How to Intermediate · 4 min read

How to use Llama embeddings for RAG

Q: How to use Llama embeddings for RAG

Use the OpenAI SDK with a Llama embeddings model like meta-llama/Llama-3.3-70b-versatile via a provider such as Groq or Together AI. Generate embeddings for your documents, store them in a vector database, then retrieve relevant vectors to augment your chat.completions.create calls for RAG workflows.

Quick answer

Use the OpenAI SDK with a Llama embeddings model like meta-llama/Llama-3.3-70b-versatile via a provider such as Groq or Together AI. Generate embeddings for your documents, store them in a vector database, then retrieve relevant vectors to augment your chat.completions.create calls for RAG workflows.

PREREQUISITES

Python 3.8+
OpenAI API key from a Llama provider (e.g., Groq, Together AI)
pip install openai>=1.0
Access to a vector database like Pinecone or FAISS

Setup

Install the openai Python package and set your API key as an environment variable. Choose a Llama provider like Groq or Together AI that supports Llama embeddings via OpenAI-compatible API endpoints.

bash

pip install openai

Step by step

This example shows how to generate embeddings for documents using Llama embeddings, store them in a simple in-memory list, and perform a similarity search to retrieve relevant documents for RAG.

python

import os
from openai import OpenAI
import numpy as np

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

# Example documents
documents = [
    "Llama models are powerful large language models.",
    "Retrieval-Augmented Generation improves answer accuracy.",
    "Embeddings convert text into vectors for similarity search."
]

# Generate embeddings for documents
response = client.embeddings.create(
    model="meta-llama/Llama-3.3-70b-versatile",
    input=documents
)

# Extract embeddings vectors
embeddings = [data.embedding for data in response.data]

# Simple in-memory vector store (list of tuples: (embedding, document))
vector_store = list(zip(embeddings, documents))

# Function to compute cosine similarity

def cosine_similarity(a, b):
    a = np.array(a)
    b = np.array(b)
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

# Query text
query = "How do Llama embeddings help in RAG?"

# Generate embedding for query
query_response = client.embeddings.create(
    model="meta-llama/Llama-3.3-70b-versatile",
    input=query
)
query_embedding = query_response.data[0].embedding

# Retrieve top relevant document by similarity
scores = [(cosine_similarity(query_embedding, emb), doc) for emb, doc in vector_store]
scores.sort(key=lambda x: x[0], reverse=True)
top_doc = scores[0][1]

# Use retrieved document as context in chat completion
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": f"Context: {top_doc}\n\nQuestion: {query}"}
]

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=messages
)

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

output

Answer: Llama embeddings convert text into vector representations that enable efficient similarity search, which helps Retrieval-Augmented Generation (RAG) by retrieving relevant documents to improve answer accuracy.

Common variations

Use a vector database like Pinecone or FAISS for scalable storage and retrieval instead of in-memory lists.
Switch providers by changing base_url and API key (e.g., Together AI at https://api.together.xyz/v1).
Use async calls with asyncio and await if supported by your environment.
Try smaller Llama embedding models if latency or cost is a concern.

Troubleshooting

If embeddings generation fails, verify your API key and base_url match your Llama provider.
Low similarity scores? Normalize vectors or check input text encoding.
For slow retrieval, use a dedicated vector database instead of in-memory storage.
Ensure environment variables are set correctly to avoid authentication errors.

✅

Key Takeaways

Use Llama embeddings from providers like Groq or Together AI via OpenAI-compatible SDKs for RAG.
Generate embeddings for documents and queries, then retrieve relevant documents by vector similarity.
Integrate retrieved documents as context in chat completions to improve answer relevance.
Use vector databases for scalable and efficient similarity search in production.
Always verify API keys and endpoints to avoid authentication and embedding errors.

Verified 2026-04 · meta-llama/Llama-3.3-70b-versatile, gpt-4o-mini

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