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05508206-790a-48ec-b0d9-5ea14450a823 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-GenAIExamples-001-Using_MicroService_to_implement_ChatQnA.md | unknown | 873a8b3a-1556-4151-901a-55cbe4d1f45c | 30 | opea-semantic-v1 | 54efccb0fc9a55b2 | on factors such as network latency. But for large-scale user access, scaling out microservices can enhance responsiveness, thereby significantly improving performance compared to monolithic designs.
By adopting this microservice architecture for RAG, we aim to enhance the flexibility, scalability, and maintainability o... | ai_ref_knowledge | OPEA Documentation | on factors such as network latency. But for large-scale user access, scaling out microservices can enhance responsiveness, thereby significantly improving performance compared to monolithic designs.
By adopting this microservice architecture for RAG, we aim to enhance the flexibility, scalability, and maintainability o... | on factors such as network latency. But for large-scale user access, scaling out microservices can enhance responsiveness, thereby significantly improving performance compared to monolithic designs.
By adopting this microservice architecture for RAG, we aim to enhance the flexibility, scalability, and maintainability o... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0796c579-ce01-496c-9a85-975a1fb5c4c6 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-GenAIExamples-001-Using_MicroService_to_implement_ChatQnA.md | unknown | 873a8b3a-1556-4151-901a-55cbe4d1f45c | 5 | opea-semantic-v1 | 057685efd2719a17 | 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.
### Megaservice | ai_ref_knowledge | OPEA Documentation | 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.
### Megaservice | 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.
### Megaservice | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0b7d676e-6ec3-445e-ae43-a812ed74e72b | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-GenAIExamples-001-Using_MicroService_to_implement_ChatQnA.md | unknown | 873a8b3a-1556-4151-901a-55cbe4d1f45c | 23 | opea-semantic-v1 | a926782d8edfd9dd | #### Example Code for Customizing Gateway
The Gateway class provides a customizable interface for accessing the megaservice. It handles requests and responses, allowing users to interact with the megaservice. The class defines methods for adding custom routes, stopping the service, and listing available services and pa... | ai_ref_knowledge | OPEA Documentation | #### Example Code for Customizing Gateway
The Gateway class provides a customizable interface for accessing the megaservice. It handles requests and responses, allowing users to interact with the megaservice. The class defines methods for adding custom routes, stopping the service, and listing available services and pa... | #### Example Code for Customizing Gateway
The Gateway class provides a customizable interface for accessing the megaservice. It handles requests and responses, allowing users to interact with the megaservice. The class defines methods for adding custom routes, stopping the service, and listing available services and pa... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
14deed95-af0a-4133-9066-f73ef115c20c | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-GenAIExamples-001-Using_MicroService_to_implement_ChatQnA.md | unknown | 873a8b3a-1556-4151-901a-55cbe4d1f45c | 24 | opea-semantic-v1 | 35f188adeefb0e11 | the service, and listing available services and parameters. Users can extend this class to implement specific handling for requests and responses according to their requirements.
```python
class Gateway:
def __init__(
self,
megaservice,
host="0.0.0.0",
port=8888,
endpoint=str(MegaServiceEndpoint.CHAT_QNA),
input... | ai_ref_knowledge | OPEA Documentation | the service, and listing available services and parameters. Users can extend this class to implement specific handling for requests and responses according to their requirements.
```python
class Gateway:
def __init__(
self,
megaservice,
host="0.0.0.0",
port=8888,
endpoint=str(MegaServiceEndpoint.CHAT_QNA),
input... | the service, and listing available services and parameters. Users can extend this class to implement specific handling for requests and responses according to their requirements.
```python
class Gateway:
def __init__(
self,
megaservice,
host="0.0.0.0",
port=8888,
endpoint=str(MegaServiceEndpoint.CHAT_QNA),
input... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
23a39e51-e5f9-4e16-9dfe-22e69175ae44 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-GenAIExamples-001-Using_MicroService_to_implement_ChatQnA.md | unknown | 873a8b3a-1556-4151-901a-55cbe4d1f45c | 20 | opea-semantic-v1 | ebc3ff3ba77fd692 | opea_mega_service: mega_flow: - dataprep
The following Python code demonstrates how to use the YAML configurations to initialize the microservices and megaservices, and set up the gateways for user interaction. | ai_ref_knowledge | OPEA Documentation | opea_mega_service: mega_flow: - dataprep
The following Python code demonstrates how to use the YAML configurations to initialize the microservices and megaservices, and set up the gateways for user interaction. | opea_mega_service: mega_flow: - dataprep
The following Python code demonstrates how to use the YAML configurations to initialize the microservices and megaservices, and set up the gateways for user interaction. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
24d67bb5-6b31-4483-bc25-01922fdaed5d | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-GenAIExamples-001-Using_MicroService_to_implement_ChatQnA.md | unknown | 873a8b3a-1556-4151-901a-55cbe4d1f45c | 12 | opea-semantic-v1 | d4cb60028d163d3d | #### Example Python Code for Constructing Services
Users can use `ServiceOrchestrator` class to build the microservice pipeline and add a gateway for each megaservice. | ai_ref_knowledge | OPEA Documentation | #### Example Python Code for Constructing Services
Users can use `ServiceOrchestrator` class to build the microservice pipeline and add a gateway for each megaservice. | #### Example Python Code for Constructing Services
Users can use `ServiceOrchestrator` class to build the microservice pipeline and add a gateway for each megaservice. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
2561fcee-9546-4daa-868e-f89c0fbc8e65 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-GenAIExamples-001-Using_MicroService_to_implement_ChatQnA.md | unknown | 873a8b3a-1556-4151-901a-55cbe4d1f45c | 17 | opea-semantic-v1 | 70f23d5135aba063 | #### Constructing Services with yaml
Below is an example of how to define microservices and megaservices using YAML for the ChatQnA application. This configuration outlines the endpoints for each microservice and specifies the workflow for the megaservices. | ai_ref_knowledge | OPEA Documentation | #### Constructing Services with yaml
Below is an example of how to define microservices and megaservices using YAML for the ChatQnA application. This configuration outlines the endpoints for each microservice and specifies the workflow for the megaservices. | #### Constructing Services with yaml
Below is an example of how to define microservices and megaservices using YAML for the ChatQnA application. This configuration outlines the endpoints for each microservice and specifies the workflow for the megaservices. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
2608df5c-f1f0-4e2b-89fd-59ae7b07fced | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-GenAIExamples-001-Using_MicroService_to_implement_ChatQnA.md | unknown | 873a8b3a-1556-4151-901a-55cbe4d1f45c | 4 | opea-semantic-v1 | 0d40734b1ab22722 | ### Microservice
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-conta... | ai_ref_knowledge | OPEA Documentation | ### Microservice
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-conta... | ### Microservice
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-conta... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
306ad5b3-6053-4461-8f8b-de61033f5167 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-GenAIExamples-001-Using_MicroService_to_implement_ChatQnA.md | unknown | 873a8b3a-1556-4151-901a-55cbe4d1f45c | 25 | opea-semantic-v1 | 1ba232c0250761c7 | ```python class Gateway: def __init__( self, megaservice, host="0.0.0.0", port=8888, endpoint=str(MegaServiceEndpoint.CHAT_QNA), input_datatype=ChatCompletionRequest, output_datatype=ChatCompletionResponse, ): ... self.gateway = MicroService( service_role=ServiceRoleType.MEGASERVICE, service_type=ServiceType.GATEWAY, .... | ai_ref_knowledge | OPEA Documentation | ```python class Gateway: def __init__( self, megaservice, host="0.0.0.0", port=8888, endpoint=str(MegaServiceEndpoint.CHAT_QNA), input_datatype=ChatCompletionRequest, output_datatype=ChatCompletionResponse, ): ... self.gateway = MicroService( service_role=ServiceRoleType.MEGASERVICE, service_type=ServiceType.GATEWAY, .... | ```python class Gateway: def __init__( self, megaservice, host="0.0.0.0", port=8888, endpoint=str(MegaServiceEndpoint.CHAT_QNA), input_datatype=ChatCompletionRequest, output_datatype=ChatCompletionResponse, ): ... self.gateway = MicroService( service_role=ServiceRoleType.MEGASERVICE, service_type=ServiceType.GATEWAY, .... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
43fac86e-80d7-407f-8751-858001d9ff5b | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-GenAIExamples-001-Using_MicroService_to_implement_ChatQnA.md | unknown | 873a8b3a-1556-4151-901a-55cbe4d1f45c | 11 | opea-semantic-v1 | 11c5b798b96b82ec | handling user documents to be stored in VectorStore under a predefined database name. The first megaservice will then query the data from this predefined database.
 | ai_ref_knowledge | OPEA Documentation | handling user documents to be stored in VectorStore under a predefined database name. The first megaservice will then query the data from this predefined database.
 | handling user documents to be stored in VectorStore under a predefined database name. The first megaservice will then query the data from this predefined database.
 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4817fe3e-14a6-49d0-9b83-7888ed4da872 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-GenAIExamples-001-Using_MicroService_to_implement_ChatQnA.md | unknown | 873a8b3a-1556-4151-901a-55cbe4d1f45c | 29 | opea-semantic-v1 | b3d1d992b0d84386 | workflow changes may include adjustments needed for existing clients to interact with the new microservice architecture. However, careful planning and communication can mitigate any disruptions.
## Miscs
Performance Impact: The microservice architecture may impact performance metrics, depending on factors such as netwo... | ai_ref_knowledge | OPEA Documentation | workflow changes may include adjustments needed for existing clients to interact with the new microservice architecture. However, careful planning and communication can mitigate any disruptions.
## Miscs
Performance Impact: The microservice architecture may impact performance metrics, depending on factors such as netwo... | workflow changes may include adjustments needed for existing clients to interact with the new microservice architecture. However, careful planning and communication can mitigate any disruptions.
## Miscs
Performance Impact: The microservice architecture may impact performance metrics, depending on factors such as netwo... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5145cecc-ae70-481e-86ff-d1cd58c8686d | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-GenAIExamples-001-Using_MicroService_to_implement_ChatQnA.md | unknown | 873a8b3a-1556-4151-901a-55cbe4d1f45c | 3 | opea-semantic-v1 | fc173fb24394db2d | deployment challenges and limiting scalability. Any change or scaling requirement in one module necessitates redeploying the entire system, leading to potential downtime and increased complexity.
## Design Proposal | ai_ref_knowledge | OPEA Documentation | deployment challenges and limiting scalability. Any change or scaling requirement in one module necessitates redeploying the entire system, leading to potential downtime and increased complexity.
## Design Proposal | deployment challenges and limiting scalability. Any change or scaling requirement in one module necessitates redeploying the entire system, leading to potential downtime and increased complexity.
## Design Proposal | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
54971f21-ee8d-4776-a28a-32ec74c44d6e | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-GenAIExamples-001-Using_MicroService_to_implement_ChatQnA.md | unknown | 873a8b3a-1556-4151-901a-55cbe4d1f45c | 16 | opea-semantic-v1 | fc25a047a54e3b85 | def construct_data_service(self): dataprep = MicroService( name="dataprep", host=SERVICE_HOST_IP, port=5000, endpoint="/v1/dataprep", use_remote_service=True, service_type=ServiceType.DATAPREP, ) self.data_service.add(dataprep) self.data_gateway = DataPrepGateway(megaservice=self.data_service, host="0.0.0.0", port=self... | ai_ref_knowledge | OPEA Documentation | def construct_data_service(self): dataprep = MicroService( name="dataprep", host=SERVICE_HOST_IP, port=5000, endpoint="/v1/dataprep", use_remote_service=True, service_type=ServiceType.DATAPREP, ) self.data_service.add(dataprep) self.data_gateway = DataPrepGateway(megaservice=self.data_service, host="0.0.0.0", port=self... | def construct_data_service(self): dataprep = MicroService( name="dataprep", host=SERVICE_HOST_IP, port=5000, endpoint="/v1/dataprep", use_remote_service=True, service_type=ServiceType.DATAPREP, ) self.data_service.add(dataprep) self.data_gateway = DataPrepGateway(megaservice=self.data_service, host="0.0.0.0", port=self... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
58a2e1a1-08da-4c5c-9472-cd8c99c39405 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-GenAIExamples-001-Using_MicroService_to_implement_ChatQnA.md | unknown | 873a8b3a-1556-4151-901a-55cbe4d1f45c | 13 | opea-semantic-v1 | 44fa438ca6f3b5de | Users can use `ServiceOrchestrator` class to build the microservice pipeline and add a gateway for each megaservice.
```python
class ChatQnAService:
def __init__(self, rag_port=8888, data_port=9999):
self.rag_port = rag_port
self.data_port = data_port
self.rag_service = ServiceOrchestrator()
self.data_service = Se... | ai_ref_knowledge | OPEA Documentation | Users can use `ServiceOrchestrator` class to build the microservice pipeline and add a gateway for each megaservice.
```python
class ChatQnAService:
def __init__(self, rag_port=8888, data_port=9999):
self.rag_port = rag_port
self.data_port = data_port
self.rag_service = ServiceOrchestrator()
self.data_service = Se... | Users can use `ServiceOrchestrator` class to build the microservice pipeline and add a gateway for each megaservice.
```python
class ChatQnAService:
def __init__(self, rag_port=8888, data_port=9999):
self.rag_port = rag_port
self.data_port = data_port
self.rag_service = ServiceOrchestrator()
self.data_service = Se... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
61692426-db94-4ef0-bb2a-0039f539f68b | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-GenAIExamples-001-Using_MicroService_to_implement_ChatQnA.md | unknown | 873a8b3a-1556-4151-901a-55cbe4d1f45c | 28 | opea-semantic-v1 | 2bf98bee0d8748c7 | of the proposed microservice architecture include easier deployment, independent scaling of components, and improved fault isolation. Cons may include increased complexity in managing multiple services.
## Compatibility
Potential incompatible interface or workflow changes may include adjustments needed for existing cli... | ai_ref_knowledge | OPEA Documentation | of the proposed microservice architecture include easier deployment, independent scaling of components, and improved fault isolation. Cons may include increased complexity in managing multiple services.
## Compatibility
Potential incompatible interface or workflow changes may include adjustments needed for existing cli... | of the proposed microservice architecture include easier deployment, independent scaling of components, and improved fault isolation. Cons may include increased complexity in managing multiple services.
## Compatibility
Potential incompatible interface or workflow changes may include adjustments needed for existing cli... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
78b1ee19-f86d-44f0-b55c-c3b9a6318d0c | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-GenAIExamples-001-Using_MicroService_to_implement_ChatQnA.md | unknown | 873a8b3a-1556-4151-901a-55cbe4d1f45c | 18 | opea-semantic-v1 | ce5803c4b9f6edc3 | define microservices and megaservices using YAML for the ChatQnA application. This configuration outlines the endpoints for each microservice and specifies the workflow for the megaservices.
```yaml
opea_micro_services:
dataprep:
endpoint: http://localhost:5000/v1/chat/completions
embedding:
endpoint: http://localh... | ai_ref_knowledge | OPEA Documentation | define microservices and megaservices using YAML for the ChatQnA application. This configuration outlines the endpoints for each microservice and specifies the workflow for the megaservices.
```yaml
opea_micro_services:
dataprep:
endpoint: http://localhost:5000/v1/chat/completions
embedding:
endpoint: http://localh... | define microservices and megaservices using YAML for the ChatQnA application. This configuration outlines the endpoints for each microservice and specifies the workflow for the megaservices.
```yaml
opea_micro_services:
dataprep:
endpoint: http://localhost:5000/v1/chat/completions
embedding:
endpoint: http://localh... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
81367c72-e5f5-451b-9012-41fb714ecc61 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-GenAIExamples-001-Using_MicroService_to_implement_ChatQnA.md | unknown | 873a8b3a-1556-4151-901a-55cbe4d1f45c | 8 | opea-semantic-v1 | 04ceee6b26082613 | ### Gateway
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 versionin... | ai_ref_knowledge | OPEA Documentation | ### Gateway
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 versionin... | ### Gateway
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 versionin... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
817b14c2-8587-4024-b069-7f14cad28d6f | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-GenAIExamples-001-Using_MicroService_to_implement_ChatQnA.md | unknown | 873a8b3a-1556-4151-901a-55cbe4d1f45c | 7 | opea-semantic-v1 | 37a43689f0055a78 | 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.
### Gateway | ai_ref_knowledge | OPEA Documentation | 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.
### Gateway | 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.
### Gateway | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
909432c8-25a2-4455-98bc-85474686dc5b | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-GenAIExamples-001-Using_MicroService_to_implement_ChatQnA.md | unknown | 873a8b3a-1556-4151-901a-55cbe4d1f45c | 6 | opea-semantic-v1 | ff216a4b613bc3eb | ### Megaservice
A 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 compreh... | ai_ref_knowledge | OPEA Documentation | ### Megaservice
A 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 compreh... | ### Megaservice
A 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 compreh... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a2c87f58-2fd4-4624-950d-e6772febc995 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-GenAIExamples-001-Using_MicroService_to_implement_ChatQnA.md | unknown | 873a8b3a-1556-4151-901a-55cbe4d1f45c | 26 | opea-semantic-v1 | b8e7a85869819208 | def define_default_routes(self): self.service.app.router.add_api_route(self.endpoint, self.handle_request, methods=["POST"]) self.service.app.router.add_api_route(str(MegaServiceEndpoint.LIST_SERVICE), self.list_service, methods=["GET"]) self.service.app.router.add_api_route( str(MegaServiceEndpoint.LIST_PARAMETERS), s... | ai_ref_knowledge | OPEA Documentation | def define_default_routes(self): self.service.app.router.add_api_route(self.endpoint, self.handle_request, methods=["POST"]) self.service.app.router.add_api_route(str(MegaServiceEndpoint.LIST_SERVICE), self.list_service, methods=["GET"]) self.service.app.router.add_api_route( str(MegaServiceEndpoint.LIST_PARAMETERS), s... | def define_default_routes(self): self.service.app.router.add_api_route(self.endpoint, self.handle_request, methods=["POST"]) self.service.app.router.add_api_route(str(MegaServiceEndpoint.LIST_SERVICE), self.list_service, methods=["GET"]) self.service.app.router.add_api_route( str(MegaServiceEndpoint.LIST_PARAMETERS), s... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a881111e-dd56-4989-a466-4a8a08df3068 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-GenAIExamples-001-Using_MicroService_to_implement_ChatQnA.md | unknown | 873a8b3a-1556-4151-901a-55cbe4d1f45c | 0 | opea-semantic-v1 | 03ed7a1c62395af9 | ## Status Under Review
## Objective
This RFC aims to introduce the OPEA microservice design and demonstrate its application to Retrieval-Augmented Generation (RAG). The objective is to address the challenge of designing a flexible architecture for Enterprise AI applications by adopting a microservice approach. This app... | ai_ref_knowledge | OPEA Documentation | ## Status Under Review
## Objective
This RFC aims to introduce the OPEA microservice design and demonstrate its application to Retrieval-Augmented Generation (RAG). The objective is to address the challenge of designing a flexible architecture for Enterprise AI applications by adopting a microservice approach. This app... | ## Status Under Review
## Objective
This RFC aims to introduce the OPEA microservice design and demonstrate its application to Retrieval-Augmented Generation (RAG). The objective is to address the challenge of designing a flexible architecture for Enterprise AI applications by adopting a microservice approach. This app... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a9310dc8-0c89-42de-afd4-5bff29d0bf4a | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-GenAIExamples-001-Using_MicroService_to_implement_ChatQnA.md | unknown | 873a8b3a-1556-4151-901a-55cbe4d1f45c | 21 | opea-semantic-v1 | 7fbe5a37e931f69a | The following Python code demonstrates how to use the YAML configurations to initialize the microservices and megaservices, and set up the gateways for user interaction.
```python
from comps import ServiceOrchestratorWithYaml
from comps import ChatQnAGateway, DataPrepGateway
data_service = ServiceOrchestratorWithYaml(y... | ai_ref_knowledge | OPEA Documentation | The following Python code demonstrates how to use the YAML configurations to initialize the microservices and megaservices, and set up the gateways for user interaction.
```python
from comps import ServiceOrchestratorWithYaml
from comps import ChatQnAGateway, DataPrepGateway
data_service = ServiceOrchestratorWithYaml(y... | The following Python code demonstrates how to use the YAML configurations to initialize the microservices and megaservices, and set up the gateways for user interaction.
```python
from comps import ServiceOrchestratorWithYaml
from comps import ChatQnAGateway, DataPrepGateway
data_service = ServiceOrchestratorWithYaml(y... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b7bcbc1d-b67a-431e-8854-f7300bf57660 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-GenAIExamples-001-Using_MicroService_to_implement_ChatQnA.md | unknown | 873a8b3a-1556-4151-901a-55cbe4d1f45c | 14 | opea-semantic-v1 | 7d2df303e9d2fc20 | ```python class ChatQnAService: def __init__(self, rag_port=8888, data_port=9999): self.rag_port = rag_port self.data_port = data_port self.rag_service = ServiceOrchestrator() self.data_service = ServiceOrchestrator()
def construct_rag_service(self):
embedding = MicroService(
name="embedding",
host=SERVICE_HOST_IP,
... | ai_ref_knowledge | OPEA Documentation | ```python class ChatQnAService: def __init__(self, rag_port=8888, data_port=9999): self.rag_port = rag_port self.data_port = data_port self.rag_service = ServiceOrchestrator() self.data_service = ServiceOrchestrator()
def construct_rag_service(self):
embedding = MicroService(
name="embedding",
host=SERVICE_HOST_IP,
... | ```python class ChatQnAService: def __init__(self, rag_port=8888, data_port=9999): self.rag_port = rag_port self.data_port = data_port self.rag_service = ServiceOrchestrator() self.data_service = ServiceOrchestrator()
def construct_rag_service(self):
embedding = MicroService(
name="embedding",
host=SERVICE_HOST_IP,
... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
be4eaa38-af19-480b-a6a1-023b07007a73 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-GenAIExamples-001-Using_MicroService_to_implement_ChatQnA.md | unknown | 873a8b3a-1556-4151-901a-55cbe4d1f45c | 27 | opea-semantic-v1 | 87b30cd6ae7f976d | ...
## Alternatives Considered
An alternative approach could be to design a monolithic application for RAG instead of a microservice architecture. However, this approach may lack the flexibility and scalability offered by microservices. Pros of the proposed microservice architecture include easier deployment, independe... | ai_ref_knowledge | OPEA Documentation | ...
## Alternatives Considered
An alternative approach could be to design a monolithic application for RAG instead of a microservice architecture. However, this approach may lack the flexibility and scalability offered by microservices. Pros of the proposed microservice architecture include easier deployment, independe... | ...
## Alternatives Considered
An alternative approach could be to design a monolithic application for RAG instead of a microservice architecture. However, this approach may lack the flexibility and scalability offered by microservices. Pros of the proposed microservice architecture include easier deployment, independe... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
cf5a9543-9307-4d1d-bea6-fed5fc4277ee | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-GenAIExamples-001-Using_MicroService_to_implement_ChatQnA.md | unknown | 873a8b3a-1556-4151-901a-55cbe4d1f45c | 2 | opea-semantic-v1 | c0e351079e2aeb86 | out individual components. This scalability ensures that the system can efficiently manage high demand, distributing the load across multiple instances of each microservice as needed.
The microservices architecture contrasts sharply with monolithic approaches, such as the tightly coupled module structure found in LangC... | ai_ref_knowledge | OPEA Documentation | out individual components. This scalability ensures that the system can efficiently manage high demand, distributing the load across multiple instances of each microservice as needed.
The microservices architecture contrasts sharply with monolithic approaches, such as the tightly coupled module structure found in LangC... | out individual components. This scalability ensures that the system can efficiently manage high demand, distributing the load across multiple instances of each microservice as needed.
The microservices architecture contrasts sharply with monolithic approaches, such as the tightly coupled module structure found in LangC... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d6fae7fe-58bd-48cd-82e8-a78d7a4e30c2 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-GenAIExamples-001-Using_MicroService_to_implement_ChatQnA.md | unknown | 873a8b3a-1556-4151-901a-55cbe4d1f45c | 10 | opea-semantic-v1 | 3d3fafeded0cae12 | user data storage in VectorStore and is composed of a single microservice, dataprep. This megaservice provides a DataprepGateway, enabling user access through the `/v1/dataprep` endpoint.
The Gateway class facilitates the registration of additional endpoints, enhancing the system's flexibility and extensibility. The /v... | ai_ref_knowledge | OPEA Documentation | user data storage in VectorStore and is composed of a single microservice, dataprep. This megaservice provides a DataprepGateway, enabling user access through the `/v1/dataprep` endpoint.
The Gateway class facilitates the registration of additional endpoints, enhancing the system's flexibility and extensibility. The /v... | user data storage in VectorStore and is composed of a single microservice, dataprep. This megaservice provides a DataprepGateway, enabling user access through the `/v1/dataprep` endpoint.
The Gateway class facilitates the registration of additional endpoints, enhancing the system's flexibility and extensibility. The /v... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
dad3d319-f895-4a45-8882-161ba124ee83 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-GenAIExamples-001-Using_MicroService_to_implement_ChatQnA.md | unknown | 873a8b3a-1556-4151-901a-55cbe4d1f45c | 9 | opea-semantic-v1 | 57cd080c6dc31387 | 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.
### Proposal
The proposed architecture for the ChatQnA application involves the creation of two megaservices. The fir... | ai_ref_knowledge | OPEA Documentation | 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.
### Proposal
The proposed architecture for the ChatQnA application involves the creation of two megaservices. The fir... | 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.
### Proposal
The proposed architecture for the ChatQnA application involves the creation of two megaservices. The fir... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
eaa40f5d-e6c4-432d-956f-02d20ff84ecd | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-GenAIExamples-001-Using_MicroService_to_implement_ChatQnA.md | unknown | 873a8b3a-1556-4151-901a-55cbe4d1f45c | 22 | opea-semantic-v1 | d410fc5a44383838 | ServiceOrchestratorWithYaml from comps import ChatQnAGateway, DataPrepGateway data_service = ServiceOrchestratorWithYaml(yaml_file_path="dataprep.yaml") rag_service = ServiceOrchestratorWithYaml(yaml_file_path="rag.yaml") rag_gateway = ChatQnAGateway(data_service, port=8888) data_gateway = DataPrepGateway(data_service,... | ai_ref_knowledge | OPEA Documentation | ServiceOrchestratorWithYaml from comps import ChatQnAGateway, DataPrepGateway data_service = ServiceOrchestratorWithYaml(yaml_file_path="dataprep.yaml") rag_service = ServiceOrchestratorWithYaml(yaml_file_path="rag.yaml") rag_gateway = ChatQnAGateway(data_service, port=8888) data_gateway = DataPrepGateway(data_service,... | ServiceOrchestratorWithYaml from comps import ChatQnAGateway, DataPrepGateway data_service = ServiceOrchestratorWithYaml(yaml_file_path="dataprep.yaml") rag_service = ServiceOrchestratorWithYaml(yaml_file_path="rag.yaml") rag_gateway = ChatQnAGateway(data_service, port=8888) data_gateway = DataPrepGateway(data_service,... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f3fcae38-0752-45c0-8760-e27c3c463a8b | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-GenAIExamples-001-Using_MicroService_to_implement_ChatQnA.md | unknown | 873a8b3a-1556-4151-901a-55cbe4d1f45c | 15 | opea-semantic-v1 | 4b6f153dd5a457ee | use_remote_service=True, service_type=ServiceType.RERANK, ) llm = MicroService( name="llm", host=SERVICE_HOST_IP, port=9000, endpoint="/v1/chat/completions", use_remote_service=True, service_type=ServiceType.LLM, ) self.rag_service.add(embedding).add(retriever).add(rerank).add(llm) self.rag_service.flow_to(embedding, r... | ai_ref_knowledge | OPEA Documentation | use_remote_service=True, service_type=ServiceType.RERANK, ) llm = MicroService( name="llm", host=SERVICE_HOST_IP, port=9000, endpoint="/v1/chat/completions", use_remote_service=True, service_type=ServiceType.LLM, ) self.rag_service.add(embedding).add(retriever).add(rerank).add(llm) self.rag_service.flow_to(embedding, r... | use_remote_service=True, service_type=ServiceType.RERANK, ) llm = MicroService( name="llm", host=SERVICE_HOST_IP, port=9000, endpoint="/v1/chat/completions", use_remote_service=True, service_type=ServiceType.LLM, ) self.rag_service.add(embedding).add(retriever).add(rerank).add(llm) self.rag_service.flow_to(embedding, r... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f6b5e803-3132-4deb-90b8-f104e09049a5 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-GenAIExamples-001-Using_MicroService_to_implement_ChatQnA.md | unknown | 873a8b3a-1556-4151-901a-55cbe4d1f45c | 19 | opea-semantic-v1 | 23e3e25bf07cd993 | ```yaml opea_micro_services: dataprep: endpoint: http://localhost:5000/v1/chat/completions embedding: endpoint: http://localhost:6000/v1/embeddings retrieval: endpoint: http://localhost:7000/v1/retrieval reranking: endpoint: http://localhost:8000/v1/reranking llm: endpoint: http://localhost:9000/v1/chat/completions
ope... | ai_ref_knowledge | OPEA Documentation | ```yaml opea_micro_services: dataprep: endpoint: http://localhost:5000/v1/chat/completions embedding: endpoint: http://localhost:6000/v1/embeddings retrieval: endpoint: http://localhost:7000/v1/retrieval reranking: endpoint: http://localhost:8000/v1/reranking llm: endpoint: http://localhost:9000/v1/chat/completions
ope... | ```yaml opea_micro_services: dataprep: endpoint: http://localhost:5000/v1/chat/completions embedding: endpoint: http://localhost:6000/v1/embeddings retrieval: endpoint: http://localhost:7000/v1/retrieval reranking: endpoint: http://localhost:8000/v1/reranking llm: endpoint: http://localhost:9000/v1/chat/completions
ope... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f8e23f6a-5d0a-4126-8ed5-de6a3e83b5f5 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-GenAIExamples-001-Using_MicroService_to_implement_ChatQnA.md | unknown | 873a8b3a-1556-4151-901a-55cbe4d1f45c | 1 | opea-semantic-v1 | 360e6e40df620c9f | users to access services through endpoints exposed by the gateway. The architecture is general and RAG is the first example that we want to apply.
## Motivation
In designing the Enterprise AI applications, leveraging a microservices architecture offers significant advantages, particularly in handling large volumes of u... | ai_ref_knowledge | OPEA Documentation | users to access services through endpoints exposed by the gateway. The architecture is general and RAG is the first example that we want to apply.
## Motivation
In designing the Enterprise AI applications, leveraging a microservices architecture offers significant advantages, particularly in handling large volumes of u... | users to access services through endpoints exposed by the gateway. The architecture is general and RAG is the first example that we want to apply.
## Motivation
In designing the Enterprise AI applications, leveraging a microservices architecture offers significant advantages, particularly in handling large volumes of u... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
00ebcc38-126c-475c-86e2-af4e6749cbe2 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-OPEA-001-Overall-Design.md | unknown | b76538fc-ec3d-4a1e-b9cb-ea62646a9c67 | 2 | opea-semantic-v1 | d033a8247280a11d | ## Motivation
This RFC is used to present the OPEA overall design philosophy, including overall architecture, working flow, component design, for community discussion. | ai_ref_knowledge | OPEA Documentation | ## Motivation
This RFC is used to present the OPEA overall design philosophy, including overall architecture, working flow, component design, for community discussion. | ## Motivation
This RFC is used to present the OPEA overall design philosophy, including overall architecture, working flow, component design, for community discussion. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
021d1df9-f61e-4c8c-b336-ac5984be6744 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-OPEA-001-Overall-Design.md | unknown | b76538fc-ec3d-4a1e-b9cb-ea62646a9c67 | 1 | opea-semantic-v1 | e2c459ccff38b0c7 | Have a stable, extensible, secure, and easy-of-use orchestration framework design for OPEA users to quickly build their own GenAI applications.
The requirements include but not limited to: | ai_ref_knowledge | OPEA Documentation | Have a stable, extensible, secure, and easy-of-use orchestration framework design for OPEA users to quickly build their own GenAI applications.
The requirements include but not limited to: | Have a stable, extensible, secure, and easy-of-use orchestration framework design for OPEA users to quickly build their own GenAI applications.
The requirements include but not limited to: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
089efc76-68d2-49a2-82ea-2f6dd1e2778a | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-OPEA-001-Overall-Design.md | unknown | b76538fc-ec3d-4a1e-b9cb-ea62646a9c67 | 18 | opea-semantic-v1 | ec06c436e334e238 | - TODO List:
- [ ] Micro Service specification
- [ ] Mega Service specification
- [ ] static cloud resource allocator vs dynamic cloud resource allocator
- [ ] open telemetry support
- [ ] authentication and trusted env | ai_ref_knowledge | OPEA Documentation | - TODO List:
- [ ] Micro Service specification
- [ ] Mega Service specification
- [ ] static cloud resource allocator vs dynamic cloud resource allocator
- [ ] open telemetry support
- [ ] authentication and trusted env | - TODO List:
- [ ] Micro Service specification
- [ ] Mega Service specification
- [ ] static cloud resource allocator vs dynamic cloud resource allocator
- [ ] open telemetry support
- [ ] authentication and trusted env | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
109dbc65-d7f1-4288-98ec-9637bf171386 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-OPEA-001-Overall-Design.md | unknown | b76538fc-ec3d-4a1e-b9cb-ea62646a9c67 | 15 | opea-semantic-v1 | 7fe6f7aaf6713332 | 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.
3. Gateway | ai_ref_knowledge | OPEA Documentation | 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.
3. Gateway | 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.
3. Gateway | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
2e10f6e7-3d70-420d-ac8e-ebf1d0c0d24d | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-OPEA-001-Overall-Design.md | unknown | b76538fc-ec3d-4a1e-b9cb-ea62646a9c67 | 12 | opea-semantic-v1 | 52759bce28c5a30f | 1. Microservice
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-contai... | ai_ref_knowledge | OPEA Documentation | 1. Microservice
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-contai... | 1. Microservice
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-contai... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
50abef2a-be57-489e-ab3b-0f0f7fdc65d3 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-OPEA-001-Overall-Design.md | unknown | b76538fc-ec3d-4a1e-b9cb-ea62646a9c67 | 10 | opea-semantic-v1 | eee32e1d0c49027c | 4. GenAIEval
The evaluation, benchmark, and scorecard suite for OPEA, targeting for performance on throughput and latency, accuracy on popular evaluation harness, safety, and hallucination. | ai_ref_knowledge | OPEA Documentation | 4. GenAIEval
The evaluation, benchmark, and scorecard suite for OPEA, targeting for performance on throughput and latency, accuracy on popular evaluation harness, safety, and hallucination. | 4. GenAIEval
The evaluation, benchmark, and scorecard suite for OPEA, targeting for performance on throughput and latency, accuracy on popular evaluation harness, safety, and hallucination. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
60c408a2-fd3b-41b2-8305-56e7ab976607 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-OPEA-001-Overall-Design.md | unknown | b76538fc-ec3d-4a1e-b9cb-ea62646a9c67 | 8 | opea-semantic-v1 | 0056519b5c40a6b4 | 3. GenAIInfra
The containerization and cloud native suite for OPEA, including artifacts to deploy GenAIExamples in a cloud native way, which can be used by enterprise users to deploy to their own cloud. | ai_ref_knowledge | OPEA Documentation | 3. GenAIInfra
The containerization and cloud native suite for OPEA, including artifacts to deploy GenAIExamples in a cloud native way, which can be used by enterprise users to deploy to their own cloud. | 3. GenAIInfra
The containerization and cloud native suite for OPEA, including artifacts to deploy GenAIExamples in a cloud native way, which can be used by enterprise users to deploy to their own cloud. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6305e286-0f72-4412-b9b7-14eaddbf0966 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-OPEA-001-Overall-Design.md | unknown | b76538fc-ec3d-4a1e-b9cb-ea62646a9c67 | 9 | opea-semantic-v1 | 63c3ef68e193c0e0 | for OPEA, including artifacts to deploy GenAIExamples in a cloud native way, which can be used by enterprise users to deploy to their own cloud.
4. GenAIEval | ai_ref_knowledge | OPEA Documentation | for OPEA, including artifacts to deploy GenAIExamples in a cloud native way, which can be used by enterprise users to deploy to their own cloud.
4. GenAIEval | for OPEA, including artifacts to deploy GenAIExamples in a cloud native way, which can be used by enterprise users to deploy to their own cloud.
4. GenAIEval | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
9578d4af-88c9-4666-ab6d-a685bbe09253 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-OPEA-001-Overall-Design.md | unknown | b76538fc-ec3d-4a1e-b9cb-ea62646a9c67 | 7 | opea-semantic-v1 | 21a90969bf986b67 | The collective list of Generative AI (GenAI) and Retrieval-Augmented Generation (RAG) examples, targeting for demonstrating the whole orchestration pipeline.
3. GenAIInfra | ai_ref_knowledge | OPEA Documentation | The collective list of Generative AI (GenAI) and Retrieval-Augmented Generation (RAG) examples, targeting for demonstrating the whole orchestration pipeline.
3. GenAIInfra | The collective list of Generative AI (GenAI) and Retrieval-Augmented Generation (RAG) examples, targeting for demonstrating the whole orchestration pipeline.
3. GenAIInfra | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
9bfeae30-e994-4b4c-8448-2276024601ae | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-OPEA-001-Overall-Design.md | unknown | b76538fc-ec3d-4a1e-b9cb-ea62646a9c67 | 3 | opea-semantic-v1 | d40a974e7338e750 | This RFC is used to present the OPEA overall design philosophy, including overall architecture, working flow, component design, for community discussion.
## Design Proposal | ai_ref_knowledge | OPEA Documentation | This RFC is used to present the OPEA overall design philosophy, including overall architecture, working flow, component design, for community discussion.
## Design Proposal | This RFC is used to present the OPEA overall design philosophy, including overall architecture, working flow, component design, for community discussion.
## Design Proposal | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a28998be-f4a5-4e5b-81b7-fe29682809eb | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-OPEA-001-Overall-Design.md | unknown | b76538fc-ec3d-4a1e-b9cb-ea62646a9c67 | 17 | opea-semantic-v1 | ee5c142094ab2190 | 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.
## Alternatives Considered | ai_ref_knowledge | OPEA Documentation | 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.
## Alternatives Considered | 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.
## Alternatives Considered | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a5bdad19-cbf1-4a5c-9fe6-b3a6000ca59e | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-OPEA-001-Overall-Design.md | unknown | b76538fc-ec3d-4a1e-b9cb-ea62646a9c67 | 4 | opea-semantic-v1 | d3b80cca22d61061 | 1. GenAIComps
The suite of microservices, leveraging a service composer to assemble a mega-service tailored for real-world Enterprise AI applications. | ai_ref_knowledge | OPEA Documentation | 1. GenAIComps
The suite of microservices, leveraging a service composer to assemble a mega-service tailored for real-world Enterprise AI applications. | 1. GenAIComps
The suite of microservices, leveraging a service composer to assemble a mega-service tailored for real-world Enterprise AI applications. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b703b3bc-b4b4-4f25-9ddb-c46c6c77990f | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-OPEA-001-Overall-Design.md | unknown | b76538fc-ec3d-4a1e-b9cb-ea62646a9c67 | 16 | opea-semantic-v1 | e088e83bb47375eb | 3. Gateway
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... | ai_ref_knowledge | OPEA Documentation | 3. Gateway
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... | 3. Gateway
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... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d410000d-5e52-40a5-a338-f32314e21470 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-OPEA-001-Overall-Design.md | unknown | b76538fc-ec3d-4a1e-b9cb-ea62646a9c67 | 6 | opea-semantic-v1 | 918fd5c3823f68dc | 2. GenAIExamples
The collective list of Generative AI (GenAI) and Retrieval-Augmented Generation (RAG) examples, targeting for demonstrating the whole orchestration pipeline. | ai_ref_knowledge | OPEA Documentation | 2. GenAIExamples
The collective list of Generative AI (GenAI) and Retrieval-Augmented Generation (RAG) examples, targeting for demonstrating the whole orchestration pipeline. | 2. GenAIExamples
The collective list of Generative AI (GenAI) and Retrieval-Augmented Generation (RAG) examples, targeting for demonstrating the whole orchestration pipeline. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
da68ffe1-24fb-4a19-afdd-84bba4a41499 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-OPEA-001-Overall-Design.md | unknown | b76538fc-ec3d-4a1e-b9cb-ea62646a9c67 | 11 | opea-semantic-v1 | 03fa0ca61987bb6c | The evaluation, benchmark, and scorecard suite for OPEA, targeting for performance on throughput and latency, accuracy on popular evaluation harness, safety, and hallucination.
The proposed OPEA workflow is | ai_ref_knowledge | OPEA Documentation | The evaluation, benchmark, and scorecard suite for OPEA, targeting for performance on throughput and latency, accuracy on popular evaluation harness, safety, and hallucination.
The proposed OPEA workflow is | The evaluation, benchmark, and scorecard suite for OPEA, targeting for performance on throughput and latency, accuracy on popular evaluation harness, safety, and hallucination.
The proposed OPEA workflow is | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ea521b09-e2d0-4320-97ce-f209c9ddd169 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-OPEA-001-Overall-Design.md | unknown | b76538fc-ec3d-4a1e-b9cb-ea62646a9c67 | 5 | opea-semantic-v1 | abbe3b924bd47afe | The suite of microservices, leveraging a service composer to assemble a mega-service tailored for real-world Enterprise AI applications.
2. GenAIExamples | ai_ref_knowledge | OPEA Documentation | The suite of microservices, leveraging a service composer to assemble a mega-service tailored for real-world Enterprise AI applications.
2. GenAIExamples | The suite of microservices, leveraging a service composer to assemble a mega-service tailored for real-world Enterprise AI applications.
2. GenAIExamples | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
fd027f5a-a9a5-4f7d-8c2b-1a48c123f931 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-OPEA-001-Overall-Design.md | unknown | b76538fc-ec3d-4a1e-b9cb-ea62646a9c67 | 14 | opea-semantic-v1 | d124938d45ca1fc4 | 2. Megaservice
A 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 comprehe... | ai_ref_knowledge | OPEA Documentation | 2. Megaservice
A 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 comprehe... | 2. Megaservice
A 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 comprehe... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
fd5649fb-49dc-4340-a381-3f31d6a78bbc | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-OPEA-001-Overall-Design.md | unknown | b76538fc-ec3d-4a1e-b9cb-ea62646a9c67 | 13 | opea-semantic-v1 | 5e416539f5f00307 | 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.
2. Megaservice | ai_ref_knowledge | OPEA Documentation | 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.
2. Megaservice | 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.
2. Megaservice | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ff006859-4a54-42ec-94da-db4c44ac321b | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-16-OPEA-001-Overall-Design.md | unknown | b76538fc-ec3d-4a1e-b9cb-ea62646a9c67 | 0 | opea-semantic-v1 | 21214d572c132696 | ## Objective
Have a stable, extensible, secure, and easy-of-use orchestration framework design for OPEA users to quickly build their own GenAI applications. | ai_ref_knowledge | OPEA Documentation | ## Objective
Have a stable, extensible, secure, and easy-of-use orchestration framework design for OPEA users to quickly build their own GenAI applications. | ## Objective
Have a stable, extensible, secure, and easy-of-use orchestration framework design for OPEA users to quickly build their own GenAI applications. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
02876a7a-ef68-4039-bf7e-e16970e1ec40 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-17-OPEA-001-Deployment-Design.md | unknown | 56c47ff9-fa2c-4bc3-9a63-1c8fa47738c5 | 8 | opea-semantic-v1 | 4a57a5fb083c4681 | application to Kubernetes environment, you should define and convert the YAML configuration file to an appropriate [docker compose](https://docs.docker.com/compose/), or [GenAI Microservice Connector-(GMC)](https://github.com/opea-project/GenAIInfra/tree/main/microservices-connector) custom resource file.
Note: A conve... | ai_ref_knowledge | OPEA Documentation | application to Kubernetes environment, you should define and convert the YAML configuration file to an appropriate [docker compose](https://docs.docker.com/compose/), or [GenAI Microservice Connector-(GMC)](https://github.com/opea-project/GenAIInfra/tree/main/microservices-connector) custom resource file.
Note: A conve... | application to Kubernetes environment, you should define and convert the YAML configuration file to an appropriate [docker compose](https://docs.docker.com/compose/), or [GenAI Microservice Connector-(GMC)](https://github.com/opea-project/GenAIInfra/tree/main/microservices-connector) custom resource file.
Note: A conve... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
06d57c6d-38a4-4457-a813-54b07738d620 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-17-OPEA-001-Deployment-Design.md | unknown | 56c47ff9-fa2c-4bc3-9a63-1c8fa47738c5 | 10 | opea-semantic-v1 | 754eb23d93f1e375 | A sample GMC [Custom Resource](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/) is like below:
```yaml
apiVersion: gmc.opea.io/v1alpha3
kind: GMConnector
metadata:
labels:
app.kubernetes.io/name: gmconnector
name: chatqna
namespace: gmcsample
spec:
routerConfig:
name: rou... | ai_ref_knowledge | OPEA Documentation | A sample GMC [Custom Resource](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/) is like below:
```yaml
apiVersion: gmc.opea.io/v1alpha3
kind: GMConnector
metadata:
labels:
app.kubernetes.io/name: gmconnector
name: chatqna
namespace: gmcsample
spec:
routerConfig:
name: rou... | A sample GMC [Custom Resource](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/) is like below:
```yaml
apiVersion: gmc.opea.io/v1alpha3
kind: GMConnector
metadata:
labels:
app.kubernetes.io/name: gmconnector
name: chatqna
namespace: gmcsample
spec:
routerConfig:
name: rou... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
17a36ac3-3c71-4dc5-9dbb-38d26f06acab | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-17-OPEA-001-Deployment-Design.md | unknown | 56c47ff9-fa2c-4bc3-9a63-1c8fa47738c5 | 11 | opea-semantic-v1 | 12ff449768abe5e4 | llm-service config: tgi_endpoint: tgi-service gmcTokenSecret: gmc-tokens endpoint: /v1/chat/completions - name: Tgi internalService: serviceName: tgi-service config: gmcTokenSecret: gmc-tokens hostPath: /root/GMC/data/tgi modelId: Intel/neural-chat-7b-v3-3 endpoint: /generate isDownstreamService: true
There should be a... | ai_ref_knowledge | OPEA Documentation | llm-service config: tgi_endpoint: tgi-service gmcTokenSecret: gmc-tokens endpoint: /v1/chat/completions - name: Tgi internalService: serviceName: tgi-service config: gmcTokenSecret: gmc-tokens hostPath: /root/GMC/data/tgi modelId: Intel/neural-chat-7b-v3-3 endpoint: /generate isDownstreamService: true
There should be a... | llm-service config: tgi_endpoint: tgi-service gmcTokenSecret: gmc-tokens endpoint: /v1/chat/completions - name: Tgi internalService: serviceName: tgi-service config: gmcTokenSecret: gmc-tokens hostPath: /root/GMC/data/tgi modelId: Intel/neural-chat-7b-v3-3 endpoint: /generate isDownstreamService: true
There should be a... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
31594d73-40ff-43a5-8068-b6be5796b53a | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-17-OPEA-001-Deployment-Design.md | unknown | 56c47ff9-fa2c-4bc3-9a63-1c8fa47738c5 | 15 | opea-semantic-v1 | e34c8b1bd7e3b7ce | - TODO List:
- [ ] one click deployment on AWS, GCP, Azure cloud
- [ ] static cloud resource allocator vs dynamic cloud resource allocator
- [ ] k8s GMC with istio | ai_ref_knowledge | OPEA Documentation | - TODO List:
- [ ] one click deployment on AWS, GCP, Azure cloud
- [ ] static cloud resource allocator vs dynamic cloud resource allocator
- [ ] k8s GMC with istio | - TODO List:
- [ ] one click deployment on AWS, GCP, Azure cloud
- [ ] static cloud resource allocator vs dynamic cloud resource allocator
- [ ] k8s GMC with istio | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
38c516b3-ba17-4da7-be73-473064e537d7 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-17-OPEA-001-Deployment-Design.md | unknown | 56c47ff9-fa2c-4bc3-9a63-1c8fa47738c5 | 4 | opea-semantic-v1 | 54cc233374a1c2c0 | Here is a YAML example for constructing a RAG (Retrieval-Augmented Generation) application:
```yaml
opea_micro_services:
embedding:
endpoint: /v1/embeddings
port: 6000
retrieval:
endpoint: /v1/retrieval
port: 7000
reranking:
endpoint: /v1/reranking
port: 8000
llm:
endpoint: /v1/chat/completions
port: 9000 | ai_ref_knowledge | OPEA Documentation | Here is a YAML example for constructing a RAG (Retrieval-Augmented Generation) application:
```yaml
opea_micro_services:
embedding:
endpoint: /v1/embeddings
port: 6000
retrieval:
endpoint: /v1/retrieval
port: 7000
reranking:
endpoint: /v1/reranking
port: 8000
llm:
endpoint: /v1/chat/completions
port: 9000 | Here is a YAML example for constructing a RAG (Retrieval-Augmented Generation) application:
```yaml
opea_micro_services:
embedding:
endpoint: /v1/embeddings
port: 6000
retrieval:
endpoint: /v1/retrieval
port: 7000
reranking:
endpoint: /v1/reranking
port: 8000
llm:
endpoint: /v1/chat/completions
port: 9000 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4ddd1595-8674-4206-b6a6-2a84b76df2fe | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-17-OPEA-001-Deployment-Design.md | unknown | 56c47ff9-fa2c-4bc3-9a63-1c8fa47738c5 | 1 | opea-semantic-v1 | 20358f2c0a19c38a | Have a clear and good design for users to deploy their own GenAI applications on docker or Kubernetes environment.
**Motivation** | ai_ref_knowledge | OPEA Documentation | Have a clear and good design for users to deploy their own GenAI applications on docker or Kubernetes environment.
**Motivation** | Have a clear and good design for users to deploy their own GenAI applications on docker or Kubernetes environment.
**Motivation** | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4de1644b-f434-42d0-93f3-bba690579d49 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-17-OPEA-001-Deployment-Design.md | unknown | 56c47ff9-fa2c-4bc3-9a63-1c8fa47738c5 | 13 | opea-semantic-v1 | 13d8eae8e605e276 | ```bash $kubectl get gmconnectors.gmc.opea.io -n gmcsample NAME URL READY AGE chatqa http://router-service.gmcsample.svc.cluster.local:8080 Success 3m
And the user can access the application pipeline via the value of `URL` field in above. | ai_ref_knowledge | OPEA Documentation | ```bash $kubectl get gmconnectors.gmc.opea.io -n gmcsample NAME URL READY AGE chatqa http://router-service.gmcsample.svc.cluster.local:8080 Success 3m
And the user can access the application pipeline via the value of `URL` field in above. | ```bash $kubectl get gmconnectors.gmc.opea.io -n gmcsample NAME URL READY AGE chatqa http://router-service.gmcsample.svc.cluster.local:8080 Success 3m
And the user can access the application pipeline via the value of `URL` field in above. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6f944d94-ed59-4ace-95ca-7b82b8662922 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-17-OPEA-001-Deployment-Design.md | unknown | 56c47ff9-fa2c-4bc3-9a63-1c8fa47738c5 | 7 | opea-semantic-v1 | 99d69f6262efe7ea | This YAML will be acting as a unified language interface for end user to define their GenAI Application.
When deploying the GenAI application to Kubernetes environment, you should define and convert the YAML configuration file to an appropriate [docker compose](https://docs.docker.com/compose/), or [GenAI Microservice ... | ai_ref_knowledge | OPEA Documentation | This YAML will be acting as a unified language interface for end user to define their GenAI Application.
When deploying the GenAI application to Kubernetes environment, you should define and convert the YAML configuration file to an appropriate [docker compose](https://docs.docker.com/compose/), or [GenAI Microservice ... | This YAML will be acting as a unified language interface for end user to define their GenAI Application.
When deploying the GenAI application to Kubernetes environment, you should define and convert the YAML configuration file to an appropriate [docker compose](https://docs.docker.com/compose/), or [GenAI Microservice ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
76f7fd0b-4e6b-4a0c-bd68-3252c37cba7c | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-17-OPEA-001-Deployment-Design.md | unknown | 56c47ff9-fa2c-4bc3-9a63-1c8fa47738c5 | 9 | opea-semantic-v1 | 1e2334cd335d997f | Note: A convert tool will be provided for OPEA to convert unified language interface to docker componse or GMC.
A sample GMC [Custom Resource](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/) is like below: | ai_ref_knowledge | OPEA Documentation | Note: A convert tool will be provided for OPEA to convert unified language interface to docker componse or GMC.
A sample GMC [Custom Resource](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/) is like below: | Note: A convert tool will be provided for OPEA to convert unified language interface to docker componse or GMC.
A sample GMC [Custom Resource](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/) is like below: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7da47a92-d1bd-429e-b8a8-70923235138a | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-17-OPEA-001-Deployment-Design.md | unknown | 56c47ff9-fa2c-4bc3-9a63-1c8fa47738c5 | 5 | opea-semantic-v1 | 7c49b68e4facaad7 | ```yaml opea_micro_services: embedding: endpoint: /v1/embeddings port: 6000 retrieval: endpoint: /v1/retrieval port: 7000 reranking: endpoint: /v1/reranking port: 8000 llm: endpoint: /v1/chat/completions port: 9000
opea_mega_service:
port: 8080
mega_flow:
- embedding >> retrieval >> reranking >> llm | ai_ref_knowledge | OPEA Documentation | ```yaml opea_micro_services: embedding: endpoint: /v1/embeddings port: 6000 retrieval: endpoint: /v1/retrieval port: 7000 reranking: endpoint: /v1/reranking port: 8000 llm: endpoint: /v1/chat/completions port: 9000
opea_mega_service:
port: 8080
mega_flow:
- embedding >> retrieval >> reranking >> llm | ```yaml opea_micro_services: embedding: endpoint: /v1/embeddings port: 6000 retrieval: endpoint: /v1/retrieval port: 7000 reranking: endpoint: /v1/reranking port: 8000 llm: endpoint: /v1/chat/completions port: 9000
opea_mega_service:
port: 8080
mega_flow:
- embedding >> retrieval >> reranking >> llm | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7f1600ca-b866-4582-a48d-858af183093d | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-17-OPEA-001-Deployment-Design.md | unknown | 56c47ff9-fa2c-4bc3-9a63-1c8fa47738c5 | 2 | opea-semantic-v1 | f842bda82031d110 | Here is a python example for constructing a RAG (Retrieval-Augmented Generation) application:
```python
from comps import MicroService, ServiceOrchestrator
class ChatQnAService:
def __init__(self, port=8080):
self.service_builder = ServiceOrchestrator(port=port, endpoint="/v1/chatqna")
def add_remote_service(self)... | ai_ref_knowledge | OPEA Documentation | Here is a python example for constructing a RAG (Retrieval-Augmented Generation) application:
```python
from comps import MicroService, ServiceOrchestrator
class ChatQnAService:
def __init__(self, port=8080):
self.service_builder = ServiceOrchestrator(port=port, endpoint="/v1/chatqna")
def add_remote_service(self)... | Here is a python example for constructing a RAG (Retrieval-Augmented Generation) application:
```python
from comps import MicroService, ServiceOrchestrator
class ChatQnAService:
def __init__(self, port=8080):
self.service_builder = ServiceOrchestrator(port=port, endpoint="/v1/chatqna")
def add_remote_service(self)... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
801b5f46-ecdf-4661-a338-6813846e7a09 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-17-OPEA-001-Deployment-Design.md | unknown | 56c47ff9-fa2c-4bc3-9a63-1c8fa47738c5 | 0 | opea-semantic-v1 | 84a1d6c7f8e9786c | **Objective**
Have a clear and good design for users to deploy their own GenAI applications on docker or Kubernetes environment. | ai_ref_knowledge | OPEA Documentation | **Objective**
Have a clear and good design for users to deploy their own GenAI applications on docker or Kubernetes environment. | **Objective**
Have a clear and good design for users to deploy their own GenAI applications on docker or Kubernetes environment. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
bbae88ac-929a-4d94-ac1e-6b586e3345c9 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-17-OPEA-001-Deployment-Design.md | unknown | 56c47ff9-fa2c-4bc3-9a63-1c8fa47738c5 | 6 | opea-semantic-v1 | 7f52c8e2f5c677d5 | opea_mega_service: port: 8080 mega_flow: - embedding >> retrieval >> reranking >> llm
This YAML will be acting as a unified language interface for end user to define their GenAI Application. | ai_ref_knowledge | OPEA Documentation | opea_mega_service: port: 8080 mega_flow: - embedding >> retrieval >> reranking >> llm
This YAML will be acting as a unified language interface for end user to define their GenAI Application. | opea_mega_service: port: 8080 mega_flow: - embedding >> retrieval >> reranking >> llm
This YAML will be acting as a unified language interface for end user to define their GenAI Application. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
bca16261-974a-44db-837f-b1318f42e884 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-17-OPEA-001-Deployment-Design.md | unknown | 56c47ff9-fa2c-4bc3-9a63-1c8fa47738c5 | 14 | opea-semantic-v1 | 6238884901e13388 | And the user can access the application pipeline via the value of `URL` field in above.
The whole deployment process illustrated by the diagram below. | ai_ref_knowledge | OPEA Documentation | And the user can access the application pipeline via the value of `URL` field in above.
The whole deployment process illustrated by the diagram below. | And the user can access the application pipeline via the value of `URL` field in above.
The whole deployment process illustrated by the diagram below. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
bcb67ee1-7bee-43af-bf8e-dc140250395c | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-17-OPEA-001-Deployment-Design.md | unknown | 56c47ff9-fa2c-4bc3-9a63-1c8fa47738c5 | 3 | opea-semantic-v1 | 1611253bb3f0c0a9 | use_remote_service=True ) rerank = MicroService( name="rerank", port=8000, expose_endpoint="/v1/reranking", use_remote_service=True ) llm = MicroService( name="llm", port=9000, expose_endpoint="/v1/chat/completions", use_remote_service=True ) self.service_builder.add(embedding).add(retriever).add(rerank).add(llm) self.... | ai_ref_knowledge | OPEA Documentation | use_remote_service=True ) rerank = MicroService( name="rerank", port=8000, expose_endpoint="/v1/reranking", use_remote_service=True ) llm = MicroService( name="llm", port=9000, expose_endpoint="/v1/chat/completions", use_remote_service=True ) self.service_builder.add(embedding).add(retriever).add(rerank).add(llm) self.... | use_remote_service=True ) rerank = MicroService( name="rerank", port=8000, expose_endpoint="/v1/reranking", use_remote_service=True ) llm = MicroService( name="llm", port=9000, expose_endpoint="/v1/chat/completions", use_remote_service=True ) self.service_builder.add(embedding).add(retriever).add(rerank).add(llm) self.... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ca767830-aa33-4776-a958-1e256d335e8b | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-17-OPEA-001-Deployment-Design.md | unknown | 56c47ff9-fa2c-4bc3-9a63-1c8fa47738c5 | 12 | opea-semantic-v1 | e9f39118b028b192 | There should be an available `gmconnectors.gmc.opea.io` CR named `chatqna` under the namespace `gmcsample`, showing below：
```bash
$kubectl get gmconnectors.gmc.opea.io -n gmcsample
NAME URL READY AGE
chatqa http://router-service.gmcsample.svc.cluster.local:8080 Success 3m | ai_ref_knowledge | OPEA Documentation | There should be an available `gmconnectors.gmc.opea.io` CR named `chatqna` under the namespace `gmcsample`, showing below：
```bash
$kubectl get gmconnectors.gmc.opea.io -n gmcsample
NAME URL READY AGE
chatqa http://router-service.gmcsample.svc.cluster.local:8080 Success 3m | There should be an available `gmconnectors.gmc.opea.io` CR named `chatqna` under the namespace `gmcsample`, showing below：
```bash
$kubectl get gmconnectors.gmc.opea.io -n gmcsample
NAME URL READY AGE
chatqa http://router-service.gmcsample.svc.cluster.local:8080 Success 3m | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
07200bc8-d528-499a-8319-f109662baa35 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-24-OPEA-001-Code-Structure.md | unknown | 83e08701-bd14-428c-b4fd-2cb853b9eb00 | 1 | opea-semantic-v1 | e627f8ca88b2aefd | on. We need a clear definition on where the new code for a given feature should be put for a consistent and well-orgnized code structure.
## Design Proposal | ai_ref_knowledge | OPEA Documentation | on. We need a clear definition on where the new code for a given feature should be put for a consistent and well-orgnized code structure.
## Design Proposal | on. We need a clear definition on where the new code for a given feature should be put for a consistent and well-orgnized code structure.
## Design Proposal | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
349923c2-8256-45bf-8577-4ee220bddfc7 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-24-OPEA-001-Code-Structure.md | unknown | 83e08701-bd14-428c-b4fd-2cb853b9eb00 | 6 | opea-semantic-v1 | 89e279b7bd3bcadd | The proposed code structure of GenAIComps is:
GenAIComps/
└── comps/
└── llms/
├── text-generation/
│ ├── tgi-gaudi/
│ │ ├── dockerfile
│ │ └── llm.py
│ ├── tgi-xeon/
│ │ ├── dockerfile
│ │ └── llm.py
│ ├── vllm-gaudi
│ ├── ray
│ └── langchain
└── text-summarization/ | ai_ref_knowledge | OPEA Documentation | The proposed code structure of GenAIComps is:
GenAIComps/
└── comps/
└── llms/
├── text-generation/
│ ├── tgi-gaudi/
│ │ ├── dockerfile
│ │ └── llm.py
│ ├── tgi-xeon/
│ │ ├── dockerfile
│ │ └── llm.py
│ ├── vllm-gaudi
│ ├── ray
│ └── langchain
└── text-summarization/ | The proposed code structure of GenAIComps is:
GenAIComps/
└── comps/
└── llms/
├── text-generation/
│ ├── tgi-gaudi/
│ │ ├── dockerfile
│ │ └── llm.py
│ ├── tgi-xeon/
│ │ ├── dockerfile
│ │ └── llm.py
│ ├── vllm-gaudi
│ ├── ray
│ └── langchain
└── text-summarization/ | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
574b634f-4f8e-404c-8d5a-ed1e60f63465 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-24-OPEA-001-Code-Structure.md | unknown | 83e08701-bd14-428c-b4fd-2cb853b9eb00 | 5 | opea-semantic-v1 | 989e1716efcefdad | │ ├── manifests │ └── microservices-connector ├── docker/ │ ├── docker_compose.yaml │ ├── dockerfile │ └── chatqna.py ├── chatqna.yaml # The MegaService Yaml └── README.md
The proposed code structure of GenAIComps is: | ai_ref_knowledge | OPEA Documentation | │ ├── manifests │ └── microservices-connector ├── docker/ │ ├── docker_compose.yaml │ ├── dockerfile │ └── chatqna.py ├── chatqna.yaml # The MegaService Yaml └── README.md
The proposed code structure of GenAIComps is: | │ ├── manifests │ └── microservices-connector ├── docker/ │ ├── docker_compose.yaml │ ├── dockerfile │ └── chatqna.py ├── chatqna.yaml # The MegaService Yaml └── README.md
The proposed code structure of GenAIComps is: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6b53aafc-21fc-4ed0-980f-7393b45e9e17 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-24-OPEA-001-Code-Structure.md | unknown | 83e08701-bd14-428c-b4fd-2cb853b9eb00 | 7 | opea-semantic-v1 | 4af764da226008cb | │ └── llm.py │ ├── tgi-xeon/ │ │ ├── dockerfile │ │ └── llm.py │ ├── vllm-gaudi │ ├── ray │ └── langchain └── text-summarization/
## Miscs | ai_ref_knowledge | OPEA Documentation | │ └── llm.py │ ├── tgi-xeon/ │ │ ├── dockerfile │ │ └── llm.py │ ├── vllm-gaudi │ ├── ray │ └── langchain └── text-summarization/
## Miscs | │ └── llm.py │ ├── tgi-xeon/ │ │ ├── dockerfile │ │ └── llm.py │ ├── vllm-gaudi │ ├── ray │ └── langchain └── text-summarization/
## Miscs | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8abb9af5-cf0f-4cc6-b30c-99402039aeb3 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-24-OPEA-001-Code-Structure.md | unknown | 83e08701-bd14-428c-b4fd-2cb853b9eb00 | 3 | opea-semantic-v1 | 2c9af2a3a947c0b3 | # the folder implementing additional operational capabilities to Kubernetes applications ├── microservices-connector/ # the folder containing the implementation of microservice connector on Kubernetes └── scripts/
The proposed code structure of GenAIExamples is: | ai_ref_knowledge | OPEA Documentation | # the folder implementing additional operational capabilities to Kubernetes applications ├── microservices-connector/ # the folder containing the implementation of microservice connector on Kubernetes └── scripts/
The proposed code structure of GenAIExamples is: | # the folder implementing additional operational capabilities to Kubernetes applications ├── microservices-connector/ # the folder containing the implementation of microservice connector on Kubernetes └── scripts/
The proposed code structure of GenAIExamples is: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a7407121-4d57-4506-ab59-ab2fbb8f2c10 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-24-OPEA-001-Code-Structure.md | unknown | 83e08701-bd14-428c-b4fd-2cb853b9eb00 | 2 | opea-semantic-v1 | c0189d5676031ad2 | The proposed code structure of GenAIInfra is:
GenAIInfra/
├── kubernetes-addon/ # the folder implementing additional operational capabilities to Kubernetes applications
├── microservices-connector/ # the folder containing the implementation of microservice connector on Kubernetes
└── scripts/ | ai_ref_knowledge | OPEA Documentation | The proposed code structure of GenAIInfra is:
GenAIInfra/
├── kubernetes-addon/ # the folder implementing additional operational capabilities to Kubernetes applications
├── microservices-connector/ # the folder containing the implementation of microservice connector on Kubernetes
└── scripts/ | The proposed code structure of GenAIInfra is:
GenAIInfra/
├── kubernetes-addon/ # the folder implementing additional operational capabilities to Kubernetes applications
├── microservices-connector/ # the folder containing the implementation of microservice connector on Kubernetes
└── scripts/ | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c2a7d959-9679-48e1-bbe4-738402b1f51c | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-24-OPEA-001-Code-Structure.md | unknown | 83e08701-bd14-428c-b4fd-2cb853b9eb00 | 0 | opea-semantic-v1 | da70885ac6bd320a | ## Motivation
OPEA project consists of serveral repos, including GenAIExamples, GenAIInfra, GenAICompos, and so on. We need a clear definition on where the new code for a given feature should be put for a consistent and well-orgnized code structure. | ai_ref_knowledge | OPEA Documentation | ## Motivation
OPEA project consists of serveral repos, including GenAIExamples, GenAIInfra, GenAICompos, and so on. We need a clear definition on where the new code for a given feature should be put for a consistent and well-orgnized code structure. | ## Motivation
OPEA project consists of serveral repos, including GenAIExamples, GenAIInfra, GenAICompos, and so on. We need a clear definition on where the new code for a given feature should be put for a consistent and well-orgnized code structure. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c5303471-2c98-40b5-bf0b-8a1f3697e26d | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-05-24-OPEA-001-Code-Structure.md | unknown | 83e08701-bd14-428c-b4fd-2cb853b9eb00 | 4 | opea-semantic-v1 | d88847d646710cb0 | The proposed code structure of GenAIExamples is:
GenAIExamples/
└── ChatQnA/
├── kubernetes/
│ ├── manifests
│ └── microservices-connector
├── docker/
│ ├── docker_compose.yaml
│ ├── dockerfile
│ └── chatqna.py
├── chatqna.yaml # The MegaService Yaml
└── README.md | ai_ref_knowledge | OPEA Documentation | The proposed code structure of GenAIExamples is:
GenAIExamples/
└── ChatQnA/
├── kubernetes/
│ ├── manifests
│ └── microservices-connector
├── docker/
│ ├── docker_compose.yaml
│ ├── dockerfile
│ └── chatqna.py
├── chatqna.yaml # The MegaService Yaml
└── README.md | The proposed code structure of GenAIExamples is:
GenAIExamples/
└── ChatQnA/
├── kubernetes/
│ ├── manifests
│ └── microservices-connector
├── docker/
│ ├── docker_compose.yaml
│ ├── dockerfile
│ └── chatqna.py
├── chatqna.yaml # The MegaService Yaml
└── README.md | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0962e5b4-f978-4e9f-9814-62d586b1c3a7 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-DocSum_Video_Audio.md | unknown | aacc3d23-bc8f-4eb4-8f9f-00d2c2992cbf | 0 | opea-semantic-v1 | 8eb68e27607efdb8 | ### Status Under Review
### Objective
This RFC aims to extend the current Document Summarization Application by incorporating video and audio summary features. This enhancement will enable the application to summarize video and audio content in addition to text documents, thereby broadening its utility and applicabilit... | ai_ref_knowledge | OPEA Documentation | ### Status Under Review
### Objective
This RFC aims to extend the current Document Summarization Application by incorporating video and audio summary features. This enhancement will enable the application to summarize video and audio content in addition to text documents, thereby broadening its utility and applicabilit... | ### Status Under Review
### Objective
This RFC aims to extend the current Document Summarization Application by incorporating video and audio summary features. This enhancement will enable the application to summarize video and audio content in addition to text documents, thereby broadening its utility and applicabilit... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0e2a646d-e5b5-4067-b846-6878ad6b9a5f | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-DocSum_Video_Audio.md | unknown | aacc3d23-bc8f-4eb4-8f9f-00d2c2992cbf | 24 | opea-semantic-v1 | 7066f706c7e194fc | can create summaries of research videos, capturing essential information and visual data. Researchers can share these summaries with their peers, facilitating knowledge sharing and collaboration.
#### 5. Podcast and Audio Content:
**Scenario**: A company produces a series of educational podcasts. Employees need to revi... | ai_ref_knowledge | OPEA Documentation | can create summaries of research videos, capturing essential information and visual data. Researchers can share these summaries with their peers, facilitating knowledge sharing and collaboration.
#### 5. Podcast and Audio Content:
**Scenario**: A company produces a series of educational podcasts. Employees need to revi... | can create summaries of research videos, capturing essential information and visual data. Researchers can share these summaries with their peers, facilitating knowledge sharing and collaboration.
#### 5. Podcast and Audio Content:
**Scenario**: A company produces a series of educational podcasts. Employees need to revi... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1d6d0e69-40aa-4543-89af-1d86442dca96 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-DocSum_Video_Audio.md | unknown | aacc3d23-bc8f-4eb4-8f9f-00d2c2992cbf | 3 | opea-semantic-v1 | b428dc818044417b | it more versatile and useful across different industries. 4. **Competitive Advantage**: Offering video and audio summarization can differentiate the application from other text-only summarization tools.
### Design Proposal | ai_ref_knowledge | OPEA Documentation | it more versatile and useful across different industries. 4. **Competitive Advantage**: Offering video and audio summarization can differentiate the application from other text-only summarization tools.
### Design Proposal | it more versatile and useful across different industries. 4. **Competitive Advantage**: Offering video and audio summarization can differentiate the application from other text-only summarization tools.
### Design Proposal | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
205c834a-7893-4752-872b-f2cbdb798aa1 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-DocSum_Video_Audio.md | unknown | aacc3d23-bc8f-4eb4-8f9f-00d2c2992cbf | 9 | opea-semantic-v1 | 2d5213232d8b9b0a | #### 2. Text Transcription, Video, and Audio Ingestion and Preprocessing: - **Audio Extraction Microservice**: Extract audio from video files for transcription.
Signature of audio extraction microservice:
```python
@traceable(run_type="tool")
@register_statistics(names=["opea_service@audio_extraction"])
def audio_e... | ai_ref_knowledge | OPEA Documentation | #### 2. Text Transcription, Video, and Audio Ingestion and Preprocessing: - **Audio Extraction Microservice**: Extract audio from video files for transcription.
Signature of audio extraction microservice:
```python
@traceable(run_type="tool")
@register_statistics(names=["opea_service@audio_extraction"])
def audio_e... | #### 2. Text Transcription, Video, and Audio Ingestion and Preprocessing: - **Audio Extraction Microservice**: Extract audio from video files for transcription.
Signature of audio extraction microservice:
```python
@traceable(run_type="tool")
@register_statistics(names=["opea_service@audio_extraction"])
def audio_e... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
269e199f-6141-458e-b409-99c29e56552f | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-DocSum_Video_Audio.md | unknown | aacc3d23-bc8f-4eb4-8f9f-00d2c2992cbf | 22 | opea-semantic-v1 | ebba0ec908bb1db7 | videos, highlighting key messages and visual elements. The marketing team can use these summaries to evaluate the impact of their videos and make data-driven decisions.
#### 4. Research and Development:
**Scenario**: Researchers record their experiments and presentations as videos. They need to document and share their... | ai_ref_knowledge | OPEA Documentation | videos, highlighting key messages and visual elements. The marketing team can use these summaries to evaluate the impact of their videos and make data-driven decisions.
#### 4. Research and Development:
**Scenario**: Researchers record their experiments and presentations as videos. They need to document and share their... | videos, highlighting key messages and visual elements. The marketing team can use these summaries to evaluate the impact of their videos and make data-driven decisions.
#### 4. Research and Development:
**Scenario**: Researchers record their experiments and presentations as videos. They need to document and share their... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
2c8ff22c-276f-4487-abe2-e4feb7474c84 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-DocSum_Video_Audio.md | unknown | aacc3d23-bc8f-4eb4-8f9f-00d2c2992cbf | 10 | opea-semantic-v1 | 8bacfb93b1405e30 | Signature of audio extraction microservice: ```python @traceable(run_type="tool") @register_statistics(names=["opea_service@audio_extraction"]) def audio_extraction(input: VideoDoc) -> AudioDoc:
- **Audio-to-Text Transcription**: Use the Audio-Speech-Recognition microservice from OPEA, which aims to generate a transcri... | ai_ref_knowledge | OPEA Documentation | Signature of audio extraction microservice: ```python @traceable(run_type="tool") @register_statistics(names=["opea_service@audio_extraction"]) def audio_extraction(input: VideoDoc) -> AudioDoc:
- **Audio-to-Text Transcription**: Use the Audio-Speech-Recognition microservice from OPEA, which aims to generate a transcri... | Signature of audio extraction microservice: ```python @traceable(run_type="tool") @register_statistics(names=["opea_service@audio_extraction"]) def audio_extraction(input: VideoDoc) -> AudioDoc:
- **Audio-to-Text Transcription**: Use the Audio-Speech-Recognition microservice from OPEA, which aims to generate a transcri... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
2f051aa3-62e2-4d7b-9bd0-b1bc18149e13 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-DocSum_Video_Audio.md | unknown | aacc3d23-bc8f-4eb4-8f9f-00d2c2992cbf | 1 | opea-semantic-v1 | 624662171ea5096f | summary features. This enhancement will enable the application to summarize video and audio content in addition to text documents, thereby broadening its utility and applicability.
### Motivation
The motivation for adding video and audio summary features stems from the increasing prevalence of multimedia content in var... | ai_ref_knowledge | OPEA Documentation | summary features. This enhancement will enable the application to summarize video and audio content in addition to text documents, thereby broadening its utility and applicability.
### Motivation
The motivation for adding video and audio summary features stems from the increasing prevalence of multimedia content in var... | summary features. This enhancement will enable the application to summarize video and audio content in addition to text documents, thereby broadening its utility and applicability.
### Motivation
The motivation for adding video and audio summary features stems from the increasing prevalence of multimedia content in var... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
49790315-3098-4649-8c83-fc61e729f57f | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-DocSum_Video_Audio.md | unknown | aacc3d23-bc8f-4eb4-8f9f-00d2c2992cbf | 15 | opea-semantic-v1 | 1bee23af105683bf | #### 4. Integration and Output: - **Summary Generation**: Combine text, audio, and visual summaries to create comprehensive document summaries from different document formats.
### Use-case Stories | ai_ref_knowledge | OPEA Documentation | #### 4. Integration and Output: - **Summary Generation**: Combine text, audio, and visual summaries to create comprehensive document summaries from different document formats.
### Use-case Stories | #### 4. Integration and Output: - **Summary Generation**: Combine text, audio, and visual summaries to create comprehensive document summaries from different document formats.
### Use-case Stories | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4ba6dfb3-5dac-4cea-b687-a0ea261b597f | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-DocSum_Video_Audio.md | unknown | aacc3d23-bc8f-4eb4-8f9f-00d2c2992cbf | 25 | opea-semantic-v1 | c9feecf4eee6ef3e | Content: **Scenario**: A company produces a series of educational podcasts. Employees need to review these podcasts to stay informed about industry trends and best practices.
**Solution**: The audio summary feature can generate concise summaries of podcast episodes, highlighting key points and important segments. Emplo... | ai_ref_knowledge | OPEA Documentation | Content: **Scenario**: A company produces a series of educational podcasts. Employees need to review these podcasts to stay informed about industry trends and best practices.
**Solution**: The audio summary feature can generate concise summaries of podcast episodes, highlighting key points and important segments. Emplo... | Content: **Scenario**: A company produces a series of educational podcasts. Employees need to review these podcasts to stay informed about industry trends and best practices.
**Solution**: The audio summary feature can generate concise summaries of podcast episodes, highlighting key points and important segments. Emplo... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4e6d0ff6-8fb4-4c6c-aa0e-094593afa5a9 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-DocSum_Video_Audio.md | unknown | aacc3d23-bc8f-4eb4-8f9f-00d2c2992cbf | 11 | opea-semantic-v1 | cb06635469ed73ac | - **Audio-to-Text Transcription**: Use the Audio-Speech-Recognition microservice from OPEA, which aims to generate a transcript for an input audio using an audio-to-text model (Whisper).
Transcript generation microservice:
- opea/whisper:latest
- opea/asr:latest | ai_ref_knowledge | OPEA Documentation | - **Audio-to-Text Transcription**: Use the Audio-Speech-Recognition microservice from OPEA, which aims to generate a transcript for an input audio using an audio-to-text model (Whisper).
Transcript generation microservice:
- opea/whisper:latest
- opea/asr:latest | - **Audio-to-Text Transcription**: Use the Audio-Speech-Recognition microservice from OPEA, which aims to generate a transcript for an input audio using an audio-to-text model (Whisper).
Transcript generation microservice:
- opea/whisper:latest
- opea/asr:latest | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
749ad8ab-4e49-46f4-8da5-aeab3c873763 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-DocSum_Video_Audio.md | unknown | aacc3d23-bc8f-4eb4-8f9f-00d2c2992cbf | 6 | opea-semantic-v1 | a61601c2a83df8ab | .-> E([Microservice : llm-docsum-tgi <br>9000]) -. Post .-> F{{TGI Service<br>8008}} end Megaservice --> |Output| G[Response] end subgraph Legend X([Microservice]) Y{{Service from industry peers}} Z[Gateway] end
The proposed design for the video and audio summary features involves the following components: | ai_ref_knowledge | OPEA Documentation | .-> E([Microservice : llm-docsum-tgi <br>9000]) -. Post .-> F{{TGI Service<br>8008}} end Megaservice --> |Output| G[Response] end subgraph Legend X([Microservice]) Y{{Service from industry peers}} Z[Gateway] end
The proposed design for the video and audio summary features involves the following components: | .-> E([Microservice : llm-docsum-tgi <br>9000]) -. Post .-> F{{TGI Service<br>8008}} end Megaservice --> |Output| G[Response] end subgraph Legend X([Microservice]) Y{{Service from industry peers}} Z[Gateway] end
The proposed design for the video and audio summary features involves the following components: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
83d8b9c7-d083-4baa-a8db-3c5c75a87687 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-DocSum_Video_Audio.md | unknown | aacc3d23-bc8f-4eb4-8f9f-00d2c2992cbf | 18 | opea-semantic-v1 | 13f580621c607362 | of training videos, highlighting key points and important segments. Employees can quickly review the summaries to understand the training content without watching the entire video.
#### 2. Educational Content:
**Scenario**: An online education platform offers video lectures on various subjects. Students often need to r... | ai_ref_knowledge | OPEA Documentation | of training videos, highlighting key points and important segments. Employees can quickly review the summaries to understand the training content without watching the entire video.
#### 2. Educational Content:
**Scenario**: An online education platform offers video lectures on various subjects. Students often need to r... | of training videos, highlighting key points and important segments. Employees can quickly review the summaries to understand the training content without watching the entire video.
#### 2. Educational Content:
**Scenario**: An online education platform offers video lectures on various subjects. Students often need to r... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
92c53901-c18e-4012-996e-8f5dafa94110 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-DocSum_Video_Audio.md | unknown | aacc3d23-bc8f-4eb4-8f9f-00d2c2992cbf | 4 | opea-semantic-v1 | bf9b0d867b871a9c | ### Design Proposal
#### Workflow of the Deployed Document Summarization Service
The workflow of the Document Summarization Service, from the user's input query to the application's output response, is as follows: | ai_ref_knowledge | OPEA Documentation | ### Design Proposal
#### Workflow of the Deployed Document Summarization Service
The workflow of the Document Summarization Service, from the user's input query to the application's output response, is as follows: | ### Design Proposal
#### Workflow of the Deployed Document Summarization Service
The workflow of the Document Summarization Service, from the user's input query to the application's output response, is as follows: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
9497ef5c-0dba-40be-98a3-8e8959c711d8 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-DocSum_Video_Audio.md | unknown | aacc3d23-bc8f-4eb4-8f9f-00d2c2992cbf | 5 | opea-semantic-v1 | cbb0cb7856a8ca75 | the Deployed Document Summarization Service The workflow of the Document Summarization Service, from the user's input query to the application's output response, is as follows:
```mermaid
flowchart LR
subgraph DocSum
direction LR
A[User] <--> |Input query| B[DocSum Gateway]
B <--> |Post| Megaservice
subgraph Megas... | ai_ref_knowledge | OPEA Documentation | the Deployed Document Summarization Service The workflow of the Document Summarization Service, from the user's input query to the application's output response, is as follows:
```mermaid
flowchart LR
subgraph DocSum
direction LR
A[User] <--> |Input query| B[DocSum Gateway]
B <--> |Post| Megaservice
subgraph Megas... | the Deployed Document Summarization Service The workflow of the Document Summarization Service, from the user's input query to the application's output response, is as follows:
```mermaid
flowchart LR
subgraph DocSum
direction LR
A[User] <--> |Input query| B[DocSum Gateway]
B <--> |Post| Megaservice
subgraph Megas... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
96e6ee4c-861d-4247-b23d-f022711209db | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-DocSum_Video_Audio.md | unknown | aacc3d23-bc8f-4eb4-8f9f-00d2c2992cbf | 13 | opea-semantic-v1 | df560eaaa3f8fe07 | - **Text Transcription**: Apply existing text summarization techniques that do not require any data preprocessing.
#### 3. Summarization:
- **Text Summarization**: Apply existing text summarization techniques to the generated transcripts. - **Audio Summarization**: Use audio summarization techniques that extract the Tr... | ai_ref_knowledge | OPEA Documentation | - **Text Transcription**: Apply existing text summarization techniques that do not require any data preprocessing.
#### 3. Summarization:
- **Text Summarization**: Apply existing text summarization techniques to the generated transcripts. - **Audio Summarization**: Use audio summarization techniques that extract the Tr... | - **Text Transcription**: Apply existing text summarization techniques that do not require any data preprocessing.
#### 3. Summarization:
- **Text Summarization**: Apply existing text summarization techniques to the generated transcripts. - **Audio Summarization**: Use audio summarization techniques that extract the Tr... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a32b5aa2-c923-42f3-879a-59d6b30139e2 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-DocSum_Video_Audio.md | unknown | aacc3d23-bc8f-4eb4-8f9f-00d2c2992cbf | 19 | opea-semantic-v1 | 666db2ae5ebc810c | #### 2. Educational Content: **Scenario**: An online education platform offers video lectures on various subjects. Students often need to review these lectures for exams.
**Solution**: The video summary feature can create summaries of video lectures, providing students with a quick overview of the main topicscovered. T... | ai_ref_knowledge | OPEA Documentation | #### 2. Educational Content: **Scenario**: An online education platform offers video lectures on various subjects. Students often need to review these lectures for exams.
**Solution**: The video summary feature can create summaries of video lectures, providing students with a quick overview of the main topicscovered. T... | #### 2. Educational Content: **Scenario**: An online education platform offers video lectures on various subjects. Students often need to review these lectures for exams.
**Solution**: The video summary feature can create summaries of video lectures, providing students with a quick overview of the main topicscovered. T... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a788aa16-2f7f-4f68-81d7-deea47dbd0c7 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-DocSum_Video_Audio.md | unknown | aacc3d23-bc8f-4eb4-8f9f-00d2c2992cbf | 14 | opea-semantic-v1 | d612b62dda849fe4 | steps. - **Visual Summarization**: Use visual summarization techniques that extract the AudioDoc and then use Audio Summarization to create Transcription, then use text summarization steps.
#### 4. Integration and Output:
- **Summary Generation**: Combine text, audio, and visual summaries to create comprehensive docume... | ai_ref_knowledge | OPEA Documentation | steps. - **Visual Summarization**: Use visual summarization techniques that extract the AudioDoc and then use Audio Summarization to create Transcription, then use text summarization steps.
#### 4. Integration and Output:
- **Summary Generation**: Combine text, audio, and visual summaries to create comprehensive docume... | steps. - **Visual Summarization**: Use visual summarization techniques that extract the AudioDoc and then use Audio Summarization to create Transcription, then use text summarization steps.
#### 4. Integration and Output:
- **Summary Generation**: Combine text, audio, and visual summaries to create comprehensive docume... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ab82fa10-eb5e-49e8-832b-f67d26d2cf89 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-DocSum_Video_Audio.md | unknown | aacc3d23-bc8f-4eb4-8f9f-00d2c2992cbf | 17 | opea-semantic-v1 | 4bd1eeb98f8677f1 | 1. Corporate Training: **Scenario**: A company conducts regular training sessions and records them as videos. Employees need to review these training videos to stay updated.
**Solution**: The video summary feature can generate concise summaries of training videos, highlighting key points and important segments. Employe... | ai_ref_knowledge | OPEA Documentation | 1. Corporate Training: **Scenario**: A company conducts regular training sessions and records them as videos. Employees need to review these training videos to stay updated.
**Solution**: The video summary feature can generate concise summaries of training videos, highlighting key points and important segments. Employe... | 1. Corporate Training: **Scenario**: A company conducts regular training sessions and records them as videos. Employees need to review these training videos to stay updated.
**Solution**: The video summary feature can generate concise summaries of training videos, highlighting key points and important segments. Employe... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b10e4d1f-3fa8-41fb-8bbe-c05db84d76d8 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-DocSum_Video_Audio.md | unknown | aacc3d23-bc8f-4eb4-8f9f-00d2c2992cbf | 8 | opea-semantic-v1 | 504f7e03eacdaed2 | #### 1. DocSum Gateway: - **User Interface**: Update the user interface to upload video and audio files in various formats to summarize alongside text.
#### 2. Text Transcription, Video, and Audio Ingestion and Preprocessing:
- **Audio Extraction Microservice**: Extract audio from video files for transcription. | ai_ref_knowledge | OPEA Documentation | #### 1. DocSum Gateway: - **User Interface**: Update the user interface to upload video and audio files in various formats to summarize alongside text.
#### 2. Text Transcription, Video, and Audio Ingestion and Preprocessing:
- **Audio Extraction Microservice**: Extract audio from video files for transcription. | #### 1. DocSum Gateway: - **User Interface**: Update the user interface to upload video and audio files in various formats to summarize alongside text.
#### 2. Text Transcription, Video, and Audio Ingestion and Preprocessing:
- **Audio Extraction Microservice**: Extract audio from video files for transcription. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ba75566b-b2b7-4436-841f-cf10be264eee | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-DocSum_Video_Audio.md | unknown | aacc3d23-bc8f-4eb4-8f9f-00d2c2992cbf | 23 | opea-semantic-v1 | aeff34f0e04cba79 | #### 4. Research and Development: **Scenario**: Researchers record their experiments and presentations as videos. They need to document and share their findings with colleagues.
**Solution**: The video summary feature can create summaries of research videos, capturing essential information and visual data. Researchers ... | ai_ref_knowledge | OPEA Documentation | #### 4. Research and Development: **Scenario**: Researchers record their experiments and presentations as videos. They need to document and share their findings with colleagues.
**Solution**: The video summary feature can create summaries of research videos, capturing essential information and visual data. Researchers ... | #### 4. Research and Development: **Scenario**: Researchers record their experiments and presentations as videos. They need to document and share their findings with colleagues.
**Solution**: The video summary feature can create summaries of research videos, capturing essential information and visual data. Researchers ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
bb61e6b8-6117-47ad-9c25-820a93c3e8e4 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-DocSum_Video_Audio.md | unknown | aacc3d23-bc8f-4eb4-8f9f-00d2c2992cbf | 2 | opea-semantic-v1 | 755997ecbb19badb | be time-consuming to digest in their entirety. By summarizing video and audio content, users can quickly grasp the key points, saving time and improving productivity.
Key motivations include:
1. **Enhanced User Experience**: Users can quickly understand the essence of video and audio content without consuming the entir... | ai_ref_knowledge | OPEA Documentation | be time-consuming to digest in their entirety. By summarizing video and audio content, users can quickly grasp the key points, saving time and improving productivity.
Key motivations include:
1. **Enhanced User Experience**: Users can quickly understand the essence of video and audio content without consuming the entir... | be time-consuming to digest in their entirety. By summarizing video and audio content, users can quickly grasp the key points, saving time and improving productivity.
Key motivations include:
1. **Enhanced User Experience**: Users can quickly understand the essence of video and audio content without consuming the entir... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
bde76383-6ec0-4c01-b4eb-e52196689894 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-DocSum_Video_Audio.md | unknown | aacc3d23-bc8f-4eb4-8f9f-00d2c2992cbf | 16 | opea-semantic-v1 | 5f122ccca24b5103 | ### Use-case Stories
#### 1. Corporate Training:
**Scenario**: A company conducts regular training sessions and records them as videos. Employees need to review these training videos to stay updated. | ai_ref_knowledge | OPEA Documentation | ### Use-case Stories
#### 1. Corporate Training:
**Scenario**: A company conducts regular training sessions and records them as videos. Employees need to review these training videos to stay updated. | ### Use-case Stories
#### 1. Corporate Training:
**Scenario**: A company conducts regular training sessions and records them as videos. Employees need to review these training videos to stay updated. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c3bdce15-4210-40b5-8c76-dfe76063a0e2 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-DocSum_Video_Audio.md | unknown | aacc3d23-bc8f-4eb4-8f9f-00d2c2992cbf | 20 | opea-semantic-v1 | e834b65a30c56aa5 | summaries of video lectures, providing students with a quick overview of the main topicscovered. This helps students to revise efficiently and focus on important concepts.
#### 3. Marketing and Advertising:
**Scenario**: A marketing team produces promotional videos for their products. They need to analyze the effective... | ai_ref_knowledge | OPEA Documentation | summaries of video lectures, providing students with a quick overview of the main topicscovered. This helps students to revise efficiently and focus on important concepts.
#### 3. Marketing and Advertising:
**Scenario**: A marketing team produces promotional videos for their products. They need to analyze the effective... | summaries of video lectures, providing students with a quick overview of the main topicscovered. This helps students to revise efficiently and focus on important concepts.
#### 3. Marketing and Advertising:
**Scenario**: A marketing team produces promotional videos for their products. They need to analyze the effective... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c5429023-7453-4d14-806d-2594dd6504f0 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-DocSum_Video_Audio.md | unknown | aacc3d23-bc8f-4eb4-8f9f-00d2c2992cbf | 7 | opea-semantic-v1 | c21ca0626238039f | The proposed design for the video and audio summary features involves the following components:
#### 1. DocSum Gateway:
- **User Interface**: Update the user interface to upload video and audio files in various formats to summarize alongside text. | ai_ref_knowledge | OPEA Documentation | The proposed design for the video and audio summary features involves the following components:
#### 1. DocSum Gateway:
- **User Interface**: Update the user interface to upload video and audio files in various formats to summarize alongside text. | The proposed design for the video and audio summary features involves the following components:
#### 1. DocSum Gateway:
- **User Interface**: Update the user interface to upload video and audio files in various formats to summarize alongside text. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c8b32907-7063-4fbd-901a-d09c8e514509 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-DocSum_Video_Audio.md | unknown | aacc3d23-bc8f-4eb4-8f9f-00d2c2992cbf | 26 | opea-semantic-v1 | 827c28a38e755226 | podcast episodes, highlighting key points and important segments. Employees can quickly review the summaries to understand the podcast content without listening to the entire episode.
By implementing the video and audio summary features, the Document Summarization Application will become a more powerful and versatile t... | ai_ref_knowledge | OPEA Documentation | podcast episodes, highlighting key points and important segments. Employees can quickly review the summaries to understand the podcast content without listening to the entire episode.
By implementing the video and audio summary features, the Document Summarization Application will become a more powerful and versatile t... | podcast episodes, highlighting key points and important segments. Employees can quickly review the summaries to understand the podcast content without listening to the entire episode.
By implementing the video and audio summary features, the Document Summarization Application will become a more powerful and versatile t... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d5a5f6d1-3a5f-4bd5-822e-0429b4fb9bed | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-DocSum_Video_Audio.md | unknown | aacc3d23-bc8f-4eb4-8f9f-00d2c2992cbf | 12 | opea-semantic-v1 | c2928ba34edabae1 | Transcript generation microservice: - opea/whisper:latest - opea/asr:latest
- **Text Transcription**: Apply existing text summarization techniques that do not require any data preprocessing. | ai_ref_knowledge | OPEA Documentation | Transcript generation microservice: - opea/whisper:latest - opea/asr:latest
- **Text Transcription**: Apply existing text summarization techniques that do not require any data preprocessing. | Transcript generation microservice: - opea/whisper:latest - opea/asr:latest
- **Text Transcription**: Apply existing text summarization techniques that do not require any data preprocessing. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation |
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