Build Enterprise-grade Generative AI Applications with Microservice Architecture
This initiative empowers the development of high-quality Generative AI applications for enterprises via microservices, simplifying the scaling and deployment process for production. It abstracts away infrastructure complexities, facilitating the seamless development and deployment of Enterprise AI services.
GenAIComps provides a suite of microservices, leveraging a service composer to assemble a mega-service tailored for real-world Enterprise AI applications. All the microservices are containerized, allowing cloud native deployment. Checkout how the microservices are used in GenAIExamples.
- Install from Pypi
pip install opea-comps- Build from Source
git clone https://github.com/opea-project/GenAIComps
cd GenAIComps
pip install -e .Microservices are akin to building blocks, offering the fundamental services for constructing RAG (Retrieval-Augmented Generation) applications.
Each Microservice is designed to perform a specific function or task within the application architecture. By breaking down the system into smaller, self-contained services, Microservices promote modularity, flexibility, and scalability.
This modular approach allows developers to independently develop, deploy, and scale individual components of the application, making it easier to maintain and evolve over time. Additionally, Microservices facilitate fault isolation, as issues in one service are less likely to impact the entire system.
The initially supported Microservices are described in the below table. More Microservices are on the way.
| MicroService | Framework | Model | Serving | HW | Description |
| Embedding | LangChain | BAAI/bge-large-en-v1.5 | TEI-Gaudi | Gaudi2 | Embedding on Gaudi2 |
| Embedding | LangChain | BAAI/bge-base-en-v1.5 | TEI | Xeon | Embedding on Xeon CPU |
| Retriever | LangChain | BAAI/bge-base-en-v1.5 | TEI | Xeon | Retriever on Xeon CPU |
| Reranking | LangChain | BAAI/bge-reranker-large | TEI-Gaudi | Gaudi2 | Reranking on Gaudi2 |
| Reranking | LangChain | BBAAI/bge-reranker-base | TEI | Xeon | Reranking on Xeon CPU |
| LLM | LangChain | Intel/neural-chat-7b-v3-3 | TGI Gaudi | Gaudi2 | LLM on Gaudi2 |
| LLM | LangChain | Intel/neural-chat-7b-v3-3 | TGI | Xeon | LLM on Xeon CPU |
A Microservices can be created by using the decorator register_microservice. Taking the embedding microservice as an example:
from langchain_community.embeddings import HuggingFaceHubEmbeddings
from langsmith import traceable
from comps import register_microservice, EmbedDoc768, ServiceType, TextDoc
@register_microservice(
name="opea_service@embedding_tgi_gaudi",
service_type=ServiceType.EMBEDDING,
endpoint="/v1/embeddings",
host="0.0.0.0",
port=6000,
input_datatype=TextDoc,
output_datatype=EmbedDoc768,
)
@traceable(run_type="embedding")
def embedding(input: TextDoc) -> EmbedDoc768:
embed_vector = embeddings.embed_query(input.text)
embed_vector = embed_vector[:768] # Keep only the first 768 elements
res = EmbedDoc768(text=input.text, embedding=embed_vector)
return resA Megaservice is a higher-level architectural construct composed of one or more Microservices, providing the capability to assemble end-to-end applications. Unlike individual Microservices, which focus on specific tasks or functions, a Megaservice orchestrates multiple Microservices to deliver a comprehensive solution.
Megaservices encapsulate complex business logic and workflow orchestration, coordinating the interactions between various Microservices to fulfill specific application requirements. This approach enables the creation of modular yet integrated applications, where each Microservice contributes to the overall functionality of the Megaservice.
Here is a simple example of building Megaservice:
from comps import MicroService, ServiceOrchestrator
EMBEDDING_SERVICE_HOST_IP = os.getenv("EMBEDDING_SERVICE_HOST_IP", "0.0.0.0")
EMBEDDING_SERVICE_PORT = os.getenv("EMBEDDING_SERVICE_PORT", 6000)
LLM_SERVICE_HOST_IP = os.getenv("LLM_SERVICE_HOST_IP", "0.0.0.0")
LLM_SERVICE_PORT = os.getenv("LLM_SERVICE_PORT", 9000)
class ExampleService:
def __init__(self, host="0.0.0.0", port=8000):
self.host = host
self.port = port
self.megaservice = ServiceOrchestrator()
def add_remote_service(self):
embedding = MicroService(
name="embedding",
host=EMBEDDING_SERVICE_HOST_IP,
port=EMBEDDING_SERVICE_PORT,
endpoint="/v1/embeddings",
use_remote_service=True,S
service_type=ServiceType.EMBEDDING,
)
llm = MicroService(
name="llm",
host=LLM_SERVICE_HOST_IP,
port=LLM_SERVICE_PORT,
endpoint="/v1/chat/completions",
use_remote_service=True,
service_type=ServiceType.LLM,
)
self.megaservice.add(embedding).add(llm)
self.megaservice.flow_to(embedding, llm)The Gateway serves as the interface for users to access the Megaservice, providing customized access based on user requirements. It acts as the entry point for incoming requests, routing them to the appropriate Microservices within the Megaservice architecture.
Gateways support API definition, API versioning, rate limiting, and request transformation, allowing for fine-grained control over how users interact with the underlying Microservices. By abstracting the complexity of the underlying infrastructure, Gateways provide a seamless and user-friendly experience for interacting with the Megaservice.
For example, the Gateway for ChatQnA can be built like this:
from comps import ChatQnAGateway
self.gateway = ChatQnAGateway(megaservice=self.megaservice, host="0.0.0.0", port=self.port)