Maximizing AI Model Deployment Speed with LitServe and LlamaIndex
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Introduction to Efficient AI Model Serving
In today's landscape of artificial intelligence, the swift serving of AI models is vital for practical applications. LitServe stands out as a high-efficiency serving engine that enables developers to effortlessly deploy and manage an extensive array of AI models. When paired with LlamaIndex, a robust data framework, LitServe transforms into an even more adaptable tool for creating intelligent applications.
Definitions and Key Features
- LitServe: A rapid serving engine built on FastAPI, designed to simplify the deployment and management of AI models.
- LlamaIndex: A data framework that connects large language models (LLMs) to external data sources, facilitating more context-aware and informative outputs.
Advantages of Using LitServe and LlamaIndex
- Speed: The optimized structure of LitServe, along with AI-specific multi-worker management, achieves serving speeds that are at least twice as fast as standard FastAPI.
- Flexibility: Compatible with various AI models, including LLMs and classical machine learning models.
- User-Friendly: A straightforward API and intuitive interface make defining and deploying AI services simple.
- Scalability: Features like batching, streaming, and GPU autoscaling ensure efficient management of substantial workloads.
- Customization: Users can integrate their own models and construct compound systems that utilize multiple models.
- Hosting Options: Choose to self-host on your hardware or deploy on Lightning AI’s fully managed cloud platform.
Code Example for Implementation
To showcase the capabilities of LitServe and LlamaIndex, let’s consider a code example that demonstrates the creation of a basic question-answering service utilizing an LLM alongside a custom document index crafted with LlamaIndex.
import os, logging, qdrant_client
from llama_index.llms.ollama import Ollama
from llama_index.core import StorageContext, Settings, VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.qdrant import QdrantVectorStore
from llama_index.embeddings.fastembed import FastEmbedEmbedding
import litserve as ls
class DocumentChatAPI(ls.LitAPI):
def setup(self, device):
# LLM x RAG workflow
Settings.llm = Ollama(model="llama3.1:latest", request_timeout=120.0)
Settings.embed_model = FastEmbedEmbedding(model_name="BAAI/bge-large-en-v1.5")
client = qdrant_client.QdrantClient(host="localhost", port=6333)
vector_store = QdrantVectorStore(client=client, collection_name="doc_search_collection")
storage_context = StorageContext.from_defaults(vector_store=vector_store)
documents = SimpleDirectoryReader("./docs").load_data()
index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)
self.query_engine = index.as_query_engine()
def decode_request(self, request):
return request["query"]
def predict(self, query):
return self.query_engine.query(query)
def encode_response(self, output):
return {"output": output}
if __name__ == "__main__":
# Deploy a scalable server
api = DocumentChatAPI()
server = ls.LitServer(api, accelerator="gpu", workers_per_device=4)
server.run(port=8000)
Conclusion: The Future of AI Model Serving
The combination of LitServe and LlamaIndex presents a formidable solution for serving AI models, especially in contexts that necessitate interaction with external data sources. Its remarkable speed, adaptability, and user-friendly nature make it an ideal choice for developers aiming to scale intelligent applications.
Resources
- LitServe
- Background-removal
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