Context Isn’t Everything: Build Efficient LLM Apps with LlamaIndex + Featherless
Build Production RAG Applications with 4,000+ Open Source Models and Zero Infrastructure Overhead
We’re excited to announce that LlamaIndex has official support for Featherless, bringing together two powerful tools for building production RAG applications. While everyone’s chasing longer context windows (100K, 1M tokens), we’ve noticed most production apps need something different: they need efficient retrieval that finds the right information, not all information.
That’s why this integration matters:
LlamaIndex provides the RAG infrastructure: data loaders, chunking strategies, and vector search
Featherless gives you access to 4,000+ open source models through a simple API
The combination can build you a RAG pipeline that can switch between models instantly, optimize for cost, and scale without infrastructure headaches.
Let’s have a deeper look at what you can build with this new integration.
Why Retrieval is Perhaps a Better solution for your problem
Stuffing your entire knowledge base into a single prompt might work for a simple demo, but at scale it leads to:
Slower response times: Processing 100k tokens takes time, even on fast hardware
More hallucinations: Models struggle with needle-in-haystack problems in massive contexts
Token overflow: Eventuelly you might hit limits, forcing crude truncation
What you actually want is precision, just the right information, fed to the model at the right time. That’s where RAG (Retrieval-Augmented Generation) shines, and LlamaIndex handles it beautifully.
What Featherless brings to the Stack
Featherless simplifies the process of getting access to open source models. Instead of provisioning GPUs, managing infrastructure, dealing with model deployment, and worrying about usage costs, you get instant access to over 4,300 open source models including DeepSeek, Llama, Qwen, Mistral and many more. Everything runs through our API, with a simple monthly subscription, you have unlimited access and tokens to our whole model catalog and can switch between them instantly, perfect for A/B testing different approaches without any infrastructure overhead.
Quickstart: Build a Local RAG application
Let’s walk through building a Q&A assistant that can answer questions about your local documents.
Install dependencies
pip install llama-index llama-index-llms-featherlessai llama-index-embeddings-huggingfaceSet up your environment
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.llms.featherlessai import FeatherlessLLM
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
import os
# Set your Featherless API key
os.environ["FEATHERLESS_API_KEY"] = "your-api-key"
# Configure local embeddings
embed_model = HuggingFaceEmbedding(
model_name="BAAI/bge-small-en-v1.5" # Efficient, high-quality embeddings
)
# Alternative: Use Ollama for local embeddings
# from llama_index.embeddings.ollama import OllamaEmbedding
# embed_model = OllamaEmbedding(model_name="nomic-embed-text")Load and Index Your Documents
# Load all files from ./docs directory
documents = SimpleDirectoryReader("docs").load_data()
# Configure Featherless as your LLM
llm = FeatherlessLLM(
model="Qwen/Qwen3-32B", # Or any model from featherless.ai
temperature=0.1, # Lower for more consistent retrieval
)
# Build your vector index with free embeddings
index = VectorStoreIndex.from_documents(
documents,
embed_model=embed_model,
chunk_size=512, # Optimal for precise retrieval
)Query Your Knowledge Base
# Create a query engine
query_engine = index.as_query_engine(
llm=llm,
similarity_top_k=3, # Retrieve top 3 most relevant chunks
)
# Ask questions
response = query_engine.query("What's our onboarding process?")
print(response)You just built a RAG pipeline system in under 30 lines of code, with zero of the infrastructure overhead
Advanced Features: Streaming and Chat
Our Featherless LlamaIndex integration supports both streaming responses and multi-turn conversations:
Streaming Responses
# Stream for real-time output
response = llm.stream_complete("Summarize the key points of machine learning")
for chunk in response:
print(chunk.delta, end="")Multi-Turn Chat
from llama_index.core.llms import ChatMessage
messages = [
ChatMessage(role="system", content="You are a helpful technical assistant"),
ChatMessage(role="user", content="What is RAG?"),
]
# Stream chat responses
stream = llm.stream_chat(messages)
for chunk in stream:
print(chunk.delta, end="")Model Switching: A/B Test Without Rewriting Code
One of Featherless’s powers is instantly model switching. Test different models for your use case:
models_to_test = [
"mistralai/Mistral-Small-24B-Instruct-2501",
"Qwen/Qwen3-8B"
"meta-llama/Meta-Llama-3.1-8B-Instruct"
]
query = "Explain our refund policy"
for model_name in models_to_test:
llm.model = model_name
response = query_engine.query(query)
print(f"\n{model_name}:\n{response}")Real-World Example: Customer Support Bot
Here's a complete example of a customer support bot that combines multiple best practices:
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.llms.featherlessai import FeatherlessLLM
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.llms import ChatMessage
import os
# Initialize Featherless
os.environ["FEATHERLESS_API_KEY"] = "your-api-key"
# Set up embeddings
embed_model = HuggingFaceEmbedding(
model_name="BAAI/bge-small-en-v1.5"
)
# Load different document types
faq_docs = SimpleDirectoryReader("./data/faqs").load_data()
policy_docs = SimpleDirectoryReader("./data/policies").load_data()
product_docs = SimpleDirectoryReader("./data/products").load_data()
# Tag documents with metadata
for doc in faq_docs:
doc.metadata["category"] = "faq"
for doc in policy_docs:
doc.metadata["category"] = "policy"
for doc in product_docs:
doc.metadata["category"] = "product"
# Combine all documents
all_docs = faq_docs + policy_docs + product_docs
# Create index with custom settings
index = VectorStoreIndex.from_documents(
all_docs,
embed_model=embed_model,
transformations=[
SentenceSplitter(chunk_size=512, chunk_overlap=50)
]
)
# Function to route queries to appropriate model
def get_llm_for_query(query: str) -> FeatherlessLLM:
query_lower = query.lower()
if any(word in query_lower for word in ["refund", "policy", "terms"]):
# Use precise model for policy questions
return FeatherlessLLM(model="Qwen/Qwen3-32B", temperature=0.1)
elif any(word in query_lower for word in ["help", "how", "tutorial"]):
# Use helpful model for guidance
return FeatherlessLLM(model="deepseek-ai/DeepSeek-R1-0528-Qwen3-8B", temperature=0.3)
else:
# Default conversational model
return FeatherlessLLM(model="mistralai/Mistral-Small-3.1-24B-Instruct-2503", temperature=0.2)
# Create a support bot function
def support_bot(user_query: str, chat_history: list = None):
# Select appropriate model
llm = get_llm_for_query(user_query)
# Create query engine with filters
query_engine = index.as_query_engine(
llm=llm,
similarity_top_k=3,
response_mode="compact", # Synthesize concise answers
)
# Add chat context if available
if chat_history:
context = "\n".join([f"{msg['role']}: {msg['content']}" for msg in chat_history[-3:]])
full_query = f"Previous conversation:\n{context}\n\nCurrent question: {user_query}"
else:
full_query = user_query
# Get response
response = query_engine.query(full_query)
return response
# Example usage
print(support_bot("What's your refund policy?"))
print(support_bot("How do I reset my password?"))Performance and efficiency strategies
As your RAG application scales, performance optimization becomes crucial. Start with embedding caching to avoid recomputing embeddings for documents you’ve already processed. LlamaIndex makes this straightforward with its storage context:
# Cache embeddings to avoid recomputation
from llama_index.core import StorageContext
from llama_index.core.storage import SimpleDocumentStore
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
storage_context = StorageContext.from_defaults(
persist_dir="./storage"
)
index = VectorStoreIndex.from_documents(
documents,
embed_model=embed_model,
storage_context=storage_context
)With Featherless’s monthly subscription, you have unlimited access to all models, which fundamentally changes how you approach optimization. Instead of minimizing token usage, you can experiment freely with different models to find the perfect fit for each use case. Don’t hesitate to use larger models for complex tasks where quality matters most.
Focus your optimization efforts on reducing latency through query caching for common questions and implementing parallel processing for better throughput. Since you’re not counting tokens, you can run extensive A/B tests across multiple models simultaneously, gathering real performance data to make informed decisions about which models work best for different query types. This freedom to experiment without constraints means you can optimize for what really matters: response quality and user experience.
What’s next?
You now have the foundation for building powerful RAG applications with LlamaIndex and the Featherless integration. Start by exploring the vast model selection at featherless.ai, you might discover specialized models perfect for your use case that you wouldn’t have considered before.
As your application grows, consider adding persistence with vector databases to handle larger document collections. Implement evaluation metrics to measure your retrieval quality and iterate on your chunking strategies. The real power comes when you start building agents that combine RAG with tool use, enabling complex workflows that go beyond simple Q&A.
Join our community on Discord to share your builds and learn from others who are pushing the boundaries of what’s possible with RAG.