Code intermediate · 3 min read

Llama RAG pipeline Python example

Direct answer

Use the OpenAI-compatible SDK with a vector store like FAISS and a Llama model from Groq or Together AI to build a retrieval-augmented generation (RAG) pipeline in Python with client.chat.completions.create calls.

Setup

Install

bash

pip install openai faiss-cpu langchain langchain_community

Env vars

OPENAI_API_KEYGROQ_API_KEY

Imports

python

import os
from openai import OpenAI
from langchain_openai import ChatOpenAI
from langchain_community.vectorstores import FAISS
from langchain_community.document_loaders import TextLoader
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.chains import RetrievalQA
from langchain_core.schema import Document

Examples

inQuery: 'What is retrieval-augmented generation?'

outAnswer: 'Retrieval-augmented generation (RAG) combines vector search with LLMs to provide accurate, context-aware answers by retrieving relevant documents and generating responses.'

inQuery: 'Explain Llama model usage in RAG pipelines.'

outAnswer: 'Llama models can be used as the generative LLM in RAG pipelines by integrating with vector stores like FAISS to retrieve context and generate precise answers.'

inQuery: 'How to handle empty search results in RAG?'

outAnswer: 'Implement fallback logic to handle empty retrievals, such as default responses or querying the LLM without context.'

Integration steps

Initialize the OpenAI-compatible client with the Llama model and API key from os.environ
Load and embed documents into a FAISS vector store for retrieval
Create a retrieval-based QA chain combining the vector store retriever and Llama chat model
Invoke the chain with a user query to retrieve relevant documents and generate an answer
Print the generated answer to the console

Full code

python

import os
from openai import OpenAI
from langchain_openai import ChatOpenAI
from langchain_community.vectorstores import FAISS
from langchain_community.document_loaders import TextLoader
from langchain_core.chains import RetrievalQA
from langchain_core.schema import Document

# Initialize OpenAI-compatible client for Groq Llama model
client = OpenAI(api_key=os.environ["GROQ_API_KEY"], base_url="https://api.groq.com/openai/v1")

# Load documents from local text files
loader = TextLoader("./docs")  # folder with text files
raw_docs = loader.load()

# Create embeddings for documents using OpenAI embeddings
embedding_client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
response = embedding_client.embeddings.create(model="text-embedding-3-small", input=[doc.page_content for doc in raw_docs])
embeddings = [item.embedding for item in response.data]

# Build FAISS vector store
vector_store = FAISS.from_documents(raw_docs, embeddings)

# Setup retriever
retriever = vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 3})

# Setup Llama chat model via OpenAI-compatible client
llama_chat = ChatOpenAI(client=client, model="llama-3.3-70b-versatile", temperature=0.0)

# Create RetrievalQA chain
qa_chain = RetrievalQA(llm=llama_chat, retriever=retriever)

# Query example
query = "What is retrieval-augmented generation?"
answer = qa_chain.invoke({"input": query})

print(f"Query: {query}")
print(f"Answer: {answer}")

API trace

Request

json

{"model": "llama-3.3-70b-versatile", "messages": [{"role": "user", "content": "<retrieved context>\nQuestion: What is retrieval-augmented generation?"}]}

Response

json

{"choices": [{"message": {"content": "Retrieval-augmented generation (RAG) combines vector search with large language models to provide accurate, context-aware answers by retrieving relevant documents and generating responses."}}]}

Extractresponse.choices[0].message.content

Variants

Streaming response version ›

Use streaming for better user experience with long answers or interactive applications.

python

import os
from openai import OpenAI
client = OpenAI(api_key=os.environ["GROQ_API_KEY"], base_url="https://api.groq.com/openai/v1")

response = client.chat.completions.create(
    model="llama-3.3-70b-versatile",
    messages=[{"role": "user", "content": "Explain retrieval-augmented generation."}],
    stream=True
)

for chunk in response:
    print(chunk.choices[0].message.content, end='')
print()

Async version with LangChain ›

Use async when integrating into asynchronous web servers or concurrent workflows.

python

import os
import asyncio
from openai import OpenAI
from langchain_openai import ChatOpenAI
from langchain_community.vectorstores import FAISS
from langchain_community.document_loaders import TextLoader
from langchain_core.chains import RetrievalQA

async def main():
    client = OpenAI(api_key=os.environ["GROQ_API_KEY"], base_url="https://api.groq.com/openai/v1")
    loader = TextLoader("./docs")
    raw_docs = loader.load()
    embedding_client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
    response = embedding_client.embeddings.create(model="text-embedding-3-small", input=[doc.page_content for doc in raw_docs])
    embeddings = [item.embedding for item in response.data]
    vector_store = FAISS.from_documents(raw_docs, embeddings)
    retriever = vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 3})
    llama_chat = ChatOpenAI(client=client, model="llama-3.3-70b-versatile", temperature=0.0)
    qa_chain = RetrievalQA(llm=llama_chat, retriever=retriever)
    answer = await qa_chain.invoke_async({"input": "What is retrieval-augmented generation?"})
    print(f"Answer: {answer}")

asyncio.run(main())

Alternative model: Together AI Llama ›

Use Together AI Llama for a strong instruct-tuned Llama model with good cost-performance balance.

python

import os
from openai import OpenAI
client = OpenAI(api_key=os.environ["TOGETHER_API_KEY"], base_url="https://api.together.xyz/v1")
response = client.chat.completions.create(
    model="meta-llama/Llama-3.3-70B-Instruct-Turbo",
    messages=[{"role": "user", "content": "Explain retrieval-augmented generation."}]
)
print(response.choices[0].message.content)

Performance

Latency~2-5 seconds per query for llama-3.3-70b with retrieval

Cost~$0.03 per 1000 tokens for llama-3.3-70b via Groq, plus embedding costs

Rate limitsTypically 60 RPM and 60,000 TPM on Groq API; check provider limits

Limit retrieved documents to top 3-5 to reduce prompt size
Use concise prompts and system instructions to save tokens
Cache embeddings and reuse vector store to avoid recomputing

Approach	Latency	Cost/call	Best for
Standard RAG with llama-3.3-70b	~3s	~$0.03	High-quality, accurate answers
Streaming RAG response	~3s + stream	~$0.03	Interactive apps with long answers
Async RAG pipeline	~3s concurrent	~$0.03	Web servers and concurrent calls
Together AI Llama model	~2.5s	~$0.025	Cost-effective instruct-tuned Llama

✓

Quick tip

Use a vector store like FAISS with OpenAI embeddings to efficiently retrieve relevant documents before querying the Llama model for accurate RAG results.

⚠

Common mistake

Beginners often forget to embed documents before indexing in FAISS, causing retrieval to fail or return irrelevant results.

Verified 2026-04 · llama-3.3-70b-versatile, meta-llama/Llama-3.3-70B-Instruct-Turbo, text-embedding-3-small

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