RAG Frameworks Comparison

Choose the right Retrieval-Augmented Generation (RAG) framework for your project on Clore.ai GPU servers.

RAG (Retrieval-Augmented Generation) lets LLMs answer questions using your own documents. This guide compares the four leading frameworks: LangChain, LlamaIndex, Haystack, and RAGFlow — covering features, performance, and when to use each.

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Quick Decision Matrix

	LangChain	LlamaIndex	Haystack	RAGFlow
Best for	General LLM apps	Document Q&A	Enterprise search	Self-hosted RAG
Learning curve	Medium	Low-Medium	Medium-High	Low
Flexibility	Very high	High	High	Medium
Built-in UI	No	No	No	Yes
GitHub stars	90K+	35K+	15K+	12K+
Language	Python	Python	Python	Python
License	MIT	MIT	Apache 2.0	Apache 2.0

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Overview

LangChain

LangChain is the most popular LLM orchestration framework. It provides a unified interface for chains, agents, memory, and RAG pipelines.

Philosophy: Everything is a chain of composable components.

from langchain.chains import RetrievalQA
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.llms import OpenAI

# Build RAG pipeline in 5 lines
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(docs, embeddings)
retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
chain = RetrievalQA.from_chain_type(llm=OpenAI(), retriever=retriever)
result = chain.run("What is the capital of France?")

LlamaIndex

LlamaIndex (formerly GPT Index) is purpose-built for document indexing and retrieval. It excels at connecting LLMs to diverse data sources.

Philosophy: Index first, query intelligently.

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

# Load and index documents
documents = SimpleDirectoryReader("data/").load_data()
index = VectorStoreIndex.from_documents(documents)

# Query
query_engine = index.as_query_engine()
response = query_engine.query("Summarize the main findings")
print(response)

Haystack

Haystack (by deepset) is an enterprise-grade NLP framework focused on search and Q&A pipelines. It has a component-based architecture with visual pipeline builder.

Philosophy: Modular pipelines with enterprise reliability.

from haystack.nodes import DensePassageRetriever, FARMReader
from haystack.pipelines import ExtractiveQAPipeline

retriever = DensePassageRetriever(document_store=document_store)
reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2")
pipeline = ExtractiveQAPipeline(reader, retriever)
result = pipeline.run(query="What is machine learning?", params={"Retriever": {"top_k": 10}})

RAGFlow

RAGFlow is an open-source RAG engine with a built-in web UI, document parsing, and knowledge base management. It's designed to be deployed as a complete solution.

Philosophy: Out-of-the-box RAG system, no coding required.

# RAGFlow is deployed via Docker Compose
# Configuration via web UI at localhost:80
version: "3"
services:
  ragflow:
    image: infiniflow/ragflow:latest
    ports:
      - "80:80"
    volumes:
      - ./ragflow-data:/ragflow/data

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Feature Comparison

Core RAG Features

Feature	LangChain	LlamaIndex	Haystack	RAGFlow
Vector store support	50+	30+	20+	Built-in
Document loaders	100+	50+	30+	Built-in
Hybrid search	✅	✅	✅	✅
Re-ranking	✅	✅	✅	✅
Multi-modal	✅	✅	Partial	✅
Streaming	✅	✅	✅	✅
Async support	✅	✅	✅	✅
Agents	✅	✅	✅	❌

Integration Ecosystem

Integration Type	LangChain	LlamaIndex	Haystack	RAGFlow
LLM providers	50+	30+	20+	10+
Vector DBs	Chroma, Pinecone, Weaviate, Qdrant, 40+ more	Chroma, Pinecone, Weaviate, 25+ more	Weaviate, Elasticsearch, 15+ more	Built-in InfiniFlow
Document types	PDF, Web, CSV, JSON, 80+	PDF, Web, CSV, DB, 40+	PDF, TXT, HTML, 20+	PDF, Word, Excel, PPT, Web
Cloud storage	S3, GCS, Azure	S3, GCS, Azure	S3, GCS	S3

Advanced RAG Features

Feature	LangChain	LlamaIndex	Haystack	RAGFlow
Query decomposition	✅	✅	✅	✅
HyDE (Hypothetical Document Embeddings)	✅	✅	❌	❌
Multi-hop retrieval	✅	✅	Partial	✅
Contextual compression	✅	✅	✅	✅
Self-RAG	✅	✅	❌	❌
GraphRAG	✅	✅ (PropertyGraph)	❌	✅
Citation tracking	Partial	✅	Partial	✅

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Performance Benchmarks

Retrieval Accuracy (RAG-Bench, 2024)

Benchmarks vary significantly by dataset and configuration. These are approximate figures from community benchmarks.

Framework	HotpotQA (F1)	Natural Questions (EM)	TriviaQA (Acc)
LangChain (RAG)	~68%	~42%	~72%
LlamaIndex (RAG)	~71%	~45%	~74%
Haystack (RAG)	~69%	~43%	~71%
RAGFlow (default)	~65%	~40%	~68%

Results depend heavily on chosen LLM, embedding model, and chunk size

Indexing Speed (10K documents, ~1KB each)

Framework	CPU Only	GPU Embeddings
LangChain	~120 sec	~18 sec
LlamaIndex	~110 sec	~15 sec
Haystack	~130 sec	~20 sec
RAGFlow	~150 sec	~25 sec

With text-embedding-ada-002 equivalent (1536 dims)

Query Latency (P50/P99, with pre-built index)

Framework	P50	P99	Notes
LangChain	450ms	1.2s	No re-ranking
LlamaIndex	400ms	1.0s	No re-ranking
Haystack	500ms	1.5s	With pipeline overhead
RAGFlow	600ms	2.0s	Includes UI/API overhead

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LangChain: Deep Dive

Strengths

✅ Largest ecosystem — 50+ integrations, massive community ✅ Agents and tools — build autonomous AI agents ✅ LangSmith — excellent observability and debugging ✅ LCEL — LangChain Expression Language for composing chains ✅ Memory systems — conversation history, entity memory

Weaknesses

❌ Complexity — can be over-engineered for simple tasks ❌ Frequent breaking changes — v0.1 vs v0.2 vs v0.3 migrations ❌ Heavy dependency — large install size ❌ Abstraction leakage — sometimes harder to debug

Best Use Cases

• Multi-step LLM pipelines with complex logic

• AI agents that use tools (web search, code execution, APIs)

• Applications needing conversation memory

• Projects needing maximum flexibility

Example: Advanced RAG with Sources

from langchain.chains import RetrievalQAWithSourcesChain
from langchain_community.vectorstores import Chroma
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter

# Setup
llm = ChatOpenAI(model="gpt-4", temperature=0)
embeddings = OpenAIEmbeddings()

# Index documents with metadata
splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
chunks = splitter.split_documents(documents)

vectorstore = Chroma.from_documents(
    chunks, 
    embeddings,
    persist_directory="./chroma_db"
)

# Build chain with source attribution
chain = RetrievalQAWithSourcesChain.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vectorstore.as_retriever(search_kwargs={"k": 5}),
    return_source_documents=True
)

result = chain({"question": "What are the main risks?"})
print(result["answer"])
print("Sources:", result["sources"])

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LlamaIndex: Deep Dive

Strengths

✅ Document-first design — best for complex document indexing ✅ Index types — Vector, Knowledge Graph, SQL, Keyword ✅ Sub-question engine — automatically decomposes complex queries ✅ Structured outputs — Pydantic integration ✅ Router query engine — intelligently routes to right index

Weaknesses

❌ Less agent-focused than LangChain ❌ Smaller ecosystem than LangChain ❌ Documentation can be inconsistent

Best Use Cases

• Document Q&A systems (PDFs, reports, wikis)

• Complex multi-document reasoning

• Knowledge graph construction

• Data-to-LLM bridges (databases, APIs)

Example: Multi-Document Query Engine

from llama_index.core import (
    VectorStoreIndex, 
    SimpleDirectoryReader,
    StorageContext,
    Settings
)
from llama_index.core.query_engine import RouterQueryEngine
from llama_index.core.tools import QueryEngineTool
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding

# Configure globally
Settings.llm = OpenAI(model="gpt-4")
Settings.embed_model = OpenAIEmbedding()

# Create separate indices for different document sets
annual_reports = SimpleDirectoryReader("./annual_reports").load_data()
tech_docs = SimpleDirectoryReader("./tech_docs").load_data()

index_reports = VectorStoreIndex.from_documents(annual_reports)
index_tech = VectorStoreIndex.from_documents(tech_docs)

# Build router that selects correct index
tools = [
    QueryEngineTool.from_defaults(
        query_engine=index_reports.as_query_engine(),
        description="Annual financial reports and business metrics"
    ),
    QueryEngineTool.from_defaults(
        query_engine=index_tech.as_query_engine(),
        description="Technical documentation and API references"
    )
]

router = RouterQueryEngine.from_defaults(query_engine_tools=tools)
response = router.query("What was the revenue growth last year?")

---

Haystack: Deep Dive

Strengths

✅ Enterprise-grade — production reliability ✅ Visual pipeline builder — Haystack Studio ✅ Annotation tool — built-in labeling UI ✅ Strong NLP — extractive QA, summarization ✅ deepset Cloud — managed deployment option

Weaknesses

❌ Steeper learning curve than competitors ❌ Smaller community than LangChain/LlamaIndex ❌ Less flexible for novel architectures

Best Use Cases

• Enterprise document search and Q&A

• Projects needing audit trails and observability

• Teams wanting visual pipeline design

• Production deployments with SLA requirements

Example: Hybrid Search Pipeline

from haystack import Pipeline
from haystack.components.retrievers import InMemoryBM25Retriever, InMemoryEmbeddingRetriever
from haystack.components.joiners import DocumentJoiner
from haystack.components.rankers import MetaFieldRanker
from haystack.components.generators import OpenAIGenerator
from haystack.components.builders import RAGPromptBuilder

# Build hybrid search pipeline
pipeline = Pipeline()
pipeline.add_component("bm25_retriever", InMemoryBM25Retriever(document_store=store, top_k=10))
pipeline.add_component("embedding_retriever", InMemoryEmbeddingRetriever(document_store=store, top_k=10))
pipeline.add_component("joiner", DocumentJoiner(join_mode="reciprocal_rank_fusion"))
pipeline.add_component("ranker", MetaFieldRanker(meta_field="score"))
pipeline.add_component("prompt_builder", RAGPromptBuilder())
pipeline.add_component("llm", OpenAIGenerator(model="gpt-4"))

# Connect components
pipeline.connect("bm25_retriever", "joiner.documents")
pipeline.connect("embedding_retriever", "joiner.documents")
pipeline.connect("joiner", "ranker")
pipeline.connect("ranker", "prompt_builder.documents")
pipeline.connect("prompt_builder", "llm")

result = pipeline.run({"bm25_retriever": {"query": "deep learning"}, 
                       "embedding_retriever": {"query": "deep learning"}})

---

RAGFlow: Deep Dive

Strengths

✅ Zero-code deployment — full UI included ✅ Advanced document parsing — tables, images, charts ✅ Knowledge base management — visual interface ✅ API included — REST API out of the box ✅ Agentic RAG — built-in agents

Weaknesses

❌ Less customizable than code-first frameworks ❌ Heavy resource requirement (Elasticsearch + Infinity DB) ❌ Limited LLM support vs LangChain ❌ Newer project — smaller community

Best Use Cases

• Non-developers needing RAG without coding

• Teams wanting a complete knowledge base product

• Internal enterprise wikis and documentation search

• Rapid prototyping of RAG applications

Deployment on Clore.ai

# docker-compose.yml for RAGFlow
version: "3"
services:
  ragflow:
    image: infiniflow/ragflow:v0.12.0
    container_name: ragflow
    ports:
      - "80:80"
      - "443:443"
    volumes:
      - ./ragflow-logs:/ragflow/logs
      - ./ragflow-data:/ragflow/data
    depends_on:
      - elasticsearch
      - infinity

  elasticsearch:
    image: elasticsearch:8.11.3
    environment:
      - discovery.type=single-node
      - ES_JAVA_OPTS=-Xms1g -Xmx1g
      - xpack.security.enabled=false
    volumes:
      - es_data:/usr/share/elasticsearch/data

  infinity:
    image: infiniflow/infinity:v0.3.0
    volumes:
      - infinity_data:/var/infinity

volumes:
  es_data:
  infinity_data:

docker compose up -d
# Access web UI at http://<server-ip>:80

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When to Use Which

Choose LangChain if:

• Building AI agents with tools (web search, code execution, APIs)

• Need maximum ecosystem flexibility

• Building complex multi-step pipelines

• Integrating with many different LLMs and data sources

• Team is comfortable with Python

Choose LlamaIndex if:

• Primary use case is document Q&A

• Working with complex document structures (tables, nested content)

• Need knowledge graphs or multi-index routing

• Want best-in-class document ingestion

• Building over structured data (databases, APIs)

Choose Haystack if:

• Enterprise environment with compliance requirements

• Need visual pipeline building tools

• Building on top of Elasticsearch

• Want extractive (not just generative) QA

• Team needs NLP pipeline observability

Choose RAGFlow if:

• Non-technical team needs self-service RAG

• Want a complete product, not a framework

• Rapid deployment is priority over customization

• Building an internal knowledge base

• Don't want to write Python code

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Running on Clore.ai: Resource Requirements

Framework	Min RAM	Min VRAM	Recommended GPU
LangChain	8GB	8GB (local LLM)	RTX 3080
LlamaIndex	8GB	8GB (local LLM)	RTX 3080
Haystack	16GB	8GB (local LLM)	RTX 3090
RAGFlow	32GB (RAM!)	16GB	A6000 / A100

RAGFlow needs more RAM: It runs Elasticsearch + InfinityDB + the app itself. Plan for at least 32GB system RAM. Haystack with Elasticsearch also benefits from 16GB+ RAM.

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Useful Links

• LangChain Docs

• LlamaIndex Docs

• Haystack Docs

• RAGFlow GitHub

• RAG Survey Paper (arxiv)

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Summary Recommendation

Simple document Q&A          → LlamaIndex
Complex AI agents            → LangChain
Enterprise search            → Haystack
No-code RAG product          → RAGFlow
Maximum flexibility          → LangChain
Best document understanding  → LlamaIndex

All four frameworks are excellent choices — the right one depends on your specific requirements, team skills, and deployment constraints. When in doubt, start with LlamaIndex for document-heavy use cases or LangChain if you need the broadest possible ecosystem.

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Clore.ai GPU Recommendations

Use Case	Recommended GPU	Est. Cost on Clore.ai
Development/Testing	RTX 3090 (24GB)	~$0.12/gpu/hr
Production	RTX 4090 (24GB)	~$0.70/gpu/hr
Large Scale	A100 80GB	~$1.20/gpu/hr

💡 All examples in this guide can be deployed on Clore.ai GPU servers. Browse available GPUs and rent by the hour — no commitments, full root access.