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How to build RAG with Llama

Q: How to build RAG with Llama

Build a Retrieval-Augmented Generation (RAG) system with Llama by combining a vector database for document retrieval and an OpenAI-compatible client to query llama-3.3-70b-versatile. Use embeddings to index documents, retrieve relevant context, then prompt the Llama model with retrieved text for accurate, context-aware generation.

Quick answer

Build a Retrieval-Augmented Generation (RAG) system with Llama by combining a vector database for document retrieval and an OpenAI-compatible client to query llama-3.3-70b-versatile. Use embeddings to index documents, retrieve relevant context, then prompt the Llama model with retrieved text for accurate, context-aware generation.

PREREQUISITES

Python 3.8+
OpenAI API key (or Groq API key for Llama)
pip install openai faiss-cpu numpy

Setup

Install required packages and set your API key in environment variables. Use openai SDK with base_url pointing to a provider hosting llama-3.3-70b-versatile (e.g., Groq or Together AI). Install faiss-cpu for vector search and numpy for numerical operations.

bash

pip install openai faiss-cpu numpy

Step by step

This example shows how to embed documents, build a FAISS index, retrieve relevant documents for a query, and generate an answer using the llama-3.3-70b-versatile model via the OpenAI-compatible SDK.

python

import os
import numpy as np
import faiss
from openai import OpenAI

# Initialize OpenAI client with Llama provider base_url (Groq example)
client = OpenAI(api_key=os.environ["GROQ_API_KEY"], base_url="https://api.groq.com/openai/v1")

# Sample documents to index
documents = [
    "Llama is a family of large language models developed by Meta.",
    "Retrieval-Augmented Generation combines vector search with LLMs.",
    "FAISS is a library for efficient similarity search.",
    "OpenAI-compatible APIs allow easy integration with Llama models."
]

# Step 1: Embed documents using text-embedding-3-small
embedding_response = client.embeddings.create(
    model="text-embedding-3-small",
    input=documents
)
embeddings = np.array([data.embedding for data in embedding_response.data], dtype=np.float32)

# Step 2: Build FAISS index
embedding_dim = embeddings.shape[1]
index = faiss.IndexFlatL2(embedding_dim)
index.add(embeddings)

# Step 3: Query embedding
query = "How does RAG work with Llama?"
query_embedding_response = client.embeddings.create(
    model="text-embedding-3-small",
    input=[query]
)
query_embedding = np.array(query_embedding_response.data[0].embedding, dtype=np.float32).reshape(1, -1)

# Step 4: Search top 2 relevant docs
k = 2
distances, indices = index.search(query_embedding, k)
relevant_docs = [documents[i] for i in indices[0]]

# Step 5: Construct prompt with retrieved context
context = "\n".join(relevant_docs)
prompt = f"Use the following context to answer the question:\n{context}\nQuestion: {query}\nAnswer:"

# Step 6: Generate answer with Llama model
response = client.chat.completions.create(
    model="llama-3.3-70b-versatile",
    messages=[{"role": "user", "content": prompt}],
    max_tokens=256
)

print("Answer:", response.choices[0].message.content)

output

Answer: Retrieval-Augmented Generation (RAG) works with Llama by first retrieving relevant documents using vector search, then using the Llama model to generate answers based on that retrieved context.

Common variations

Use async calls with asyncio and await for scalable RAG systems.
Switch embedding models or vector stores like Chroma or Pinecone for production.
Use smaller Llama variants or other providers by changing base_url and model parameters.

Troubleshooting

If embeddings are empty or errors occur, verify your API key and model names.
Ensure faiss-cpu is installed correctly; on some platforms, use faiss-gpu if GPU is available.
If retrieval returns irrelevant documents, increase k or improve document quality.

✅

Key Takeaways

Use vector embeddings and FAISS to retrieve relevant documents for RAG with Llama.
Query llama-3.3-70b-versatile via OpenAI-compatible SDK with retrieved context for accurate generation.
Adjust embedding models, vector stores, and Llama variants to optimize performance and cost.
Always secure API keys via environment variables and verify model names for compatibility.
Async and streaming variants enable scalable and responsive RAG applications.

Verified 2026-04 · llama-3.3-70b-versatile, text-embedding-3-small

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