Index) is a robust library for developing retrieval augmented generation (RAG) systems. It focuses on efficient indexing and retrieval from massive datasets.

LlamaIndex uses advanced techniques such as vector similarity search and hierarchical indexing to enable rapid and accurate retrieval of relevant information, improving the capabilities of generative language models.

The library interfaces effortlessly with common large language models (LLMs), allowing for the insertion of received data into the creation process and making it an effective tool for improving the responsiveness of LLM-based applications.

Key Features:

Multiple index types
- Vector Store Index: Stores data as dense vectors, allowing for quick similarity searches in applications such as document retrieval and recommendation systems
- List Index: A simple, sequential index for smaller datasets that allows for rapid linear searches
- Tree Index: This index uses a hierarchical structure to perform efficient semantic searches, making it ideal for complex queries requiring hierarchical data
- Keyword Table Index: A mapping table is used to facilitate keyword-based searches and provide quick access to data based on specific terms or tags
Retrieval Optimization: Effectively retrieves essential info with little latency
Document Loaders allow for data loading from several sources, including files (TXT, PDF, DOC, CSV), APIs, databases (SQL/NoSQL), and web scraping
Combines embedding models (OpenAI, Hugging Face) and vector database retrievers (BM25, DPR, FAISS, Pinecone)

3. Haystack

Deepset’s Haystack is an open-source natural language processing platform that specializes in creating RAG pipelines for search and question-answering systems. Its complete collection of tools and modular design enables the creation of adaptable and customizable RAG solutions.

The framework provides document retrieval, question answering, and generation components that support a variety of retrieval methods, including Elasticsearch and FAISS. Haystack also interfaces with cutting-edge language models such as BERT and RoBERTa, which improves its capacity to handle difficult query workloads.

It also has a user-friendly API and a web-based UI, allowing users to easily interact with the system and create successful question-and-answer and search applications.

Key Features:

Supports Elasticsearch, FAISS, SQL, and InMemory storage backends
GenerativePipeline: Combines retriever and generator (GPT-3/4)
Keyword-based retrieval with BM25
TransformersReader: Extractive QA using Hugging Face models
DensePassageRetriever retrieves dense embeddings using DPR
Readers: FARM.Reader: Extractive QA with Transformer models
EmbeddingRetriever: Custom embeddings using Hugging Face models
HybridPipeline: Combines many retrievers/readers for best performance
Built-in tools for assessing QA and search processes

4. RAGatouille

RAGatouille is a lightweight framework that simplifies the creation of RAG pipelines by combining the power of pre-trained language models with efficient retrieval approaches to generate highly relevant and coherent content. It abstracts the difficulties of retrieval and generation, emphasizing modularity and ease of usage.

The framework’s architecture is versatile and modular, allowing users to experiment with different retrieval algorithms and generation models. RAGatouille supports a variety of data sources, including text documents, databases, and knowledge graphs, and is flexible to numerous domains and use cases, making it an excellent solution for anyone wishing to employ RAG activities efficiently.

Key Features:

Large datasets are efficiently handled via improved retrieval
Generate reactions with OpenAI (GPT-3/4), Hugging Face Transformers, or Anthropic Claude
Data retrieval options include keyword-based (SimpleRetriever, BM25Retriever) and dense passage retrieval (DenseRetriever)
Create customisable prompt templates to ensure consistent question comprehension
Dask and Ray enable distributed processing

5. EmbedChain

EmbedChain is an open-source framework for developing chatbot-like applications enhanced with bespoke knowledge using embeddings and large language models (LLMs). It specializes on embedding-based retrieval for RAG, which uses dense vector representations to extract useful information from big datasets quickly.

EmbedChain offers a simple and clear API for indexing and querying embeddings, making it easy to integrate into retrieval augmented generation workflows. It supports many embedding models, including BERT and RoBERTa, and provides flexibility through similarity metrics and indexing schemes, boosting its capacity to adjust applications to individual requirements.

Key Features:

Supports embedding models such as OpenAI, BERT, RoBERTa, and Sentence Transformers
Collects data from files (TXT, PDF, DOC, CSV), APIs, and web scraping
Embeddings enable efficient and precise retrieval
A simple interface allows you to quickly design and deploy RAG systems
Offers a simple API for indexing and querying embeddings

6. NeMo Guardrails

NeMo Guardrails is an open-source framework for easily integrating programmable guardrails into LLM-based conversational apps. Guardrails (or rails) are specific methods of controlling the output of a large language model, such as not discussing politics, responding in a particular way to specific user requests, following a predefined dialog path, using a specific language style, extracting structured data, etc.

Key Features:

Building trustworthiness, safety, and security. You can design rails to guide and protect talks and specify your LLM-based application’s behavior on specified topics and restrict it from partaking in discussions on other issues
An LLM can be connected to other services (also known as tools) in a seamless and secure manner
You can direct the LLM to follow predefined conversational paths, allowing you to create the interaction in line with conversation design best practices and impose standard operating procedures (for example, authentication and support).

7. Verba

Verba is an open-source RAG chatbot driven by Weaviate. It makes it easier to explore datasets and extract insights by providing a user-friendly interface from start to finish. Verba distinguishes itself by supporting local deployments or integration with LLM providers such as OpenAI, Cohere, and HuggingFace, as well as its ease of setup and versatility in handling diverse data types.

Its primary capabilities include easy data import, intelligent query resolution, and quicker searches through semantic caching, making it perfect for developing sophisticated RAG applications.

Key Features:

Local Embedding and Generation Models driven by Ollama
HuggingFace powers local embedding models, while Cohere, Anthrophic, and OpenAI provide generation models
Hybrid search that combines semantic search with keyword search
Autocomplete Suggestion: Verba proposes autocomplete
Filtering: You can apply filters before completing retrieval augmented generation (e.g., documents, document kinds, etc.)
Customizable metadata: Free control over metadata
Async Ingestion: Ingest data asynchronously to accelerate the process

8. Phoenix

Phoenix is an open-source AI observability platform that enables experimentation, evaluation, and debugging. Phoenix can run almost anywhere, including your Jupyter notebook, local workstation, containerized deployment, or in the cloud.

Key features:

Tracing: Use OpenTelemetry-based instrumentation to trace the runtime of your LLM application
Evaluation: Use LLMs to benchmark your application’s performance through response and retrieval evaluations
Datasets: Create versioned datasets containing examples to experiment with and evaluate them with the option of fine tuning
Experiments: Monitor and assess changes to cues, LLMs, and retrieval
Phoenix supports popular frameworks (LlamaIndex, LangChain, Haystack, DSPy) and LLM providers (OpenAI, Bedrock) without bias towards vendors or languages.

9. MongoDB

MongoDB is an open-source NoSQL database that prioritizes scalability and performance. It follows a document-oriented approach and supports data types comparable to JSON. This flexibility enables more dynamic and fluid data representation, making MongoDB popular for online applications, real-time analytics, and large-scale data management.

MongoDB enables extensive queries, full index support, replication, and sharding, as well as sophisticated high availability and horizontal scaling capabilities. MongoDB Atlas Vector Search enables semantic similarity searches on your data, which may be used with LLMs to create AI-powered apps.

Key Features

Atlas Vector Search lets you store vector embeddings alongside your source data and metadata, taking advantage of the document model’s capabilities
These vector embeddings can then be queried using an aggregation pipeline for a quick semantic similarity search on the data, employing an approximate closest neighbors approach

Conclusion

Retrieval augmented generation (RAG) is a powerful method for changing how we interact with language models. By combining the strengths of generative models and data retrieval, RAG systems can provide extremely accurate and contextually relevant responses. The best RAG tools or libraries we’ve reviewed include various features and capabilities that can help teams build more advanced natural language processing systems.

Whether you’re creating a chatbot, a question-and-answer system, or a content production platform, RAG has the potential to elevate your project to the next level – be it via a one of the RAG tools or RAG as a service.

Top 9 RAG Tools to Boost Your LLM Workflows

What are RAG tools?