GenAIComps
GenAI components at micro-service level; GenAI service composer to create mega-service
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GenAIComps is an initiative aimed at building enterprise-grade Generative AI applications using a microservice architecture. It simplifies the scaling and deployment process for production, abstracting away infrastructure complexities. GenAIComps provides a suite of containerized microservices that can be assembled into a mega-service tailored for real-world Enterprise AI applications. The modular approach of microservices allows for independent development, deployment, and scaling of individual components, promoting modularity, flexibility, and scalability. The mega-service orchestrates multiple microservices to deliver comprehensive solutions, encapsulating complex business logic and workflow orchestration. The gateway serves as the interface for users to access the mega-service, providing customized access based on user requirements.
README:
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) and other Enterprise AI 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.
A Microservices can be created by using the decorator register_microservice. Taking the embedding microservice as an example:
from comps import register_microservice, EmbedDoc, 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=EmbedDoc,
)
def embedding(input: TextDoc) -> EmbedDoc:
embed_vector = embeddings.embed_query(input.text)
res = EmbedDoc(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,
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)self.gateway = ChatQnAGateway(megaservice=self.megaservice, host="0.0.0.0", port=self.port)
## Check Mega/Micro Service health status and version number
Use below command to check Mega/Micro Service status.
```bash
curl http://${your_ip}:${service_port}/v1/health_check\
-X GET \
-H 'Content-Type: application/json'
Users should get output like below example if Mega/Micro Service works correctly.
{"Service Title":"ChatQnAGateway/MicroService","Version":"1.0","Service Description":"OPEA Microservice Infrastructure"}Welcome to the OPEA open-source community! We are thrilled to have you here and excited about the potential contributions you can bring to the OPEA platform. Whether you are fixing bugs, adding new GenAI components, improving documentation, or sharing your unique use cases, your contributions are invaluable.
Together, we can make OPEA the go-to platform for enterprise AI solutions. Let's work together to push the boundaries of what's possible and create a future where AI is accessible, efficient, and impactful for everyone.
Please check the Contributing guidelines for a detailed guide on how to contribute a GenAI example and all the ways you can contribute!
Thank you for being a part of this journey. We can't wait to see what we can achieve together!
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