# How to Implement Retrieval-Augmented Generation (RAG): A Complete Developer Guide

> Learn how to implement retrieval-augmented generation using LlamaIndex or Haystack. This developer guide details combining LLMs with vector databases for enhanced AI applications.

- Repository: [Owain Lewis/awesome-artificial-intelligence](https://github.com/owainlewis/awesome-artificial-intelligence)
- Tags: how-to-guide
- Published: 2026-06-20

---

**Retrieval-augmented generation combines a large language model with an external vector database to retrieve up-to-date, domain-specific information at inference time, using frameworks like LlamaIndex or Haystack to orchestrate document ingestion, embedding, and retrieval.**

The `owainlewis/awesome-artificial-intelligence` repository curates the best open-source libraries for implementing retrieval-augmented generation without building pipelines from scratch. According to the source code analysis, the repository's [`README.md`](https://github.com/owainlewis/awesome-artificial-intelligence/blob/main/README.md) (lines 71-80) highlights RAG-focused projects including **LlamaIndex** and **Haystack**, while the [`pyproject.toml`](https://github.com/owainlewis/awesome-artificial-intelligence/blob/main/pyproject.toml) file pins compatible dependency versions for these frameworks.

## The Core RAG Architecture

A typical retrieval-augmented generation pipeline consists of four distinct stages. Raw texts from PDFs, webpages, or database rows are first **chunked and embedded** using a vector-based encoder, then stored in a vector database like FAISS or Chroma. At query time, the user prompt is embedded and a nearest-neighbor search returns the most relevant chunks. These retrieved passages are concatenated with the original prompt and fed to the **LLM generator**, which produces a grounded answer.

The repository specifically recommends **LlamaIndex** (described as the "Data Framework for ingesting, indexing, and querying private data with LLMs") for implementing this workflow. The architecture maps cleanly to components curated in the repository:

- **Ingestion & Embedding**: LlamaIndex's `SimpleDirectoryReader` and `LLMPredictor` handle multiple document types and use the same LLM for embeddings
- **Vector Store**: FAISS or Chroma provide fast approximate nearest-neighbor search
- **Retriever**: The `VectorStoreRetriever` provides relevance scoring and configurable top-k retrieval
- **Orchestration**: LangGraph or CrewAI enable complex multi-step workflows

## Setting Up Your Environment

Install the core dependencies referenced in the repository's [`pyproject.toml`](https://github.com/owainlewis/awesome-artificial-intelligence/blob/main/pyproject.toml):

```bash
pip install llama-index faiss-cpu openai

```

The [`pyproject.toml`](https://github.com/owainlewis/awesome-artificial-intelligence/blob/main/pyproject.toml) file in the `owainlewis/awesome-artificial-intelligence` repository ensures compatible versions of LlamaIndex, LangGraph, and supporting libraries.

## Implementing RAG with LlamaIndex

### Step 1: Document Ingestion and Indexing

Use `SimpleDirectoryReader` to load documents and `GPTVectorStoreIndex` to build the vector index with FAISS as the underlying store:

```python
from llama_index import SimpleDirectoryReader, GPTVectorStoreIndex
from llama_index.llms import OpenAI

# Load all .txt/.pdf files in ./data

documents = SimpleDirectoryReader("./data").load_data()

# Set up the LLM that will also produce embeddings

llm = OpenAI(model="gpt-4o", temperature=0.0)

# Build a vector index (FAISS under the hood)

index = GPTVectorStoreIndex.from_documents(documents, llm=llm)

# Persist the index for later reuse

index.storage_context.persist(persist_dir="./persist")

```

### Step 2: Querying with Retrieval-Augmented Generation

Load the persisted index and create a query engine that automatically retrieves relevant context and generates answers:

```python
from llama_index import load_index_from_storage

# Load the persisted index

storage_context = index.storage_context
index = load_index_from_storage(storage_context, persist_dir="./persist")

# Create a query engine that automatically retrieves & generates

query_engine = index.as_query_engine(
    similarity_top_k=5,  # number of retrieved chunks

    llm=llm,            # same LLM used for embeddings

)

# Ask a question

response = query_engine.query(
    "What are the most important considerations when deploying LLMs in production?"
)

print(response)

```

Under the hood, this embeds the user query, fetches the top-k most similar document chunks from FAISS, concatenates the query and retrieved passages, and sends them to the LLM for a grounded answer.

### Step 3: Adding Source Citations

Enable source tracking to verify which documents contributed to the answer:

```python

# Enable source tracking

query_engine = index.as_query_engine(
    similarity_top_k=5,
    llm=llm,
    response_mode="compact",  # returns answer + source nodes

)

response = query_engine.query("Explain Retrieval-Augmented Generation.")
print(response.response)  # Answer text

print("\nSources:")
for node in response.source_nodes:
    print(f"- {node.metadata['source']} (score {node.score:.2f})")

```

## Advanced Orchestration with LangGraph

For complex multi-step workflows, the repository recommends **LangGraph** (listed under Frameworks in [`README.md`](https://github.com/owainlewis/awesome-artificial-intelligence/blob/main/README.md)). This stateful graph framework separates retrieval and generation into explicit nodes, making debugging and monitoring easier:

```python
from langgraph.graph import StateGraph, END

def retrieve(state):
    query = state["question"]
    docs = index.as_retriever(similarity_top_k=4).retrieve(query)
    return {"retrieved": docs, "question": query}

def answer(state):
    prompt = f"""Answer the following question using ONLY the supplied context.

Question: {state["question"]}

Context:
{'\n---\n'.join([d.text for d in state["retrieved"]])}
"""
    answer = llm.complete(prompt)
    return {"answer": answer, "question": state["question"]}

workflow = StateGraph()
workflow.add_node("retrieve", retrieve)
workflow.add_node("answer", answer)
workflow.add_edge("retrieve", "answer")
workflow.add_edge("answer", END)

app = workflow.compile()
result = app.invoke({"question": "How does vector similarity work?"})
print(result["answer"])

```

## Repository Resources and Configuration

The `owainlewis/awesome-artificial-intelligence` repository serves as a curated knowledge base rather than a code implementation. Key files include:

- **[`README.md`](https://github.com/owainlewis/awesome-artificial-intelligence/blob/main/README.md)**: Central curated list of RAG-relevant resources (lines 71-80 highlight LlamaIndex and Haystack)
- **[`pyproject.toml`](https://github.com/owainlewis/awesome-artificial-intelligence/blob/main/pyproject.toml)**: Dependency specifications ensuring compatible versions of LlamaIndex, LangGraph, and vector stores

The repository points to the most actively maintained RAG libraries—**LlamaIndex**, **Haystack**, **LangGraph**, and **CrewAI**—and provides surrounding context (books, papers, courses) needed to design robust solutions.

## Summary

- **Retrieval-augmented generation** combines vector search with LLM generation to ground responses in external data
- The [`README.md`](https://github.com/owainlewis/awesome-artificial-intelligence/blob/main/README.md) in `owainlewis/awesome-artificial-intelligence` (lines 71-80) curates **LlamaIndex** and **Haystack** as primary frameworks for RAG implementation
- Use `SimpleDirectoryReader` and `GPTVectorStoreIndex` for document ingestion and FAISS-based indexing
- The `as_query_engine()` method handles retrieval and generation automatically, with configurable `similarity_top_k` parameters
- **LangGraph** enables explicit stateful workflows for complex multi-step RAG pipelines
- Source citations are accessible via `response.source_nodes` when using the appropriate response mode

## Frequently Asked Questions

### What is the difference between RAG and fine-tuning?

Retrieval-augmented generation keeps the underlying LLM weights frozen while injecting relevant context at inference time, whereas fine-tuning updates the model parameters on specific training data. RAG is preferable for dynamic knowledge bases that change frequently, as you only need to update the vector index rather than retrain the model.

### Which vector database should I use for RAG?

According to the curated resources in [`README.md`](https://github.com/owainlewis/awesome-artificial-intelligence/blob/main/README.md), **FAISS** and **Chroma** are recommended for most Python-based RAG implementations. FAISS provides fast approximate nearest-neighbor search and integrates seamlessly with LlamaIndex's `VectorStoreRetriever`. Chroma offers a simpler API for smaller datasets and prototyping.

### How do I handle citations in a RAG pipeline?

Use LlamaIndex's `response_mode="compact"` or similar settings when creating the query engine with `as_query_engine()`. The `response.source_nodes` attribute contains the retrieved chunks with metadata including source file paths and relevance scores, allowing you to attribute specific facts to their original documents.

### Can I use open-source models instead of OpenAI for RAG?

Yes. While the examples use OpenAI's `gpt-4o`, LlamaIndex can wrap any LLM implementation. The `awesome-artificial-intelligence` repository lists alternatives including local models via **Ollama** or **Hugging Face**, which can be substituted in the `OpenAI` class position or using LlamaIndex's `LLMPredictor` abstraction with compatible embeddings.