Build a Retrieval Augmented Generation (RAG) App: Part 1

·

Introduction

One of the most powerful applications enabled by Large Language Models (LLMs) is sophisticated question-answering (Q&A) chatbots. These applications can answer questions about specific source information using a technique known as Retrieval Augmented Generation (RAG).

This multi-part tutorial will guide you through building a RAG application:

Core Concepts

What is RAG?

RAG combines two key components:

  1. Information retrieval from a knowledge base
  2. Response generation using an LLM

This approach allows systems to provide accurate, context-aware answers by:

Typical RAG Architecture

A standard RAG application has two main workflows:

Indexing Pipeline:

  1. Load data from source documents
  2. Split content into manageable chunks
  3. Store processed content in a searchable format

Query Processing:

  1. Retrieve relevant documents based on user queries
  2. Generate answers using retrieved context

Implementation Walkthrough

Setup Requirements

To follow this tutorial, you'll need:

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

Document Processing

Loading Content

We'll use web content as our data source:

from langchain_community.document_loaders import WebBaseLoader

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

Chunking Strategy

Optimal chunking improves retrieval accuracy:

from langchain_text_splitters import RecursiveCharacterTextSplitter

text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,
    chunk_overlap=200,
    add_start_index=True
)
all_splits = text_splitter.split_documents(docs)

Vector Storage

from langchain_core.vectorstores import InMemoryVectorStore

vector_store = InMemoryVectorStore(embeddings)
document_ids = vector_store.add_documents(documents=all_splits)

Building the RAG Chain

Retrieval Component

def retrieve(state: State):
    retrieved_docs = vector_store.similarity_search(state["question"])
    return {"context": retrieved_docs}

Generation Component

from langchain import hub
prompt = hub.pull("rlm/rag-prompt")

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}

Application Orchestration

from langgraph.graph import START, StateGraph

graph_builder = StateGraph(State).add_sequence([retrieve, generate])
graph_builder.add_edge(START, "retrieve")
graph = graph_builder.compile()

Advanced Techniques

Query Analysis

Enhance retrieval with structured queries:

class Search(TypedDict):
    query: Annotated[str, "Search query to run"]
    section: Annotated[Literal["beginning", "middle", "end"], "Section to query"]

def analyze_query(state: State):
    structured_llm = llm.with_structured_output(Search)
    query = structured_llm.invoke(state["question"])
    return {"query": query}

Performance Optimization

Key considerations:

FAQs

What are the main benefits of RAG?

RAG systems combine the strengths of information retrieval and language generation, allowing for:

How do I choose the right chunk size?

The optimal chunk size depends on:

What vector databases work best for RAG?

Popular options include:
👉 Highly engaging anchor text

Next Steps

In Part 2, we'll explore:

Key takeaways from Part 1:

For production deployments, consider: