Top 5 RAG Frameworks for AI Applications

RAG has become a popular technology in 2025, it avoids the fine-tuning of the model which is expensive as well as time-consuming. There’s an increased demand for RAG frameworks in the current scenario, Lets Understand what are these. Retrieval-augmented generation (RAG) frameworks are essential tools in the field of artificial intelligence. They enhance the capabilities of Large Language Models (LLMs) by allowing them to retrieve relevant information from external sources. This leads to more accurate and context-aware responses. Here, we will explore five notable RAG frameworks: LangChain, LlamaIndex, LangGraph, Haystack, and RAGFlow. Each framework offers unique features that can improve your AI projects.

Table of contents

• LangChain
• LlamaIndex
• LangGraph
• Haystack
• RAGFlow
• Conclusion

1. LangChain

LangChain is a flexible framework that simplifies the development of applications using LLMs. It provides tools for building RAG applications, making integration straightforward.

• Key Features:
  
  • Modular design for easy customization.
  • Supports various LLMs and data sources.
  • Built-in tools for document retrieval and processing.
  • Suitable for chatbots and virtual assistants.

Here’s the hands-on:

Install the following libraries

! pip install langchain_community tiktoken langchain-openai langchainhub chromadb langchain

Set up OpenAI API key and os environment

from getpass import getpass
openai = getpass("OpenAI API Key:")
import os
os.environ["OPENAI_API_KEY"] = openai

Import the following dependencies

import bs4
from langchain import hub
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.vectorstores import Chroma
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI, OpenAIEmbeddings

Loading the document for RAG using WebBase Loader (replace with your own Data)

# Load Documents
loader = WebBaseLoader(
web_paths=("https://lilianweng.github.io/posts/2023-06-23-agent/",),
bs_kwargs=dict(
parse_only=bs4.SoupStrainer(
class_=("post-content", "post-title", "post-header")
)
),
)
docs = loader.load()

Chunking the document using RecursiveCharacterTextSplitter

# Split
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
splits = text_splitter.split_documents(docs)

Storing the vector documents in ChromaDB

# Embed
vectorstore = Chroma.from_documents(documents=splits,
embedding=OpenAIEmbeddings())
retriever = vectorstore.as_retriever()

Pulling the RAG prompt from the LangChain hub and defining LLM

# Prompt
prompt = hub.pull("rlm/rag-prompt")
# LLM
llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)

Processing the retrieved docs

# Post-processing
def format_docs(docs):
return "\n\n".join(doc.page_content for doc in docs)

Creating the RAG chain

# Chain
rag_chain = (
{"context": retriever | format_docs, "question": RunnablePassthrough()}
| prompt
| llm
| StrOutputParser()

Invoking the chain with the question

# Question
rag_chain.invoke("What is Task Decomposition?")

Output

‘Task Decomposition is a technique used to break down complex tasks into<br> smaller and simpler steps. This approach helps agents to plan ahead and<br> tackle difficult tasks more effectively. Task decomposition can be done<br> through various methods, including using prompting techniques, task-specific<br> instructions, or human inputs.’

Also Read: Find everything about LangChain Here.

2. LlamaIndex

LlamaIndex, previously known as the GPT Index, focuses on organizing and retrieving data efficiently for LLM applications. It helps developers access and use large datasets quickly.

• Key Features:
  
  • Organizes data for fast lookups.
  • Customizable components for RAG workflows.
  • Supports multiple data formats, including PDFs and SQL.
  • Integrates with vector stores like Pinecone and FAISS.

Here’s the hands-on:

Install the following dependencies

!pip install llama-index llama-index-readers-file
!pip install llama-index-embeddings-openai
!pip install llama-index-llms-openai

Import the following dependencies and initialize the LLM and embeddings

from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
llm = OpenAI(model='gpt-4o')
embed_model = OpenAIEmbedding()
from llama_index.core import Settings
Settings.llm = llm
Settings.embed_model = embed_model

Download the data (You can replace it with your data)

!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/10k/uber_2021.pdf' -O './uber_2021.pdf'

Read the data using SimpleDirectoryReader

from llama_index.core import SimpleDirectoryReader

documents = SimpleDirectoryReader(input_files=["/content/uber_2021.pdf"]).load_data()

Chunking the document using TokenTextSplitter

from llama_index.core.node_parser import TokenTextSplitter
splitter = TokenTextSplitter(
chunk_size=512,
chunk_overlap=0,
)
nodes = splitter.get_nodes_from_documents(documents)

Storing the vector embeddings in VectorStoreIndex

from llama_index.core import VectorStoreIndex
index = VectorStoreIndex(nodes)
query_engine = index.as_query_engine(similarity_top_k=2)
Invoking the LLM using RAG
response = query_engine.query("What is the revenue of Uber in 2021?")
print(response)

Output

‘The revenue of Uber in 2021 was $171.7 million.

3. LangGraph

LangGraph connects LLMs with graph-based data structures. This framework is useful for applications that require complex data relationships.

• Key Features:
  
  • Efficiently retrieves data from graph structures.
  • Combines LLMs with graph data for better context.
  • Allows customization of the retrieval process.

Code

Install the following dependencies

%pip install --quiet --upgrade langchain-text-splitters langchain-community langgraph langchain-openai

Initialise the model, embeddings and Vector database

from langchain.chat_models import init_chat_model
llm = init_chat_model("gpt-4o-mini", model_provider="openai")
from langchain_openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
from langchain_core.vectorstores import InMemoryVectorStore
vector_store = InMemoryVectorStore(embeddings)

Import the following dependencies

import bs4
from langchain import hub
from langchain_community.document_loaders import WebBaseLoader
from langchain_core.documents import Document
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langgraph.graph import START, StateGraph
from typing_extensions import List, TypedDict

Download the dataset using WebBaseLoader(replace it with your own dataset)

# Load and chunk contents of the blog
loader = WebBaseLoader(
web_paths=("https://lilianweng.github.io/posts/2023-06-23-agent/",),
bs_kwargs=dict(
parse_only=bs4.SoupStrainer(
class_=("post-content", "post-title", "post-header")
)
),
)
docs = loader.load()

Chunking of the document using RecursiveCharacterTextSplitter

text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
all_splits = text_splitter.split_documents(docs)
# Index chunks
_ = vector_store.add_documents(documents=all_splits)

Extracting the prompt from the LangChain hub

# Define prompt for question-answering
prompt = hub.pull("rlm/rag-prompt")
Defining the State, Nodes and edges in Langgraph
Define state for application
class State(TypedDict):
question: str
context: List[Document]
answer: str
# Define application steps
def retrieve(state: State):
retrieved_docs = vector_store.similarity_search(state["question"])
return {"context": retrieved_docs}
def generate(state: State):
docs_content = "\n\n".join(doc.page_content for doc in state["context"])
messages = prompt.invoke({"question": state["question"], "context": docs_content})
response = llm.invoke(messages)
return {"answer": response.content}

Compiling the Graph

# Compile application and test
graph_builder = StateGraph(State).add_sequence([retrieve, generate])
graph_builder.add_edge(START, "retrieve")
graph = graph_builder.compile()

Invoking the LLM for RAG

response = graph.invoke({"question": "What is Task Decomposition?"})
print(response["answer"])

Output

Task Decomposition is the process of breaking down a complicated task into<br> smaller, manageable steps. This can be achieved using techniques like Chain<br> of Thought (CoT) or Tree of Thoughts, which guide models to reason step by<br> step or evaluate multiple possibilities. The goal is to simplify complex<br> tasks and enhance understanding of the reasoning process.

4. Haystack

Haystack is an end-to-end framework for developing applications powered by LLMs and transformer models. It excels in document search and question answering.

• Key Features:
  
  • Combines document search with LLM capabilities.
  • Uses various retrieval methods for optimal results.
  • Offers pre-built pipelines for quick development.
  • Compatible with Elasticsearch and OpenSearch.

Here’s the hands-on:

Install the following Dependencies

!pip install haystack-ai
!pip install "datasets>=2.6.1"
!pip install "sentence-transformers>=3.0.0"
Import the VectorStore and initialise it
from haystack.document_stores.in_memory import InMemoryDocumentStore
document_store = InMemoryDocumentStore()

Loading the inbuilt dataset from the dataset library

from datasets import load_dataset
from haystack import Document
dataset = load_dataset("bilgeyucel/seven-wonders", split="train")
docs = [Document(content=doc["content"], meta=doc["meta"]) for doc in dataset]

Downloading the Embedding model (you can replace it with OpenAI embeddings also)

from haystack.components.embedders import SentenceTransformersDocumentEmbedder
doc_embedder = SentenceTransformersDocumentEmbedder(model="sentence-transformers/all-MiniLM-L6-v2")
doc_embedder.warm_up()
docs_with_embeddings = doc_embedder.run(docs)
document_store.write_documents(docs_with_embeddings["documents"])

Storing the embeddings in VectorStore

from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
retriever = InMemoryEmbeddingRetriever(document_store)

Defining the prompt for RAG

from haystack.components.builders import ChatPromptBuilder
from haystack.dataclasses import ChatMessage
template = [
ChatMessage.from_user(
"""
Given the following information, answer the question.
Context:
{% for document in documents %}
{{ document.content }}
{% endfor %}
Question: {{question}}
Answer:
"""
)
]
prompt_builder = ChatPromptBuilder(template=template)

Initializing the LLM

from haystack.components.generators.chat import OpenAIChatGenerator
chat_generator = OpenAIChatGenerator(model="gpt-4o-mini")

Defining the Pipeline nodes

from haystack import Pipeline
basic_rag_pipeline = Pipeline()
# Add components to your pipeline
basic_rag_pipeline.add_component("text_embedder", text_embedder)
basic_rag_pipeline.add_component("retriever", retriever)
basic_rag_pipeline.add_component("prompt_builder", prompt_builder)
basic_rag_pipeline.add_component("llm", chat_generator)

Connecting the nodes to each other

# Now, connect the components to each other
basic_rag_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
basic_rag_pipeline.connect("retriever", "prompt_builder")
basic_rag_pipeline.connect("prompt_builder.prompt", "llm.messages")

Invoking the LLM using RAG

question = "What does Rhodes Statue look like?"
response = basic_rag_pipeline.run({"text_embedder": {"text": question}, "prompt_builder": {"question": question}})
print(response["llm"]["replies"][0].text)

Output

Batches: 100%<br><br> 1/1 [00:00<br>‘The Colossus of Rhodes, a statue of the Greek sun-god Helios, is believed to<br> have stood approximately 33 meters (108 feet) tall and was constructed with<br> iron tie bars and brass plates forming its skin, filled with stone blocks.<br> Although the specific details of its appearance are not definitively known,<br> contemporary accounts suggest that it had curly hair with bronze or silver<br> spikes radiating like flames on the head. The statue likely depicted Helios<br> in a powerful, commanding pose, possibly with one hand shielding his eyes,<br> similar to other representations of the sun god from the time. Overall, it<br> was designed to project strength and radiance, celebrating Rhodes' victory<br> over its enemies.’

5. RAGFlow

RAGFlow focuses on integrating retrieval and generation processes. It streamlines the development of RAG applications.

• Key Features:
  
  • Simplifies the connection between retrieval and generation.
  • Allows for tailored workflows to meet project needs.
  • Integrates easily with various databases and document formats.

Here’s the hands-on:

Sign up at the RAGFlow and then Click on Try RAGFlow

Then Click on Create Knowledge Base

Then Go to Model Providers and select the LLM model that you want to use, We are using Groq here and paste its API key.

Then Go to System Model settings and select the chat model from there.

Now go to datasets and upload the pdf you want, then click on the Play button near the Parsing status column and wait for the pdf to get parsed.

Now go to the chat section create an assistant there, Give it a name and also select the knowledge base that you created.

Then create a new chat and ask the question it will perform RAG over your knowledge base and answer accordingly.

Conclusion

RAG has become an important technology for custom enterprise datasets in recent times, hence the need for RAG frameworks has increased drastically. Frameworks like LangChain, LlamaIndex, LangGraph, Haystack, and RAGFlow represent significant advancements in AI applications. By using these frameworks, developers can create systems that provide accurate and relevant information. As AI continues to evolve, these tools will play an important role in shaping intelligent applications.

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