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d629b1d8-5209-4de0-a395-75396b56f9b3 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-DocSum_Video_Audio.md | unknown | aacc3d23-bc8f-4eb4-8f9f-00d2c2992cbf | 21 | opea-semantic-v1 | 1e6828778c8c053f | #### 3. Marketing and Advertising: **Scenario**: A marketing team produces promotional videos for their products. They need to analyze the effectiveness of these videos.
**Solution**: The video summary feature can generate summaries of promotional videos, highlighting key messages and visual elements. The marketing tea... | ai_ref_knowledge | OPEA Documentation | #### 3. Marketing and Advertising: **Scenario**: A marketing team produces promotional videos for their products. They need to analyze the effectiveness of these videos.
**Solution**: The video summary feature can generate summaries of promotional videos, highlighting key messages and visual elements. The marketing tea... | #### 3. Marketing and Advertising: **Scenario**: A marketing team produces promotional videos for their products. They need to analyze the effectiveness of these videos.
**Solution**: The video summary feature can generate summaries of promotional videos, highlighting key messages and visual elements. The marketing tea... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
147210d8-4547-4fee-8ad5-6632d92c72cb | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-Guardrails-Gateway.md | unknown | 995502f7-ff6f-4247-9a64-1011944eeb29 | 1 | opea-semantic-v1 | 7d5a357acd9948df | in network transmission and protocol encoding/decoding. - Support stateful guardrails. - Enhance Observability. - Leverage OpenVINO for AI acceleration instructions including AVX, AVX512 and AMX.
## Design Proposal | ai_ref_knowledge | OPEA Documentation | in network transmission and protocol encoding/decoding. - Support stateful guardrails. - Enhance Observability. - Leverage OpenVINO for AI acceleration instructions including AVX, AVX512 and AMX.
## Design Proposal | in network transmission and protocol encoding/decoding. - Support stateful guardrails. - Enhance Observability. - Leverage OpenVINO for AI acceleration instructions including AVX, AVX512 and AMX.
## Design Proposal | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1b97381b-4d21-474b-9869-97e6740578f1 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-Guardrails-Gateway.md | unknown | 995502f7-ff6f-4247-9a64-1011944eeb29 | 18 | opea-semantic-v1 | 71be99bb0cb5db19 | popular proxy in cloud native, which contains out-of-box access log, stats and metrics, and can be integrated into observability platform including OpenTelemetry and Prometheus naturally.
Guardrails in gateway will leverages these abilities about observability to meet potential regulartory and compliance needs. | ai_ref_knowledge | OPEA Documentation | popular proxy in cloud native, which contains out-of-box access log, stats and metrics, and can be integrated into observability platform including OpenTelemetry and Prometheus naturally.
Guardrails in gateway will leverages these abilities about observability to meet potential regulartory and compliance needs. | popular proxy in cloud native, which contains out-of-box access log, stats and metrics, and can be integrated into observability platform including OpenTelemetry and Prometheus naturally.
Guardrails in gateway will leverages these abilities about observability to meet potential regulartory and compliance needs. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
29cc171b-cbeb-40a6-bf79-13e7ecdfdc4b | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-Guardrails-Gateway.md | unknown | 995502f7-ff6f-4247-9a64-1011944eeb29 | 12 | opea-semantic-v1 | 537954f538ac019f | LLM, such as anti-jailbreaking, anti-poisoning for the input side, anti-toxicity, factuality check for the output side, and PII detection for both input and output side.
Guardrails can also be spliited into 2 types, stateless and stateful. Guardrails including anti-jailbreaking, anti-toxicity and PII detection are cons... | ai_ref_knowledge | OPEA Documentation | LLM, such as anti-jailbreaking, anti-poisoning for the input side, anti-toxicity, factuality check for the output side, and PII detection for both input and output side.
Guardrails can also be spliited into 2 types, stateless and stateful. Guardrails including anti-jailbreaking, anti-toxicity and PII detection are cons... | LLM, such as anti-jailbreaking, anti-poisoning for the input side, anti-toxicity, factuality check for the output side, and PII detection for both input and output side.
Guardrails can also be spliited into 2 types, stateless and stateful. Guardrails including anti-jailbreaking, anti-toxicity and PII detection are cons... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
2fb3ca23-dd64-423c-bcdf-38539d86b970 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-Guardrails-Gateway.md | unknown | 995502f7-ff6f-4247-9a64-1011944eeb29 | 8 | opea-semantic-v1 | 205a416f346241ac | The gateway consists of 2 basic components, inference runtime and guardrails.
```mermaid
graph TD
Gateway---Runtime[Inference Runtime API]
Runtime---OpenVINO
Runtime---PyTorch
Runtime---Others[...]
Gateway---Guardrails
Guardrails---Load[Load Model]
Guardrails---Inference
Guardrails---Access[Access Control] | ai_ref_knowledge | OPEA Documentation | The gateway consists of 2 basic components, inference runtime and guardrails.
```mermaid
graph TD
Gateway---Runtime[Inference Runtime API]
Runtime---OpenVINO
Runtime---PyTorch
Runtime---Others[...]
Gateway---Guardrails
Guardrails---Load[Load Model]
Guardrails---Inference
Guardrails---Access[Access Control] | The gateway consists of 2 basic components, inference runtime and guardrails.
```mermaid
graph TD
Gateway---Runtime[Inference Runtime API]
Runtime---OpenVINO
Runtime---PyTorch
Runtime---Others[...]
Gateway---Guardrails
Guardrails---Load[Load Model]
Guardrails---Inference
Guardrails---Access[Access Control] | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
381513f7-37fa-4dc8-97ab-d5833630ba3f | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-Guardrails-Gateway.md | unknown | 995502f7-ff6f-4247-9a64-1011944eeb29 | 11 | opea-semantic-v1 | 45cc75f7575aa407 | ```mermaid flowchart LR Entry(Entry)-->GuardrailsA GuardrailsA["Guardrails\nAnti-Jailbreaking"]-->Embedding Embedding-->Retrieve Retrieve-->Rerank Rerank-->LLM LLM-->GuardrailsB["Guardrails\nAnti-Profanity"]
Guardrails service provides certain protection for LLM, such as anti-jailbreaking, anti-poisoning for the input ... | ai_ref_knowledge | OPEA Documentation | ```mermaid flowchart LR Entry(Entry)-->GuardrailsA GuardrailsA["Guardrails\nAnti-Jailbreaking"]-->Embedding Embedding-->Retrieve Retrieve-->Rerank Rerank-->LLM LLM-->GuardrailsB["Guardrails\nAnti-Profanity"]
Guardrails service provides certain protection for LLM, such as anti-jailbreaking, anti-poisoning for the input ... | ```mermaid flowchart LR Entry(Entry)-->GuardrailsA GuardrailsA["Guardrails\nAnti-Jailbreaking"]-->Embedding Embedding-->Retrieve Retrieve-->Rerank Rerank-->LLM LLM-->GuardrailsB["Guardrails\nAnti-Profanity"]
Guardrails service provides certain protection for LLM, such as anti-jailbreaking, anti-poisoning for the input ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
581cc767-49ba-4e9d-afd9-8056f3b53dde | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-Guardrails-Gateway.md | unknown | 995502f7-ff6f-4247-9a64-1011944eeb29 | 22 | opea-semantic-v1 | 2a5c8ae251271877 | The gateway can also work with guardrails microservices.
```mermaid
graph LR
Entry(Entry)-->GuardrailsC["Guardrails\nAnti-Hallucination"]
GuardrailsC["Guardrails\nAnti-Hallucination"]-->GuardrailsA["Guardrails\nAnti-Jailbreaking"]
GuardrailsA-->Embedding
Embedding-->Retrieve
Retrieve-->Rerank
Rerank-->GuardrailsB... | ai_ref_knowledge | OPEA Documentation | The gateway can also work with guardrails microservices.
```mermaid
graph LR
Entry(Entry)-->GuardrailsC["Guardrails\nAnti-Hallucination"]
GuardrailsC["Guardrails\nAnti-Hallucination"]-->GuardrailsA["Guardrails\nAnti-Jailbreaking"]
GuardrailsA-->Embedding
Embedding-->Retrieve
Retrieve-->Rerank
Rerank-->GuardrailsB... | The gateway can also work with guardrails microservices.
```mermaid
graph LR
Entry(Entry)-->GuardrailsC["Guardrails\nAnti-Hallucination"]
GuardrailsC["Guardrails\nAnti-Hallucination"]-->GuardrailsA["Guardrails\nAnti-Jailbreaking"]
GuardrailsA-->Embedding
Embedding-->Retrieve
Retrieve-->Rerank
Rerank-->GuardrailsB... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6d533bc8-7223-4425-b80a-790bc48ef62d | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-Guardrails-Gateway.md | unknown | 995502f7-ff6f-4247-9a64-1011944eeb29 | 14 | opea-semantic-v1 | 4e5035a67091049f | as microservice, but due to the limitation microservice, it is not able to track requests for responses, leading to difficulty in providing stateless guard ability.
The opt-in guardrails in gateway works in the architecture given below. | ai_ref_knowledge | OPEA Documentation | as microservice, but due to the limitation microservice, it is not able to track requests for responses, leading to difficulty in providing stateless guard ability.
The opt-in guardrails in gateway works in the architecture given below. | as microservice, but due to the limitation microservice, it is not able to track requests for responses, leading to difficulty in providing stateless guard ability.
The opt-in guardrails in gateway works in the architecture given below. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
925537f3-c23a-47a4-9aa9-d69dc4a23357 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-Guardrails-Gateway.md | unknown | 995502f7-ff6f-4247-9a64-1011944eeb29 | 4 | opea-semantic-v1 | 1eea296c09940d07 | shown that each hop adds a 3ms of latency, which can be even longer when mTLS is turned on for security reason in inter-nodes deployment.
The opt-in guardrails in gateway works in the architecture given below. | ai_ref_knowledge | OPEA Documentation | shown that each hop adds a 3ms of latency, which can be even longer when mTLS is turned on for security reason in inter-nodes deployment.
The opt-in guardrails in gateway works in the architecture given below. | shown that each hop adds a 3ms of latency, which can be even longer when mTLS is turned on for security reason in inter-nodes deployment.
The opt-in guardrails in gateway works in the architecture given below. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
9675b78c-d8e0-498d-9ef7-3f0f8a8dbb83 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-Guardrails-Gateway.md | unknown | 995502f7-ff6f-4247-9a64-1011944eeb29 | 5 | opea-semantic-v1 | 1b5128be321cf3fd | The opt-in guardrails in gateway works in the architecture given below.
```mermaid
graph LR
Entry(Entry)-->Gateway["Gateway\nGuardrails"]
Gateway-->Embedding
Embedding-->Gateway
Gateway-->Retrieve
Retrieve-->Gateway
Gateway-->Rerank
Rerank-->Gateway
Gateway-->LLM
LLM-->Gateway | ai_ref_knowledge | OPEA Documentation | The opt-in guardrails in gateway works in the architecture given below.
```mermaid
graph LR
Entry(Entry)-->Gateway["Gateway\nGuardrails"]
Gateway-->Embedding
Embedding-->Gateway
Gateway-->Retrieve
Retrieve-->Gateway
Gateway-->Rerank
Rerank-->Gateway
Gateway-->LLM
LLM-->Gateway | The opt-in guardrails in gateway works in the architecture given below.
```mermaid
graph LR
Entry(Entry)-->Gateway["Gateway\nGuardrails"]
Gateway-->Embedding
Embedding-->Gateway
Gateway-->Retrieve
Retrieve-->Gateway
Gateway-->Rerank
Rerank-->Gateway
Gateway-->LLM
LLM-->Gateway | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b351c14c-e3d3-4a9b-bdf3-117ebc592963 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-Guardrails-Gateway.md | unknown | 995502f7-ff6f-4247-9a64-1011944eeb29 | 9 | opea-semantic-v1 | 98d5025332903061 | ```mermaid graph TD Gateway---Runtime[Inference Runtime API] Runtime---OpenVINO Runtime---PyTorch Runtime---Others[...] Gateway---Guardrails Guardrails---Load[Load Model] Guardrails---Inference Guardrails---Access[Access Control]
A unified inference runtime API provides a general interface for inference runtimes. Any i... | ai_ref_knowledge | OPEA Documentation | ```mermaid graph TD Gateway---Runtime[Inference Runtime API] Runtime---OpenVINO Runtime---PyTorch Runtime---Others[...] Gateway---Guardrails Guardrails---Load[Load Model] Guardrails---Inference Guardrails---Access[Access Control]
A unified inference runtime API provides a general interface for inference runtimes. Any i... | ```mermaid graph TD Gateway---Runtime[Inference Runtime API] Runtime---OpenVINO Runtime---PyTorch Runtime---Others[...] Gateway---Guardrails Guardrails---Load[Load Model] Guardrails---Inference Guardrails---Access[Access Control]
A unified inference runtime API provides a general interface for inference runtimes. Any i... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b617d458-a911-497f-8bd7-023d7dda716b | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-Guardrails-Gateway.md | unknown | 995502f7-ff6f-4247-9a64-1011944eeb29 | 21 | opea-semantic-v1 | 340c9d8f622631f2 | ```mermaid graph LR Entry(Entry)-->Embedding subgraph SidecarA[Sidecar] Embedding end Embedding-->Retrieve Retrieve-->Rerank Rerank-->LLM subgraph SidecarB[Sidecar] LLM end
The gateway can also work with guardrails microservices. | ai_ref_knowledge | OPEA Documentation | ```mermaid graph LR Entry(Entry)-->Embedding subgraph SidecarA[Sidecar] Embedding end Embedding-->Retrieve Retrieve-->Rerank Rerank-->LLM subgraph SidecarB[Sidecar] LLM end
The gateway can also work with guardrails microservices. | ```mermaid graph LR Entry(Entry)-->Embedding subgraph SidecarA[Sidecar] Embedding end Embedding-->Retrieve Retrieve-->Rerank Rerank-->LLM subgraph SidecarB[Sidecar] LLM end
The gateway can also work with guardrails microservices. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
babcf993-4b51-42cb-9e44-fa452ebfc8d0 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-Guardrails-Gateway.md | unknown | 995502f7-ff6f-4247-9a64-1011944eeb29 | 23 | opea-semantic-v1 | 4c6f1320d22302de | - TODO
- [ ] API definitions for meta service deployment and Kubernetes deployment
- [ ] Envoy inference framework and guardrails HTTP filter | ai_ref_knowledge | OPEA Documentation | - TODO
- [ ] API definitions for meta service deployment and Kubernetes deployment
- [ ] Envoy inference framework and guardrails HTTP filter | - TODO
- [ ] API definitions for meta service deployment and Kubernetes deployment
- [ ] Envoy inference framework and guardrails HTTP filter | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c39757b8-2345-4e5c-9568-648a385b294f | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-Guardrails-Gateway.md | unknown | 995502f7-ff6f-4247-9a64-1011944eeb29 | 16 | opea-semantic-v1 | 212a917451371c99 | ```mermaid flowchart LR Entry(Entry)-->GuardrailsA subgraph Gateway GuardrailsA["Guardrails\nAnti-Jailbreaking"]-->GuardrailsC GuardrailsB-->GuardrailsC end GuardrailsC["Guardrails\nAnti-Hallucination"]-->Embedding Embedding-->Retrieve Retrieve-->Rerank Rerank-->LLM LLM-->GuardrailsB["Guardrails\nAnti-Profanity"]
As a ... | ai_ref_knowledge | OPEA Documentation | ```mermaid flowchart LR Entry(Entry)-->GuardrailsA subgraph Gateway GuardrailsA["Guardrails\nAnti-Jailbreaking"]-->GuardrailsC GuardrailsB-->GuardrailsC end GuardrailsC["Guardrails\nAnti-Hallucination"]-->Embedding Embedding-->Retrieve Retrieve-->Rerank Rerank-->LLM LLM-->GuardrailsB["Guardrails\nAnti-Profanity"]
As a ... | ```mermaid flowchart LR Entry(Entry)-->GuardrailsA subgraph Gateway GuardrailsA["Guardrails\nAnti-Jailbreaking"]-->GuardrailsC GuardrailsB-->GuardrailsC end GuardrailsC["Guardrails\nAnti-Hallucination"]-->Embedding Embedding-->Retrieve Retrieve-->Rerank Rerank-->LLM LLM-->GuardrailsB["Guardrails\nAnti-Profanity"]
As a ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c3f1c60a-f857-4759-a348-644f2dc7693b | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-Guardrails-Gateway.md | unknown | 995502f7-ff6f-4247-9a64-1011944eeb29 | 0 | opea-semantic-v1 | ebef576723a31c4e | ## Motivation
- Reduce latency in network transmission and protocol encoding/decoding. - Support stateful guardrails. - Enhance Observability. - Leverage OpenVINO for AI acceleration instructions including AVX, AVX512 and AMX. | ai_ref_knowledge | OPEA Documentation | ## Motivation
- Reduce latency in network transmission and protocol encoding/decoding. - Support stateful guardrails. - Enhance Observability. - Leverage OpenVINO for AI acceleration instructions including AVX, AVX512 and AMX. | ## Motivation
- Reduce latency in network transmission and protocol encoding/decoding. - Support stateful guardrails. - Enhance Observability. - Leverage OpenVINO for AI acceleration instructions including AVX, AVX512 and AMX. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c75e6160-b9b2-4696-8cd7-622186ae24f6 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-Guardrails-Gateway.md | unknown | 995502f7-ff6f-4247-9a64-1011944eeb29 | 19 | opea-semantic-v1 | cc4de33316fcd772 | Let's say the embedding and LLM services are AI-powered and require guardrails protection.
The opt-in gateway can be deployed as a gateway or sidecar services. | ai_ref_knowledge | OPEA Documentation | Let's say the embedding and LLM services are AI-powered and require guardrails protection.
The opt-in gateway can be deployed as a gateway or sidecar services. | Let's say the embedding and LLM services are AI-powered and require guardrails protection.
The opt-in gateway can be deployed as a gateway or sidecar services. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d44dcac1-3bd0-4656-99b4-1ff746178e52 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-Guardrails-Gateway.md | unknown | 995502f7-ff6f-4247-9a64-1011944eeb29 | 13 | opea-semantic-v1 | 44338b629c8d7c24 | on both prompt input and response output, while anti-hallucination is regarded as a stateful guard, it needs both input and ouput for the relativity between.
[Guardrails Microservice](https://github.com/xuechendi/GenAIComps/tree/pii_detection/comps/guardrails) provides certain guardrails as microservice, but due to the... | ai_ref_knowledge | OPEA Documentation | on both prompt input and response output, while anti-hallucination is regarded as a stateful guard, it needs both input and ouput for the relativity between.
[Guardrails Microservice](https://github.com/xuechendi/GenAIComps/tree/pii_detection/comps/guardrails) provides certain guardrails as microservice, but due to the... | on both prompt input and response output, while anti-hallucination is regarded as a stateful guard, it needs both input and ouput for the relativity between.
[Guardrails Microservice](https://github.com/xuechendi/GenAIComps/tree/pii_detection/comps/guardrails) provides certain guardrails as microservice, but due to the... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d49b3bf8-d8c5-4b6a-86b3-a5c1bccfd19b | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-Guardrails-Gateway.md | unknown | 995502f7-ff6f-4247-9a64-1011944eeb29 | 17 | opea-semantic-v1 | 5995eed0f30b618b | ### Observability
Envoy is the most popular proxy in cloud native, which contains out-of-box access log, stats and metrics, and can be integrated into observability platform including OpenTelemetry and Prometheus naturally. | ai_ref_knowledge | OPEA Documentation | ### Observability
Envoy is the most popular proxy in cloud native, which contains out-of-box access log, stats and metrics, and can be integrated into observability platform including OpenTelemetry and Prometheus naturally. | ### Observability
Envoy is the most popular proxy in cloud native, which contains out-of-box access log, stats and metrics, and can be integrated into observability platform including OpenTelemetry and Prometheus naturally. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d7625daa-ed63-4c25-98d2-6ac26dacafb3 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-Guardrails-Gateway.md | unknown | 995502f7-ff6f-4247-9a64-1011944eeb29 | 15 | opea-semantic-v1 | ed8fff019cdf60f7 | The opt-in guardrails in gateway works in the architecture given below.
```mermaid
flowchart LR
Entry(Entry)-->GuardrailsA
subgraph Gateway
GuardrailsA["Guardrails\nAnti-Jailbreaking"]-->GuardrailsC
GuardrailsB-->GuardrailsC
end
GuardrailsC["Guardrails\nAnti-Hallucination"]-->Embedding
Embedding-->Retrieve
Retr... | ai_ref_knowledge | OPEA Documentation | The opt-in guardrails in gateway works in the architecture given below.
```mermaid
flowchart LR
Entry(Entry)-->GuardrailsA
subgraph Gateway
GuardrailsA["Guardrails\nAnti-Jailbreaking"]-->GuardrailsC
GuardrailsB-->GuardrailsC
end
GuardrailsC["Guardrails\nAnti-Hallucination"]-->Embedding
Embedding-->Retrieve
Retr... | The opt-in guardrails in gateway works in the architecture given below.
```mermaid
flowchart LR
Entry(Entry)-->GuardrailsA
subgraph Gateway
GuardrailsA["Guardrails\nAnti-Jailbreaking"]-->GuardrailsC
GuardrailsB-->GuardrailsC
end
GuardrailsC["Guardrails\nAnti-Hallucination"]-->Embedding
Embedding-->Retrieve
Retr... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ddf972ae-f8f6-4da3-bb18-f651e83065b0 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-Guardrails-Gateway.md | unknown | 995502f7-ff6f-4247-9a64-1011944eeb29 | 3 | opea-semantic-v1 | c30384de3d8a161e | ```mermaid graph LR Entry(Entry)-->Gateway Gateway-->Embedding Embedding-->Gateway Gateway-->Retrieve Retrieve-->Gateway Gateway-->Rerank Rerank-->Gateway Gateway-->LLM LLM-->Guardrails Guardrails-->LLM LLM-->Gateway
All services use RESTful API calling to communicate. There is overhead in network transmission and prot... | ai_ref_knowledge | OPEA Documentation | ```mermaid graph LR Entry(Entry)-->Gateway Gateway-->Embedding Embedding-->Gateway Gateway-->Retrieve Retrieve-->Gateway Gateway-->Rerank Rerank-->Gateway Gateway-->LLM LLM-->Guardrails Guardrails-->LLM LLM-->Gateway
All services use RESTful API calling to communicate. There is overhead in network transmission and prot... | ```mermaid graph LR Entry(Entry)-->Gateway Gateway-->Embedding Embedding-->Gateway Gateway-->Retrieve Retrieve-->Gateway Gateway-->Rerank Rerank-->Gateway Gateway-->LLM LLM-->Guardrails Guardrails-->LLM LLM-->Gateway
All services use RESTful API calling to communicate. There is overhead in network transmission and prot... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e2c81e1e-5332-4994-884a-67cc8f60c3dd | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-Guardrails-Gateway.md | unknown | 995502f7-ff6f-4247-9a64-1011944eeb29 | 7 | opea-semantic-v1 | c68e505518d01975 | the real world deployment, there may be many guardrails in all perspectives, and the gateway is the best place to provide guardrails for the system.
The gateway consists of 2 basic components, inference runtime and guardrails. | ai_ref_knowledge | OPEA Documentation | the real world deployment, there may be many guardrails in all perspectives, and the gateway is the best place to provide guardrails for the system.
The gateway consists of 2 basic components, inference runtime and guardrails. | the real world deployment, there may be many guardrails in all perspectives, and the gateway is the best place to provide guardrails for the system.
The gateway consists of 2 basic components, inference runtime and guardrails. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e5abde32-115b-40f9-97c5-c433dbeb759d | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-Guardrails-Gateway.md | unknown | 995502f7-ff6f-4247-9a64-1011944eeb29 | 2 | opea-semantic-v1 | 0ef1efdeefd19bc2 | The LangChain-like workflow is presented below.
```mermaid
graph LR
Entry(Entry)-->Gateway
Gateway-->Embedding
Embedding-->Gateway
Gateway-->Retrieve
Retrieve-->Gateway
Gateway-->Rerank
Rerank-->Gateway
Gateway-->LLM
LLM-->Guardrails
Guardrails-->LLM
LLM-->Gateway | ai_ref_knowledge | OPEA Documentation | The LangChain-like workflow is presented below.
```mermaid
graph LR
Entry(Entry)-->Gateway
Gateway-->Embedding
Embedding-->Gateway
Gateway-->Retrieve
Retrieve-->Gateway
Gateway-->Rerank
Rerank-->Gateway
Gateway-->LLM
LLM-->Guardrails
Guardrails-->LLM
LLM-->Gateway | The LangChain-like workflow is presented below.
```mermaid
graph LR
Entry(Entry)-->Gateway
Gateway-->Embedding
Embedding-->Gateway
Gateway-->Retrieve
Retrieve-->Gateway
Gateway-->Rerank
Rerank-->Gateway
Gateway-->LLM
LLM-->Guardrails
Guardrails-->LLM
LLM-->Gateway | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
eedbac7e-7cd8-4d75-a90f-8ef1f2abaf98 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-Guardrails-Gateway.md | unknown | 995502f7-ff6f-4247-9a64-1011944eeb29 | 20 | opea-semantic-v1 | ffb7a98121a61c36 | The opt-in gateway can be deployed as a gateway or sidecar services.
```mermaid
graph LR
Entry(Entry)-->Embedding
subgraph SidecarA[Sidecar]
Embedding
end
Embedding-->Retrieve
Retrieve-->Rerank
Rerank-->LLM
subgraph SidecarB[Sidecar]
LLM
end | ai_ref_knowledge | OPEA Documentation | The opt-in gateway can be deployed as a gateway or sidecar services.
```mermaid
graph LR
Entry(Entry)-->Embedding
subgraph SidecarA[Sidecar]
Embedding
end
Embedding-->Retrieve
Retrieve-->Rerank
Rerank-->LLM
subgraph SidecarB[Sidecar]
LLM
end | The opt-in gateway can be deployed as a gateway or sidecar services.
```mermaid
graph LR
Entry(Entry)-->Embedding
subgraph SidecarA[Sidecar]
Embedding
end
Embedding-->Retrieve
Retrieve-->Rerank
Rerank-->LLM
subgraph SidecarB[Sidecar]
LLM
end | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
fcd8c6b0-fd67-44a7-9a99-800538cde519 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-Guardrails-Gateway.md | unknown | 995502f7-ff6f-4247-9a64-1011944eeb29 | 6 | opea-semantic-v1 | 4d1fb60fdd9d29e6 | ```mermaid graph LR Entry(Entry)-->Gateway["Gateway\nGuardrails"] Gateway-->Embedding Embedding-->Gateway Gateway-->Retrieve Retrieve-->Gateway Gateway-->Rerank Rerank-->Gateway Gateway-->LLM LLM-->Gateway
The gateway can host multiple guardrails without extra network transmission or protocol encoding/decoding. In the ... | ai_ref_knowledge | OPEA Documentation | ```mermaid graph LR Entry(Entry)-->Gateway["Gateway\nGuardrails"] Gateway-->Embedding Embedding-->Gateway Gateway-->Retrieve Retrieve-->Gateway Gateway-->Rerank Rerank-->Gateway Gateway-->LLM LLM-->Gateway
The gateway can host multiple guardrails without extra network transmission or protocol encoding/decoding. In the ... | ```mermaid graph LR Entry(Entry)-->Gateway["Gateway\nGuardrails"] Gateway-->Embedding Embedding-->Gateway Gateway-->Retrieve Retrieve-->Gateway Gateway-->Rerank Rerank-->Gateway Gateway-->LLM LLM-->Gateway
The gateway can host multiple guardrails without extra network transmission or protocol encoding/decoding. In the ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
fd3d52f2-c4dc-4731-b712-d36b61835028 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-06-21-OPEA-001-Guardrails-Gateway.md | unknown | 995502f7-ff6f-4247-9a64-1011944eeb29 | 10 | opea-semantic-v1 | ac2c52e4ca629f08 | runtimes. Any inference runtime can be integrated into the system including OpenVINO. The guardrails leverages the inferece runtime and decides if the request/reponse is valid.
### Stateful Guardrails | ai_ref_knowledge | OPEA Documentation | runtimes. Any inference runtime can be integrated into the system including OpenVINO. The guardrails leverages the inferece runtime and decides if the request/reponse is valid.
### Stateful Guardrails | runtimes. Any inference runtime can be integrated into the system including OpenVINO. The guardrails leverages the inferece runtime and decides if the request/reponse is valid.
### Stateful Guardrails | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
040ae719-686d-4b19-bf4b-750021dfa625 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 1 | opea-semantic-v1 | e0d7fb8e6b6e952a | system that can understand and process information, evaluate situations, take appropriate actions, communicate responses, and track ongoing situations, finally output with result meeting defined goals.
Single Agent Example: | ai_ref_knowledge | OPEA Documentation | system that can understand and process information, evaluate situations, take appropriate actions, communicate responses, and track ongoing situations, finally output with result meeting defined goals.
Single Agent Example: | system that can understand and process information, evaluate situations, take appropriate actions, communicate responses, and track ongoing situations, finally output with result meeting defined goals.
Single Agent Example: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
05fbe0b9-48a0-4280-8157-ec50fe1e2082 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 33 | opea-semantic-v1 | f927cafbde477b10 | strategy: choices([react, planexec, humanInLoopPlanExec]) require_human_feedback: bool llm_endpoint_url: str llm_engine: choices([tgi, vllm, openai]) llm_model_id: str recursion_limit: int require_human_feedback: bool
#### Agent Role microservice definition - 'Executor':
Tools executors. Executor is used to process in... | ai_ref_knowledge | OPEA Documentation | strategy: choices([react, planexec, humanInLoopPlanExec]) require_human_feedback: bool llm_endpoint_url: str llm_engine: choices([tgi, vllm, openai]) llm_model_id: str recursion_limit: int require_human_feedback: bool
#### Agent Role microservice definition - 'Executor':
Tools executors. Executor is used to process in... | strategy: choices([react, planexec, humanInLoopPlanExec]) require_human_feedback: bool llm_endpoint_url: str llm_engine: choices([tgi, vllm, openai]) llm_model_id: str recursion_limit: int require_human_feedback: bool
#### Agent Role microservice definition - 'Executor':
Tools executors. Executor is used to process in... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0b313a19-bdcd-42c2-866f-119352d5cd60 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 10 | opea-semantic-v1 | 3bb28b99f70aeeeb | works and rules of using this microservice. Devops are expected to follow customer tool template to provide their own tools and register to Agent microservice.
* End user: End user describe who writes application which will use OPEA exposed endpoints and API to fulfill task goals. End users are expected to use this RFC... | ai_ref_knowledge | OPEA Documentation | works and rules of using this microservice. Devops are expected to follow customer tool template to provide their own tools and register to Agent microservice.
* End user: End user describe who writes application which will use OPEA exposed endpoints and API to fulfill task goals. End users are expected to use this RFC... | works and rules of using this microservice. Devops are expected to follow customer tool template to provide their own tools and register to Agent microservice.
* End user: End user describe who writes application which will use OPEA exposed endpoints and API to fulfill task goals. End users are expected to use this RFC... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0c8cfbda-2064-46eb-8a72-babee083a7c0 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 18 | opea-semantic-v1 | f254fcc538aafa0d | 2. openAI assistant API > Reference: https://platform.openai.com/docs/api-reference/assistants
Advantage and limitation:
* User can create a session thread memorizing previous conversation as long-term memory. And Human-In-Loop agent will only works use this API. * User client application may need codes change to work... | ai_ref_knowledge | OPEA Documentation | 2. openAI assistant API > Reference: https://platform.openai.com/docs/api-reference/assistants
Advantage and limitation:
* User can create a session thread memorizing previous conversation as long-term memory. And Human-In-Loop agent will only works use this API. * User client application may need codes change to work... | 2. openAI assistant API > Reference: https://platform.openai.com/docs/api-reference/assistants
Advantage and limitation:
* User can create a session thread memorizing previous conversation as long-term memory. And Human-In-Loop agent will only works use this API. * User client application may need codes change to work... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
121cfe1c-e298-4fff-8586-4e05b812a55c | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 6 | opea-semantic-v1 | c748be6b6dbffc77 | ## Motivation
This RFC aims to provide low-code / no-code agents as new microservice / megaservice for Enterprise users who are looking for using their own tools with LLM. Tools includes domain_specific_search, knowledgebase_retrieval, enterprise_servic_api_authorization_required, proprietary_tools, etc. | ai_ref_knowledge | OPEA Documentation | ## Motivation
This RFC aims to provide low-code / no-code agents as new microservice / megaservice for Enterprise users who are looking for using their own tools with LLM. Tools includes domain_specific_search, knowledgebase_retrieval, enterprise_servic_api_authorization_required, proprietary_tools, etc. | ## Motivation
This RFC aims to provide low-code / no-code agents as new microservice / megaservice for Enterprise users who are looking for using their own tools with LLM. Tools includes domain_specific_search, knowledgebase_retrieval, enterprise_servic_api_authorization_required, proprietary_tools, etc. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
229882aa-5f4a-47ff-af18-366b52616705 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 41 | opea-semantic-v1 | 13a544e638c4f181 | presented in this configuration, 1st layer supervisor agent is the gateway to interact with user, and 1st layer agent will manage 2nd layer worker agents.
 | ai_ref_knowledge | OPEA Documentation | presented in this configuration, 1st layer supervisor agent is the gateway to interact with user, and 1st layer agent will manage 2nd layer worker agents.
 | presented in this configuration, 1st layer supervisor agent is the gateway to interact with user, and 1st layer agent will manage 2nd layer worker agents.
 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
3229005c-e45f-41e8-afaa-09509ac58125 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 42 | opea-semantic-v1 | 6f8358cc0036a091 | User can also chain agent into a multi-step mega service. audioAgent_megaservice.yaml 
#### Part3.2 Graph-Based Multi Agent
In Phase II, we propose to provide a graph-based multi agents system, which enterprise user will be able to... | ai_ref_knowledge | OPEA Documentation | User can also chain agent into a multi-step mega service. audioAgent_megaservice.yaml 
#### Part3.2 Graph-Based Multi Agent
In Phase II, we propose to provide a graph-based multi agents system, which enterprise user will be able to... | User can also chain agent into a multi-step mega service. audioAgent_megaservice.yaml 
#### Part3.2 Graph-Based Multi Agent
In Phase II, we propose to provide a graph-based multi agents system, which enterprise user will be able to... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
38579d7e-9373-47af-b98f-2e0a563e7419 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 45 | opea-semantic-v1 | 8c865d81be310086 | The user can build and launch the graph-based message group by the combination of docker image and yaml file: 
The yaml file contains the basic config information for each single “Role” in the agent architecture. The user can build... | ai_ref_knowledge | OPEA Documentation | The user can build and launch the graph-based message group by the combination of docker image and yaml file: 
The yaml file contains the basic config information for each single “Role” in the agent architecture. The user can build... | The user can build and launch the graph-based message group by the combination of docker image and yaml file: 
The yaml file contains the basic config information for each single “Role” in the agent architecture. The user can build... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
3a10df43-83c5-48c0-8fd0-08157403c49a | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 11 | opea-semantic-v1 | 83d1abda24311e7b | will use OPEA exposed endpoints and API to fulfill task goals. End users are expected to use this RFC to understand API keywords and rules.
## Design Proposal | ai_ref_knowledge | OPEA Documentation | will use OPEA exposed endpoints and API to fulfill task goals. End users are expected to use this RFC to understand API keywords and rules.
## Design Proposal | will use OPEA exposed endpoints and API to fulfill task goals. End users are expected to use this RFC to understand API keywords and rules.
## Design Proposal | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
3bc74ff6-7016-4791-bde5-82166bcc9987 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 43 | opea-semantic-v1 | 7e4299103c44569c | system, which enterprise user will be able to define edges and conditional edges between agent nodes, planner nodes and tools for complex task agent design.
 | ai_ref_knowledge | OPEA Documentation | system, which enterprise user will be able to define edges and conditional edges between agent nodes, planner nodes and tools for complex task agent design.
 | system, which enterprise user will be able to define edges and conditional edges between agent nodes, planner nodes and tools for complex task agent design.
 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4353dbec-dfb8-4d7b-ba34-f9ef0d18b817 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 20 | opea-semantic-v1 | 03ed139bd906cb43 | used to create agent runtime instance with a set of tool / append addition instructions - "/v1/assistants": { "instructions": str, "name": str, "tools": list }
# threads API is to used maintain conversation session with one user. It can be resumed from previous, can tracking long term memories. - "/v1/threads/ ": { # e... | ai_ref_knowledge | OPEA Documentation | used to create agent runtime instance with a set of tool / append addition instructions - "/v1/assistants": { "instructions": str, "name": str, "tools": list }
# threads API is to used maintain conversation session with one user. It can be resumed from previous, can tracking long term memories. - "/v1/threads/ ": { # e... | used to create agent runtime instance with a set of tool / append addition instructions - "/v1/assistants": { "instructions": str, "name": str, "tools": list }
# threads API is to used maintain conversation session with one user. It can be resumed from previous, can tracking long term memories. - "/v1/threads/ ": { # e... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
48558649-25fc-4d44-8d44-0f084d76b67c | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 19 | opea-semantic-v1 | 42907b45776ada61 | API. * User client application may need codes change to work with this new API. * openAI assistant API is tagged with ‘beta’, not stable
# assistants API is used to create agent runtime instance with a set of tool / append addition instructions
- "/v1/assistants": {
"instructions": str,
"name": str,
"tools": list
... | ai_ref_knowledge | OPEA Documentation | API. * User client application may need codes change to work with this new API. * openAI assistant API is tagged with ‘beta’, not stable
# assistants API is used to create agent runtime instance with a set of tool / append addition instructions
- "/v1/assistants": {
"instructions": str,
"name": str,
"tools": list
... | API. * User client application may need codes change to work with this new API. * openAI assistant API is tagged with ‘beta’, not stable
# assistants API is used to create agent runtime instance with a set of tool / append addition instructions
- "/v1/assistants": {
"instructions": str,
"name": str,
"tools": list
... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
52eda620-3901-41ac-8ae5-350bb1af22d1 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 32 | opea-semantic-v1 | 63fc53b4ad1d1a2f | #### Agent Role microservice definition - 'Planner': Agent without tools. Planner only contains LLM endpoints as planner, certain strategies to complete an optimized plan.
configuration: | ai_ref_knowledge | OPEA Documentation | #### Agent Role microservice definition - 'Planner': Agent without tools. Planner only contains LLM endpoints as planner, certain strategies to complete an optimized plan.
configuration: | #### Agent Role microservice definition - 'Planner': Agent without tools. Planner only contains LLM endpoints as planner, certain strategies to complete an optimized plan.
configuration: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5592700f-c91b-45f6-aa5d-0338fbfb9692 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 0 | opea-semantic-v1 | 39dbde4f384fe4dd | This RFC introduces a new concept of an "Hierarchical Agent," which includes two parts.
* 'Agent’: Agent refers to a framework that integrates the reasoning capabilities of large language models (LLMs) with the ability to take actionable steps, creating a more sophisticated system that can understand and process inform... | ai_ref_knowledge | OPEA Documentation | This RFC introduces a new concept of an "Hierarchical Agent," which includes two parts.
* 'Agent’: Agent refers to a framework that integrates the reasoning capabilities of large language models (LLMs) with the ability to take actionable steps, creating a more sophisticated system that can understand and process inform... | This RFC introduces a new concept of an "Hierarchical Agent," which includes two parts.
* 'Agent’: Agent refers to a framework that integrates the reasoning capabilities of large language models (LLMs) with the ability to take actionable steps, creating a more sophisticated system that can understand and process inform... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5b1af2cd-bb11-4f24-9560-b9918e67f019 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 47 | opea-semantic-v1 | bda95d87c6b68f16 | Appending agents/roles in MessageGroup. Define the role class define the action of the role add edges recompile the messagegroup 
#### Part 4. Agent Debug System | ai_ref_knowledge | OPEA Documentation | Appending agents/roles in MessageGroup. Define the role class define the action of the role add edges recompile the messagegroup 
#### Part 4. Agent Debug System | Appending agents/roles in MessageGroup. Define the role class define the action of the role add edges recompile the messagegroup 
#### Part 4. Agent Debug System | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5bbe8585-1502-4e0c-a39c-0b40d4f4ab43 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 22 | opea-semantic-v1 | 4925641a7474b400 | threads messages API is to add a task content to thread_1 (the thread created by threads API) - "/v1/threads/thread_1/messages": { "role": str, "content": str }
# threads run API is to start to execute agent thread using run api | ai_ref_knowledge | OPEA Documentation | threads messages API is to add a task content to thread_1 (the thread created by threads API) - "/v1/threads/thread_1/messages": { "role": str, "content": str }
# threads run API is to start to execute agent thread using run api | threads messages API is to add a task content to thread_1 (the thread created by threads API) - "/v1/threads/thread_1/messages": { "role": str, "content": str }
# threads run API is to start to execute agent thread using run api | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5f36c98d-b603-429a-85df-5212e34a174c | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 46 | opea-semantic-v1 | 02717a2443d6049a | among the candidate tail_nodes based on the output of the head_node. The logic of this selection part is defined by the state component “Should_Continue”. 
Appending agents/roles in MessageGroup. Define the role class define the a... | ai_ref_knowledge | OPEA Documentation | among the candidate tail_nodes based on the output of the head_node. The logic of this selection part is defined by the state component “Should_Continue”. 
Appending agents/roles in MessageGroup. Define the role class define the a... | among the candidate tail_nodes based on the output of the head_node. The logic of this selection part is defined by the state component “Should_Continue”. 
Appending agents/roles in MessageGroup. Define the role class define the a... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6571baca-ed26-4af0-9557-1ba304f71a96 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 7 | opea-semantic-v1 | 6c579c9060e69472 | as new microservice / megaservice for Enterprise users who are looking for using their own tools with LLM. Tools includes domain_specific_search, knowledgebase_retrieval, enterprise_servic_api_authorization_required, proprietary_tools, etc.
## Persona | ai_ref_knowledge | OPEA Documentation | as new microservice / megaservice for Enterprise users who are looking for using their own tools with LLM. Tools includes domain_specific_search, knowledgebase_retrieval, enterprise_servic_api_authorization_required, proprietary_tools, etc.
## Persona | as new microservice / megaservice for Enterprise users who are looking for using their own tools with LLM. Tools includes domain_specific_search, knowledgebase_retrieval, enterprise_servic_api_authorization_required, proprietary_tools, etc.
## Persona | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6e9cdb08-5868-4828-90c3-634ec1a6bfc5 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 36 | opea-semantic-v1 | 382fb4c36795be2d | We planned to provide multi-agent system in two phases.
* Phase I: Hierarchical Multi Agents
1. In this design, only top-layer Agent will be exposed to OPEA mega flow. And only ‘Agent’ microservice will be used to compose Hierarchical Multi Agents system. 2. Users are only allowed to use yaml files to provide tools co... | ai_ref_knowledge | OPEA Documentation | We planned to provide multi-agent system in two phases.
* Phase I: Hierarchical Multi Agents
1. In this design, only top-layer Agent will be exposed to OPEA mega flow. And only ‘Agent’ microservice will be used to compose Hierarchical Multi Agents system. 2. Users are only allowed to use yaml files to provide tools co... | We planned to provide multi-agent system in two phases.
* Phase I: Hierarchical Multi Agents
1. In this design, only top-layer Agent will be exposed to OPEA mega flow. And only ‘Agent’ microservice will be used to compose Hierarchical Multi Agents system. 2. Users are only allowed to use yaml files to provide tools co... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6f97075d-2eb9-4158-910a-08f720ee07de | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 35 | opea-semantic-v1 | c2edf421c41aa5f2 | [tool_name]: description: str callable_api: choices([http://xxxx, xxx.py:func_name]) env: str pip_dependencies: str # sep by , args_schema: query: type: choices([int, str, bool]) description: str return_output: str
> Any microservcice follow this spec can be registered as role in Part3-graph-based | ai_ref_knowledge | OPEA Documentation | [tool_name]: description: str callable_api: choices([http://xxxx, xxx.py:func_name]) env: str pip_dependencies: str # sep by , args_schema: query: type: choices([int, str, bool]) description: str return_output: str
> Any microservcice follow this spec can be registered as role in Part3-graph-based | [tool_name]: description: str callable_api: choices([http://xxxx, xxx.py:func_name]) env: str pip_dependencies: str # sep by , args_schema: query: type: choices([int, str, bool]) description: str return_output: str
> Any microservcice follow this spec can be registered as role in Part3-graph-based | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
70c9ca5d-4f4e-425a-b6ec-1f920ebed0e4 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 9 | opea-semantic-v1 | b6e21ead8f7f484f | microservices and chained in megaflow. OPEA developer develops OPEA agent codes and add new Agent Implementation by extending current Agent library with advanced agent strategies.
* Enterprise User (Devops): Devops describe who will follow OPEA yaml configuration format to update settings according to their real need, ... | ai_ref_knowledge | OPEA Documentation | microservices and chained in megaflow. OPEA developer develops OPEA agent codes and add new Agent Implementation by extending current Agent library with advanced agent strategies.
* Enterprise User (Devops): Devops describe who will follow OPEA yaml configuration format to update settings according to their real need, ... | microservices and chained in megaflow. OPEA developer develops OPEA agent codes and add new Agent Implementation by extending current Agent library with advanced agent strategies.
* Enterprise User (Devops): Devops describe who will follow OPEA yaml configuration format to update settings according to their real need, ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
79df6a62-3a74-401a-ad53-873ed782ec2b | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 4 | opea-semantic-v1 | 6661aa539d455ec8 | Multi Agent example:
curl ${ip_addr}:${SUPERVISOR_AGENT_PORT}/v1/chat/completions -X POST \
-d "{'input': 'Generate a Analyst Stock Recommendations by taking an average of all analyst recommendations and classifying them as Strong Buy, Buy, Hold, Underperform or Sell.'}" | ai_ref_knowledge | OPEA Documentation | Multi Agent example:
curl ${ip_addr}:${SUPERVISOR_AGENT_PORT}/v1/chat/completions -X POST \
-d "{'input': 'Generate a Analyst Stock Recommendations by taking an average of all analyst recommendations and classifying them as Strong Buy, Buy, Hold, Underperform or Sell.'}" | Multi Agent example:
curl ${ip_addr}:${SUPERVISOR_AGENT_PORT}/v1/chat/completions -X POST \
-d "{'input': 'Generate a Analyst Stock Recommendations by taking an average of all analyst recommendations and classifying them as Strong Buy, Buy, Hold, Underperform or Sell.'}" | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7eaad9c2-89ac-4a40-94de-7d2e39aa1824 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 40 | opea-semantic-v1 | 0dc98aae4168407f |  
__Example 2__: ‘Hierarchical Multi Agents’
3 agents are presented in this configuration, 1st layer supervisor agent is the gateway to intera... | ai_ref_knowledge | OPEA Documentation |  
__Example 2__: ‘Hierarchical Multi Agents’
3 agents are presented in this configuration, 1st layer supervisor agent is the gateway to intera... |  
__Example 2__: ‘Hierarchical Multi Agents’
3 agents are presented in this configuration, 1st layer supervisor agent is the gateway to intera... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8177af56-7d9d-4d71-9a77-f830a15833d5 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 15 | opea-semantic-v1 | 6709510117faaed6 | ### Part 1. API SPEC
Provide two types of API for different client application. 1. openAI chat completion API. > Reference: https://platform.openai.com/docs/api-reference/chat/create | ai_ref_knowledge | OPEA Documentation | ### Part 1. API SPEC
Provide two types of API for different client application. 1. openAI chat completion API. > Reference: https://platform.openai.com/docs/api-reference/chat/create | ### Part 1. API SPEC
Provide two types of API for different client application. 1. openAI chat completion API. > Reference: https://platform.openai.com/docs/api-reference/chat/create | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
82bab454-c45b-4775-8c6a-de6b3e362ffa | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 38 | opea-semantic-v1 | 67e8b1f23d4732ea | and also provide flexibility to handle resource management when certain tools are running way slower than others. > Detailed configuration please refer to Part3.2 
#### Part3.1 Hierarchical Multi Agents | ai_ref_knowledge | OPEA Documentation | and also provide flexibility to handle resource management when certain tools are running way slower than others. > Detailed configuration please refer to Part3.2 
#### Part3.1 Hierarchical Multi Agents | and also provide flexibility to handle resource management when certain tools are running way slower than others. > Detailed configuration please refer to Part3.2 
#### Part3.1 Hierarchical Multi Agents | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
86701597-62e2-4077-a1c9-0b0e5762d5e8 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 26 | opea-semantic-v1 | ae6e7a51f90610e2 | #### SPEC for any agent Role - agent, planner, executor
"/v1/chat/completions": {
"model": str,
"messages": list,
"tools": list,
}
"/v1/assistants": {
"instructions": str,
"name": str,
"tools": list
}
"/v1/threads/: {}
"/v1/threads/thread_1/runs": {
'assistant_id': str,
'instructions': str,
}
"/v1/thread... | ai_ref_knowledge | OPEA Documentation | #### SPEC for any agent Role - agent, planner, executor
"/v1/chat/completions": {
"model": str,
"messages": list,
"tools": list,
}
"/v1/assistants": {
"instructions": str,
"name": str,
"tools": list
}
"/v1/threads/: {}
"/v1/threads/thread_1/runs": {
'assistant_id': str,
'instructions': str,
}
"/v1/thread... | #### SPEC for any agent Role - agent, planner, executor
"/v1/chat/completions": {
"model": str,
"messages": list,
"tools": list,
}
"/v1/assistants": {
"instructions": str,
"name": str,
"tools": list
}
"/v1/threads/: {}
"/v1/threads/thread_1/runs": {
'assistant_id': str,
'instructions': str,
}
"/v1/thread... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
86add590-7f2c-4507-aabc-4986cb02713a | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 13 | opea-semantic-v1 | 978b84acec2f1b7e | chat-completion API * Agent example - Insight Assistant v0.1 (IT demo) * hierarchical multi agents * includes: research(rag, data_crawler); writer(format); reviewer(rule) * Agent debug system
V0.9
* Agent component v0.1
* Support assistants API
* K8s helm chart
* Agent Example - Insight Assistant v0.1
* Shared demo ... | ai_ref_knowledge | OPEA Documentation | chat-completion API * Agent example - Insight Assistant v0.1 (IT demo) * hierarchical multi agents * includes: research(rag, data_crawler); writer(format); reviewer(rule) * Agent debug system
V0.9
* Agent component v0.1
* Support assistants API
* K8s helm chart
* Agent Example - Insight Assistant v0.1
* Shared demo ... | chat-completion API * Agent example - Insight Assistant v0.1 (IT demo) * hierarchical multi agents * includes: research(rag, data_crawler); writer(format); reviewer(rule) * Agent debug system
V0.9
* Agent component v0.1
* Support assistants API
* K8s helm chart
* Agent Example - Insight Assistant v0.1
* Shared demo ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8868e98a-439e-4433-902f-f103291262f2 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 8 | opea-semantic-v1 | 327666e5b2cdf77f | We use the listed terms to define different persona mentioned in this document.
* OPEA developer: OPEA developers describe who will follow current OPEA API SPEC or expand OPEA API SPEC to add new solutions. OPEA developers are expected to use this RFC to understand how this microservice communicates with other microser... | ai_ref_knowledge | OPEA Documentation | We use the listed terms to define different persona mentioned in this document.
* OPEA developer: OPEA developers describe who will follow current OPEA API SPEC or expand OPEA API SPEC to add new solutions. OPEA developers are expected to use this RFC to understand how this microservice communicates with other microser... | We use the listed terms to define different persona mentioned in this document.
* OPEA developer: OPEA developers describe who will follow current OPEA API SPEC or expand OPEA API SPEC to add new solutions. OPEA developers are expected to use this RFC to understand how this microservice communicates with other microser... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8bb79917-f43a-492d-b42b-8fd76d9a4871 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 23 | opea-semantic-v1 | 16a8bcfd001b2d7c | # threads run API is to start to execute agent thread using run api
- "/v1/threads/thread_1/runs": {
'assistant_id': str,
'instructions': str,
} | ai_ref_knowledge | OPEA Documentation | # threads run API is to start to execute agent thread using run api
- "/v1/threads/thread_1/runs": {
'assistant_id': str,
'instructions': str,
} | # threads run API is to start to execute agent thread using run api
- "/v1/threads/thread_1/runs": {
'assistant_id': str,
'instructions': str,
} | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
90000fd6-0485-4b77-a404-8b26d10df825 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 44 | opea-semantic-v1 | 6d499a1773fdffc6 | 
The user can build and launch the graph-based message group by the combination of docker image and yaml file:
 | ai_ref_knowledge | OPEA Documentation | 
The user can build and launch the graph-based message group by the combination of docker image and yaml file:
 | 
The user can build and launch the graph-based message group by the combination of docker image and yaml file:
 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
90947425-f63d-45f8-956d-42ee5bcb47b5 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 27 | opea-semantic-v1 | 8978b6978fdcf63d | "/v1/assistants": { "instructions": str, "name": str, "tools": list } "/v1/threads/: {} "/v1/threads/thread_1/runs": { 'assistant_id': str, 'instructions': str, } "/v1/threads/thread_1/messages": { "role": str, "content": str }
#### Agent Role microservice definition - 'Agent':
A complete implementation of Agent, whic... | ai_ref_knowledge | OPEA Documentation | "/v1/assistants": { "instructions": str, "name": str, "tools": list } "/v1/threads/: {} "/v1/threads/thread_1/runs": { 'assistant_id': str, 'instructions': str, } "/v1/threads/thread_1/messages": { "role": str, "content": str }
#### Agent Role microservice definition - 'Agent':
A complete implementation of Agent, whic... | "/v1/assistants": { "instructions": str, "name": str, "tools": list } "/v1/threads/: {} "/v1/threads/thread_1/runs": { 'assistant_id': str, 'instructions': str, } "/v1/threads/thread_1/messages": { "role": str, "content": str }
#### Agent Role microservice definition - 'Agent':
A complete implementation of Agent, whic... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
9c65d311-9855-454d-9692-60d92586aa09 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 14 | opea-semantic-v1 | da7ba11a720c36b8 | * Support assistants API * K8s helm chart * Agent Example - Insight Assistant v0.1 * Shared demo with IT * Establish IT collaboration effort
V1.0
* Performance benchmark
* Scaling
* Concurrency | ai_ref_knowledge | OPEA Documentation | * Support assistants API * K8s helm chart * Agent Example - Insight Assistant v0.1 * Shared demo with IT * Establish IT collaboration effort
V1.0
* Performance benchmark
* Scaling
* Concurrency | * Support assistants API * K8s helm chart * Agent Example - Insight Assistant v0.1 * Shared demo with IT * Establish IT collaboration effort
V1.0
* Performance benchmark
* Scaling
* Concurrency | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a32f29b0-5c1e-48b5-bda1-ee80082aa684 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 39 | opea-semantic-v1 | d14da0ad57489706 | __Example 1__: ‘Single Agent megaservice’ Only 1 agent is presented in this configuration. 
3 tools are registered to this agent through custom_tools.yaml
3 tools are registered to this agent through custom_tools.yaml
3 tools are registered to this agent through custom_tools.yaml
require_human_feedback: bool
llm_endpoint_url: str
llm_engine: choices([tgi, vllm, openai])
llm_model_id: str
recursion_limit: int
tools: file_path or dict | ai_ref_knowledge | OPEA Documentation | configuration:
strategy: choices([react, planexec, humanInLoopPlanExec])
require_human_feedback: bool
llm_endpoint_url: str
llm_engine: choices([tgi, vllm, openai])
llm_model_id: str
recursion_limit: int
tools: file_path or dict | configuration:
strategy: choices([react, planexec, humanInLoopPlanExec])
require_human_feedback: bool
llm_endpoint_url: str
llm_engine: choices([tgi, vllm, openai])
llm_model_id: str
recursion_limit: int
tools: file_path or dict | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ad0d7c11-e8b0-45d1-b461-0ffba5a44df2 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 5 | opea-semantic-v1 | 5c49f1a41dff7915 | "{'input': 'Generate a Analyst Stock Recommendations by taking an average of all analyst recommendations and classifying them as Strong Buy, Buy, Hold, Underperform or Sell.'}"
 | ai_ref_knowledge | OPEA Documentation | "{'input': 'Generate a Analyst Stock Recommendations by taking an average of all analyst recommendations and classifying them as Strong Buy, Buy, Hold, Underperform or Sell.'}"
 | "{'input': 'Generate a Analyst Stock Recommendations by taking an average of all analyst recommendations and classifying them as Strong Buy, Buy, Hold, Underperform or Sell.'}"
 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b686ae9c-1bc8-4c83-b92b-8384cbab5c8b | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 3 | opea-semantic-v1 | fa25f0007135f175 | ready-to-use individual agents. (4) For small tasks which can be perfectly performed by single Agent, user can directly use 'Agent' microservice with simple/easy resource management.
Multi Agent example: | ai_ref_knowledge | OPEA Documentation | ready-to-use individual agents. (4) For small tasks which can be perfectly performed by single Agent, user can directly use 'Agent' microservice with simple/easy resource management.
Multi Agent example: | ready-to-use individual agents. (4) For small tasks which can be perfectly performed by single Agent, user can directly use 'Agent' microservice with simple/easy resource management.
Multi Agent example: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
bc2bc468-ca77-4605-8f5c-38c5a7ca95b2 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 12 | opea-semantic-v1 | ca6283c971bdbd01 | ### Execution Plan
v0.8 (PR ready or merge to opea - agent branch)
* Agent component v0.1
* Support chat-completion API
* Agent example - Insight Assistant v0.1 (IT demo)
* hierarchical multi agents
* includes: research(rag, data_crawler); writer(format); reviewer(rule)
* Agent debug system | ai_ref_knowledge | OPEA Documentation | ### Execution Plan
v0.8 (PR ready or merge to opea - agent branch)
* Agent component v0.1
* Support chat-completion API
* Agent example - Insight Assistant v0.1 (IT demo)
* hierarchical multi agents
* includes: research(rag, data_crawler); writer(format); reviewer(rule)
* Agent debug system | ### Execution Plan
v0.8 (PR ready or merge to opea - agent branch)
* Agent component v0.1
* Support chat-completion API
* Agent example - Insight Assistant v0.1 (IT demo)
* hierarchical multi agents
* includes: research(rag, data_crawler); writer(format); reviewer(rule)
* Agent debug system | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
be76abb0-8ce8-4160-8ca0-413c1782baad | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 17 | opea-semantic-v1 | 7a5d487a0b6b572f | working with any existing client uses openAI. * will not be able to memorize user historical session, human_in_loop agent will not work using this API.
"/v1/chat/completions": {
"model": str,
"messages": list,
"tools": list,
} | ai_ref_knowledge | OPEA Documentation | working with any existing client uses openAI. * will not be able to memorize user historical session, human_in_loop agent will not work using this API.
"/v1/chat/completions": {
"model": str,
"messages": list,
"tools": list,
} | working with any existing client uses openAI. * will not be able to memorize user historical session, human_in_loop agent will not work using this API.
"/v1/chat/completions": {
"model": str,
"messages": list,
"tools": list,
} | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c70a25f1-823c-4b4b-94b8-a7d41df785b2 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 30 | opea-semantic-v1 | 6ad2de3fffdcd123 | strategy: choices([react, planexec, humanInLoopPlanExec]) require_human_feedback: bool llm_endpoint_url: str llm_engine: choices([tgi, vllm, openai]) llm_model_id: str recursion_limit: int tools: file_path or dict
# Tools definition
[tool_name]:
description: str
callable_api: choices([http://xxxx, xxx.py:func_name])... | ai_ref_knowledge | OPEA Documentation | strategy: choices([react, planexec, humanInLoopPlanExec]) require_human_feedback: bool llm_endpoint_url: str llm_engine: choices([tgi, vllm, openai]) llm_model_id: str recursion_limit: int tools: file_path or dict
# Tools definition
[tool_name]:
description: str
callable_api: choices([http://xxxx, xxx.py:func_name])... | strategy: choices([react, planexec, humanInLoopPlanExec]) require_human_feedback: bool llm_endpoint_url: str llm_engine: choices([tgi, vllm, openai]) llm_model_id: str recursion_limit: int tools: file_path or dict
# Tools definition
[tool_name]:
description: str
callable_api: choices([http://xxxx, xxx.py:func_name])... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c73023e1-b90f-4ae1-a41d-025bee6c4809 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 16 | opea-semantic-v1 | 25c0cad15f9ab32c | Provide two types of API for different client application. 1. openAI chat completion API. > Reference: https://platform.openai.com/docs/api-reference/chat/create
Advantage and limitation:
* Most common API, should be working with any existing client uses openAI. * will not be able to memorize user historical session, ... | ai_ref_knowledge | OPEA Documentation | Provide two types of API for different client application. 1. openAI chat completion API. > Reference: https://platform.openai.com/docs/api-reference/chat/create
Advantage and limitation:
* Most common API, should be working with any existing client uses openAI. * will not be able to memorize user historical session, ... | Provide two types of API for different client application. 1. openAI chat completion API. > Reference: https://platform.openai.com/docs/api-reference/chat/create
Advantage and limitation:
* Most common API, should be working with any existing client uses openAI. * will not be able to memorize user historical session, ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
cf523fc8-fc5b-4bb4-b9b9-ace3af2a65ea | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 34 | opea-semantic-v1 | 1ce74dbc75af3332 | Configuration:
[tool_name]:
description: str
callable_api: choices([http://xxxx, xxx.py:func_name])
env: str
pip_dependencies: str # sep by ,
args_schema:
query:
type: choices([int, str, bool])
description: str
return_output: str | ai_ref_knowledge | OPEA Documentation | Configuration:
[tool_name]:
description: str
callable_api: choices([http://xxxx, xxx.py:func_name])
env: str
pip_dependencies: str # sep by ,
args_schema:
query:
type: choices([int, str, bool])
description: str
return_output: str | Configuration:
[tool_name]:
description: str
callable_api: choices([http://xxxx, xxx.py:func_name])
env: str
pip_dependencies: str # sep by ,
args_schema:
query:
type: choices([int, str, bool])
description: str
return_output: str | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d2d48ecb-ab48-4065-8fee-5238ccf655bf | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 21 | opea-semantic-v1 | b62fbf8ca8aef388 | conversation session with one user. It can be resumed from previous, can tracking long term memories. - "/v1/threads/ ": { # empty is allowed }
# threads messages API is to add a task content to thread_1 (the thread created by threads API)
- "/v1/threads/thread_1/messages": {
"role": str,
"content": str
} | ai_ref_knowledge | OPEA Documentation | conversation session with one user. It can be resumed from previous, can tracking long term memories. - "/v1/threads/ ": { # empty is allowed }
# threads messages API is to add a task content to thread_1 (the thread created by threads API)
- "/v1/threads/thread_1/messages": {
"role": str,
"content": str
} | conversation session with one user. It can be resumed from previous, can tracking long term memories. - "/v1/threads/ ": { # empty is allowed }
# threads messages API is to add a task content to thread_1 (the thread created by threads API)
- "/v1/threads/thread_1/messages": {
"role": str,
"content": str
} | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e03a774e-d5d2-4fef-ad36-ec84ac3c36c2 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 24 | opea-semantic-v1 | 4757825205a8450c | ### Part 2. 'Agent' genAI Component definition
'Agent' genAI Component is regarded as the resource management unit in “Agent” design. It will be launched as one microservice and can be instantiated as ‘Agent’, ‘Planner’ or ‘Executor’ according to configuration. Tools will be registered to 'Agent' microservice during la... | ai_ref_knowledge | OPEA Documentation | ### Part 2. 'Agent' genAI Component definition
'Agent' genAI Component is regarded as the resource management unit in “Agent” design. It will be launched as one microservice and can be instantiated as ‘Agent’, ‘Planner’ or ‘Executor’ according to configuration. Tools will be registered to 'Agent' microservice during la... | ### Part 2. 'Agent' genAI Component definition
'Agent' genAI Component is regarded as the resource management unit in “Agent” design. It will be launched as one microservice and can be instantiated as ‘Agent’, ‘Planner’ or ‘Executor’ according to configuration. Tools will be registered to 'Agent' microservice during la... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e7cb1a4a-e752-4661-9fed-b1046286f881 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 28 | opea-semantic-v1 | d525a0e5d45d44de | of Agent, which contains LLM endpoint as planner, strategy algorithm for plan execution, Tools, and database handler to keep track of historical state and conversation.
configuration: | ai_ref_knowledge | OPEA Documentation | of Agent, which contains LLM endpoint as planner, strategy algorithm for plan execution, Tools, and database handler to keep track of historical state and conversation.
configuration: | of Agent, which contains LLM endpoint as planner, strategy algorithm for plan execution, Tools, and database handler to keep track of historical state and conversation.
configuration: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
efd4bc77-15b3-46c3-959c-7849de941431 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 31 | opea-semantic-v1 | 78300339ddbf1bf5 | definition [tool_name]: description: str callable_api: choices([http://xxxx, xxx.py:func_name]) env: str pip_dependencies: str # sep by , args_schema: query: type: choices([int, str, bool]) description: str return_output: str
#### Agent Role microservice definition - 'Planner':
Agent without tools. Planner only contai... | ai_ref_knowledge | OPEA Documentation | definition [tool_name]: description: str callable_api: choices([http://xxxx, xxx.py:func_name]) env: str pip_dependencies: str # sep by , args_schema: query: type: choices([int, str, bool]) description: str return_output: str
#### Agent Role microservice definition - 'Planner':
Agent without tools. Planner only contai... | definition [tool_name]: description: str callable_api: choices([http://xxxx, xxx.py:func_name]) env: str pip_dependencies: str # sep by , args_schema: query: type: choices([int, str, bool]) description: str return_output: str
#### Agent Role microservice definition - 'Planner':
Agent without tools. Planner only contai... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f2ee7803-be34-4260-90c5-87bd6d6babb5 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 37 | opea-semantic-v1 | 93ee9dea49c219bb | simple yaml definition can still be used to compose a multi agent system to handle complex tasks. > Detailed configuration please refer to Part3.1 
* Phase II: Graph-Based Multi Agent
1. In this design, we provide user a new SDK t... | ai_ref_knowledge | OPEA Documentation | simple yaml definition can still be used to compose a multi agent system to handle complex tasks. > Detailed configuration please refer to Part3.1 
* Phase II: Graph-Based Multi Agent
1. In this design, we provide user a new SDK t... | simple yaml definition can still be used to compose a multi agent system to handle complex tasks. > Detailed configuration please refer to Part3.1 
* Phase II: Graph-Based Multi Agent
1. In this design, we provide user a new SDK t... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
fa737830-1cd5-4c3d-9318-147c3bec80f3 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 25 | opea-semantic-v1 | d443ae39fd724152 | one microservice and can be instantiated as ‘Agent’, ‘Planner’ or ‘Executor’ according to configuration. Tools will be registered to 'Agent' microservice during launch or runtime.
 | ai_ref_knowledge | OPEA Documentation | one microservice and can be instantiated as ‘Agent’, ‘Planner’ or ‘Executor’ according to configuration. Tools will be registered to 'Agent' microservice during launch or runtime.
 | one microservice and can be instantiated as ‘Agent’, ‘Planner’ or ‘Executor’ according to configuration. Tools will be registered to 'Agent' microservice during launch or runtime.
 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
fae04a04-91c2-425a-98d6-ac5946b7ca40 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-07-11-OPEA-Agent.md | unknown | a7609592-a6f1-4ba4-8620-0f646259fe4b | 2 | opea-semantic-v1 | 81c34dd5729ed30f | 
* ‘Multi Agent' system: Multi Agents refer to a design that leveraging a Hierarchical Agent Teams to complete sub-tasks through individual agent working groups. Benefits of multi-agents’ design: (1) Grouping tools/responsibi... | ai_ref_knowledge | OPEA Documentation | 
* ‘Multi Agent' system: Multi Agents refer to a design that leveraging a Hierarchical Agent Teams to complete sub-tasks through individual agent working groups. Benefits of multi-agents’ design: (1) Grouping tools/responsibi... | 
* ‘Multi Agent' system: Multi Agents refer to a design that leveraging a Hierarchical Agent Teams to complete sub-tasks through individual agent working groups. Benefits of multi-agents’ design: (1) Grouping tools/responsibi... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0efedcec-2376-4896-97bf-82ab8d4e2f71 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-02-OPEA-AIAvatarChatbot.md | unknown | 8f162d0c-5272-470d-aa8e-abd7756516b8 | 25 | opea-semantic-v1 | f3e038ad1769cc6c | flowchart LR %% Colors %% classDef blue fill:#ADD8E6,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5 classDef thistle fill:#D8BFD8,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5 classDef orange fill:#FBAA60,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5 classDef orchid fill:#C26DBC,stroke:#ADD8E6,stroke-width:2px,... | ai_ref_knowledge | OPEA Documentation | flowchart LR %% Colors %% classDef blue fill:#ADD8E6,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5 classDef thistle fill:#D8BFD8,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5 classDef orange fill:#FBAA60,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5 classDef orchid fill:#C26DBC,stroke:#ADD8E6,stroke-width:2px,... | flowchart LR %% Colors %% classDef blue fill:#ADD8E6,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5 classDef thistle fill:#D8BFD8,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5 classDef orange fill:#FBAA60,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5 classDef orchid fill:#C26DBC,stroke:#ADD8E6,stroke-width:2px,... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1119b5b9-5d9e-485f-af4b-4f43bb7fb909 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-02-OPEA-AIAvatarChatbot.md | unknown | 8f162d0c-5272-470d-aa8e-abd7756516b8 | 13 | opea-semantic-v1 | e4bbec837f553372 | the existing chatbot pipelines such as [ChatQnA](https://github.com/opea-project/GenAIExamples/tree/2e312f44edbcbf89bf00bc21d9e9c847405ecae8/ChatQnA), [AudioQnA](https://github.com/opea-project/GenAIExamples/tree/2e312f44edbcbf89bf00bc21d9e9c847405ecae8/AudioQnA), [SearchQnA](https://github.com/opea-project/GenAIExampl... | ai_ref_knowledge | OPEA Documentation | the existing chatbot pipelines such as [ChatQnA](https://github.com/opea-project/GenAIExamples/tree/2e312f44edbcbf89bf00bc21d9e9c847405ecae8/ChatQnA), [AudioQnA](https://github.com/opea-project/GenAIExamples/tree/2e312f44edbcbf89bf00bc21d9e9c847405ecae8/AudioQnA), [SearchQnA](https://github.com/opea-project/GenAIExampl... | the existing chatbot pipelines such as [ChatQnA](https://github.com/opea-project/GenAIExamples/tree/2e312f44edbcbf89bf00bc21d9e9c847405ecae8/ChatQnA), [AudioQnA](https://github.com/opea-project/GenAIExamples/tree/2e312f44edbcbf89bf00bc21d9e9c847405ecae8/AudioQnA), [SearchQnA](https://github.com/opea-project/GenAIExampl... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
11aa3e1f-fb82-4cb9-b244-dd7309174fe9 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-02-OPEA-AIAvatarChatbot.md | unknown | 8f162d0c-5272-470d-aa8e-abd7756516b8 | 7 | opea-semantic-v1 | 287f45b5bb6b1d48 | src="assets/avatar2.jpg" alt="Image 3" width="130"/> <img src="assets/avatar3.png" alt="Image 4" width="130"/> --> <!-- <img src="assets/avatar5.png" alt="Image 5" width="100"/> --> <!-- <img src="assets/avatar6.png" alt="Image 6" width="130"/> </p> -->
 | ai_ref_knowledge | OPEA Documentation | src="assets/avatar2.jpg" alt="Image 3" width="130"/> <img src="assets/avatar3.png" alt="Image 4" width="130"/> --> <!-- <img src="assets/avatar5.png" alt="Image 5" width="100"/> --> <!-- <img src="assets/avatar6.png" alt="Image 6" width="130"/> </p> -->
 | src="assets/avatar2.jpg" alt="Image 3" width="130"/> <img src="assets/avatar3.png" alt="Image 4" width="130"/> --> <!-- <img src="assets/avatar5.png" alt="Image 5" width="100"/> --> <!-- <img src="assets/avatar6.png" alt="Image 6" width="130"/> </p> -->
 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
13439a04-9df4-4582-9e1e-aaca076c69a8 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-02-OPEA-AIAvatarChatbot.md | unknown | 8f162d0c-5272-470d-aa8e-abd7756516b8 | 35 | opea-semantic-v1 | 4140c9e00d637708 | 
## Compatibility
<!-- List possible incompatible interface or workflow changes if exists. -->
The new AvatarChatbot megaservice and animation microservice are compatible with the existing OPEA GenAIExamples and GenAIComps repo... | ai_ref_knowledge | OPEA Documentation | 
## Compatibility
<!-- List possible incompatible interface or workflow changes if exists. -->
The new AvatarChatbot megaservice and animation microservice are compatible with the existing OPEA GenAIExamples and GenAIComps repo... | 
## Compatibility
<!-- List possible incompatible interface or workflow changes if exists. -->
The new AvatarChatbot megaservice and animation microservice are compatible with the existing OPEA GenAIExamples and GenAIComps repo... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1615d483-04b1-4950-a6b0-8c81d7eb773c | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-02-OPEA-AIAvatarChatbot.md | unknown | 8f162d0c-5272-470d-aa8e-abd7756516b8 | 12 | opea-semantic-v1 | 4a6e2cebf57e35c6 | can be integrated seamlessly with existing micro- and mega-services in OPEA, to enhance the platform's capabilities in multimodal AI, human-computer interaction, and digital human graphics.
Overall, this project adds to the OPEA platform a new microservice block that animates the chatbot appearance, and integrates it w... | ai_ref_knowledge | OPEA Documentation | can be integrated seamlessly with existing micro- and mega-services in OPEA, to enhance the platform's capabilities in multimodal AI, human-computer interaction, and digital human graphics.
Overall, this project adds to the OPEA platform a new microservice block that animates the chatbot appearance, and integrates it w... | can be integrated seamlessly with existing micro- and mega-services in OPEA, to enhance the platform's capabilities in multimodal AI, human-computer interaction, and digital human graphics.
Overall, this project adds to the OPEA platform a new microservice block that animates the chatbot appearance, and integrates it w... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
16f8e36c-5129-40d8-b158-5d89d6146c96 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-02-OPEA-AIAvatarChatbot.md | unknown | 8f162d0c-5272-470d-aa8e-abd7756516b8 | 20 | opea-semantic-v1 | d11b24120e446351 | high-quality video of the avatar speaking in real-time. The animation microservice currently uses the [Wav2Lip](https://github.com/Rudrabha/Wav2Lip) model for lip synchronization and [GFPGAN](https://github.com/TencentARC/GFPGAN) model for face restoration.
User can build their own Docker image with `Dockerfile_hpu` an... | ai_ref_knowledge | OPEA Documentation | high-quality video of the avatar speaking in real-time. The animation microservice currently uses the [Wav2Lip](https://github.com/Rudrabha/Wav2Lip) model for lip synchronization and [GFPGAN](https://github.com/TencentARC/GFPGAN) model for face restoration.
User can build their own Docker image with `Dockerfile_hpu` an... | high-quality video of the avatar speaking in real-time. The animation microservice currently uses the [Wav2Lip](https://github.com/Rudrabha/Wav2Lip) model for lip synchronization and [GFPGAN](https://github.com/TencentARC/GFPGAN) model for face restoration.
User can build their own Docker image with `Dockerfile_hpu` an... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
2e83c0b4-31a2-479c-8c04-62646a8851aa | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-02-OPEA-AIAvatarChatbot.md | unknown | 8f162d0c-5272-470d-aa8e-abd7756516b8 | 15 | opea-semantic-v1 | d65eb126728f03a4 | ## Design Proposal <!-- This is the heart of the document, used to elaborate the design philosophy and detail proposal. -->
### Avatar Chatbot design
<!-- Removed PPT slides --> | ai_ref_knowledge | OPEA Documentation | ## Design Proposal <!-- This is the heart of the document, used to elaborate the design philosophy and detail proposal. -->
### Avatar Chatbot design
<!-- Removed PPT slides --> | ## Design Proposal <!-- This is the heart of the document, used to elaborate the design philosophy and detail proposal. -->
### Avatar Chatbot design
<!-- Removed PPT slides --> | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
37de374d-5af8-4a94-9c92-11abf481188a | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-02-OPEA-AIAvatarChatbot.md | unknown | 8f162d0c-5272-470d-aa8e-abd7756516b8 | 28 | opea-semantic-v1 | 53ef00e210d11275 | %% Connections %% direction LR USER1 -->|1| UI UI -->|2| GW GW <==>|3| AvatarChatbot-Megaservice ASR ==>|4| LLM ==>|5| TTS ==>|6| animation
direction TB
ASR <-.->|3'| WHISPER
LLM <-.->|4'| TGI
TTS <-.->|5'| T5
animation <-.->|6'| WAV2LIP | ai_ref_knowledge | OPEA Documentation | %% Connections %% direction LR USER1 -->|1| UI UI -->|2| GW GW <==>|3| AvatarChatbot-Megaservice ASR ==>|4| LLM ==>|5| TTS ==>|6| animation
direction TB
ASR <-.->|3'| WHISPER
LLM <-.->|4'| TGI
TTS <-.->|5'| T5
animation <-.->|6'| WAV2LIP | %% Connections %% direction LR USER1 -->|1| UI UI -->|2| GW GW <==>|3| AvatarChatbot-Megaservice ASR ==>|4| LLM ==>|5| TTS ==>|6| animation
direction TB
ASR <-.->|3'| WHISPER
LLM <-.->|4'| TGI
TTS <-.->|5'| T5
animation <-.->|6'| WAV2LIP | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
57a581bb-5d91-4e0c-a161-da6868f052a1 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-02-OPEA-AIAvatarChatbot.md | unknown | 8f162d0c-5272-470d-aa8e-abd7756516b8 | 6 | opea-semantic-v1 | bf02f548320ec04c | and image/video inputs; and a new megaservice, AvatarChatbot, that integrates the animation microservice with the existing AudioQnA service to build a human-like AI audio chatbot.
<!--<p align="left">
<img src="assets/avatar4.png" alt="Image 1" width="130"/>
<img src="assets/avatar1.jpg" alt="Image 2" width="130"/>
... | ai_ref_knowledge | OPEA Documentation | and image/video inputs; and a new megaservice, AvatarChatbot, that integrates the animation microservice with the existing AudioQnA service to build a human-like AI audio chatbot.
<!--<p align="left">
<img src="assets/avatar4.png" alt="Image 1" width="130"/>
<img src="assets/avatar1.jpg" alt="Image 2" width="130"/>
... | and image/video inputs; and a new megaservice, AvatarChatbot, that integrates the animation microservice with the existing AudioQnA service to build a human-like AI audio chatbot.
<!--<p align="left">
<img src="assets/avatar4.png" alt="Image 1" width="130"/>
<img src="assets/avatar1.jpg" alt="Image 2" width="130"/>
... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5861c4b7-465c-4446-b3a2-a12e29f9da65 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-02-OPEA-AIAvatarChatbot.md | unknown | 8f162d0c-5272-470d-aa8e-abd7756516b8 | 3 | opea-semantic-v1 | f9a453ff2ea0ef8a | --> v0.1 - ASMO Team sharing on Thursday 10/24/2024 * [GenAIComps pr #775](https://github.com/opea-project/GenAIComps/pull/775) | <span style="color: green;">Merged</span> * [GenAIExamples pr #923](https://github.com/opea-project/GenAIExamples/pull/923) | <span style="color: green;">Merged</span>
## Objective
<!-- List... | ai_ref_knowledge | OPEA Documentation | --> v0.1 - ASMO Team sharing on Thursday 10/24/2024 * [GenAIComps pr #775](https://github.com/opea-project/GenAIComps/pull/775) | <span style="color: green;">Merged</span> * [GenAIExamples pr #923](https://github.com/opea-project/GenAIExamples/pull/923) | <span style="color: green;">Merged</span>
## Objective
<!-- List... | --> v0.1 - ASMO Team sharing on Thursday 10/24/2024 * [GenAIComps pr #775](https://github.com/opea-project/GenAIComps/pull/775) | <span style="color: green;">Merged</span> * [GenAIExamples pr #923](https://github.com/opea-project/GenAIExamples/pull/923) | <span style="color: green;">Merged</span>
## Objective
<!-- List... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
596c5b3c-a833-4ee5-ab2a-6b8727755bf7 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-02-OPEA-AIAvatarChatbot.md | unknown | 8f162d0c-5272-470d-aa8e-abd7756516b8 | 0 | opea-semantic-v1 | 8e814f3b6ed9a6e4 | Code contributions: "animation" component: https://github.com/opea-project/GenAIComps/tree/main/comps/animation/wav2lip "AvatarChatbot" examples: https://github.com/opea-project/GenAIExamples/tree/main/AvatarChatbot
Intel Developer Zone Article "Create an AI Avatar Talking Bot with PyTorch* and Open Platform for Enterp... | ai_ref_knowledge | OPEA Documentation | Code contributions: "animation" component: https://github.com/opea-project/GenAIComps/tree/main/comps/animation/wav2lip "AvatarChatbot" examples: https://github.com/opea-project/GenAIExamples/tree/main/AvatarChatbot
Intel Developer Zone Article "Create an AI Avatar Talking Bot with PyTorch* and Open Platform for Enterp... | Code contributions: "animation" component: https://github.com/opea-project/GenAIComps/tree/main/comps/animation/wav2lip "AvatarChatbot" examples: https://github.com/opea-project/GenAIExamples/tree/main/AvatarChatbot
Intel Developer Zone Article "Create an AI Avatar Talking Bot with PyTorch* and Open Platform for Enterp... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5ccd2aca-d364-4073-b50d-5e74f7fca960 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-02-OPEA-AIAvatarChatbot.md | unknown | 8f162d0c-5272-470d-aa8e-abd7756516b8 | 33 | opea-semantic-v1 | 6115e71a1da05592 | 
### Real-time demo
AI Avatar Chatbot Demo on Intel® Gaudi® 2, image input (top) and video input (down)
<!-- <div style="display: flex; justify-content: space-between;">
<video src="assets/demo_latest_image.mpg" controls style="width: 49%;"></video>
<video src="assets/demo_latest_v... | ai_ref_knowledge | OPEA Documentation | 
### Real-time demo
AI Avatar Chatbot Demo on Intel® Gaudi® 2, image input (top) and video input (down)
<!-- <div style="display: flex; justify-content: space-between;">
<video src="assets/demo_latest_image.mpg" controls style="width: 49%;"></video>
<video src="assets/demo_latest_v... | 
### Real-time demo
AI Avatar Chatbot Demo on Intel® Gaudi® 2, image input (top) and video input (down)
<!-- <div style="display: flex; justify-content: space-between;">
<video src="assets/demo_latest_image.mpg" controls style="width: 49%;"></video>
<video src="assets/demo_latest_v... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5d6d81f1-b806-425b-8453-4d77b11f9269 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-02-OPEA-AIAvatarChatbot.md | unknown | 8f162d0c-5272-470d-aa8e-abd7756516b8 | 2 | opea-semantic-v1 | e57989d97b8d5b38 | ## Author <!-- List all contributors of this RFC. --> [ctao456](https://github.com/ctao456), [alexsin368](https://github.com/alexsin368), [YuningQiu](https://github.com/YuningQiu), [louie-tsai](https://github.com/louie-tsai)
## Status
<!-- Change the PR status to Under Review | Rejected | Accepted. -->
v0.1 - ASMO Team... | ai_ref_knowledge | OPEA Documentation | ## Author <!-- List all contributors of this RFC. --> [ctao456](https://github.com/ctao456), [alexsin368](https://github.com/alexsin368), [YuningQiu](https://github.com/YuningQiu), [louie-tsai](https://github.com/louie-tsai)
## Status
<!-- Change the PR status to Under Review | Rejected | Accepted. -->
v0.1 - ASMO Team... | ## Author <!-- List all contributors of this RFC. --> [ctao456](https://github.com/ctao456), [alexsin368](https://github.com/alexsin368), [YuningQiu](https://github.com/YuningQiu), [louie-tsai](https://github.com/louie-tsai)
## Status
<!-- Change the PR status to Under Review | Rejected | Accepted. -->
v0.1 - ASMO Team... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6020c082-9330-41c0-a2cf-373cc5f904bc | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-02-OPEA-AIAvatarChatbot.md | unknown | 8f162d0c-5272-470d-aa8e-abd7756516b8 | 32 | opea-semantic-v1 | cb4c4e0c65d04504 | <div style="display: flex; justify-content: space-between;"> <img src="assets/ui_latest_1.png" alt="alt text" style="width: 33%;"/> <img src="assets/ui_latest_2.png" alt="alt text" style="width: 33%;"/> <img src="assets/ui_latest_3.png" alt="alt text" style="width: 33%;"/> </div> -->
![avatars ui](assets/avatars-ui.png... | ai_ref_knowledge | OPEA Documentation | <div style="display: flex; justify-content: space-between;"> <img src="assets/ui_latest_1.png" alt="alt text" style="width: 33%;"/> <img src="assets/ui_latest_2.png" alt="alt text" style="width: 33%;"/> <img src="assets/ui_latest_3.png" alt="alt text" style="width: 33%;"/> </div> -->
![avatars ui](assets/avatars-ui.png... | <div style="display: flex; justify-content: space-between;"> <img src="assets/ui_latest_1.png" alt="alt text" style="width: 33%;"/> <img src="assets/ui_latest_2.png" alt="alt text" style="width: 33%;"/> <img src="assets/ui_latest_3.png" alt="alt text" style="width: 33%;"/> </div> -->
![avatars ui](assets/avatars-ui.png... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6b842a00-414e-4335-9217-70a48b526977 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-02-OPEA-AIAvatarChatbot.md | unknown | 8f162d0c-5272-470d-aa8e-abd7756516b8 | 17 | opea-semantic-v1 | 429f1dc352ca48f7 | above design, including uploading relevant documents/weblinks, storing them in the database, and retrieving them for the LLM model. These features will be added in v0.2.
Flowchart: AvatarChatbot Megaservice
<!-- Insert Mermaid flowchart here -->
```mermaid
%%{ init : { "theme" : "base", "flowchart" : { "curve" : "step... | ai_ref_knowledge | OPEA Documentation | above design, including uploading relevant documents/weblinks, storing them in the database, and retrieving them for the LLM model. These features will be added in v0.2.
Flowchart: AvatarChatbot Megaservice
<!-- Insert Mermaid flowchart here -->
```mermaid
%%{ init : { "theme" : "base", "flowchart" : { "curve" : "step... | above design, including uploading relevant documents/weblinks, storing them in the database, and retrieving them for the LLM model. These features will be added in v0.2.
Flowchart: AvatarChatbot Megaservice
<!-- Insert Mermaid flowchart here -->
```mermaid
%%{ init : { "theme" : "base", "flowchart" : { "curve" : "step... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6cf70b8c-72c3-4184-bb7f-03cf97e464d9 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-02-OPEA-AIAvatarChatbot.md | unknown | 8f162d0c-5272-470d-aa8e-abd7756516b8 | 30 | opea-semantic-v1 | 97dca86e509dfe27 | USER2 -->|1| UI UI <-.->|6'| WAV2LIP
#### Frontend UI
The frontend UI is Gradio. User is prompted to upload either an image or a video as the avatar source. The user also asks his question verbally through the microphone by clicking on the "record" button. The AvatarChatbot backend processes the audio input and generat... | ai_ref_knowledge | OPEA Documentation | USER2 -->|1| UI UI <-.->|6'| WAV2LIP
#### Frontend UI
The frontend UI is Gradio. User is prompted to upload either an image or a video as the avatar source. The user also asks his question verbally through the microphone by clicking on the "record" button. The AvatarChatbot backend processes the audio input and generat... | USER2 -->|1| UI UI <-.->|6'| WAV2LIP
#### Frontend UI
The frontend UI is Gradio. User is prompted to upload either an image or a video as the avatar source. The user also asks his question verbally through the microphone by clicking on the "record" button. The AvatarChatbot backend processes the audio input and generat... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6d5e562d-b59b-47f9-981a-ccd5eae85c6c | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-02-OPEA-AIAvatarChatbot.md | unknown | 8f162d0c-5272-470d-aa8e-abd7756516b8 | 4 | opea-semantic-v1 | d743318ed7a6d777 | with computers is as natural as interacting with humans. Yet all existing OPEA applications (ChatQnA, AudioQnA, SearchQnA, etc.) are text-based and lack interactive visual elements.
* Also worthnoting, the majority of existing OPEA applications lack multimodal features, i.e., they do not process both audio and visual i... | ai_ref_knowledge | OPEA Documentation | with computers is as natural as interacting with humans. Yet all existing OPEA applications (ChatQnA, AudioQnA, SearchQnA, etc.) are text-based and lack interactive visual elements.
* Also worthnoting, the majority of existing OPEA applications lack multimodal features, i.e., they do not process both audio and visual i... | with computers is as natural as interacting with humans. Yet all existing OPEA applications (ChatQnA, AudioQnA, SearchQnA, etc.) are text-based and lack interactive visual elements.
* Also worthnoting, the majority of existing OPEA applications lack multimodal features, i.e., they do not process both audio and visual i... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
728947c3-7fad-4d96-96a3-0fbf09147c45 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-02-OPEA-AIAvatarChatbot.md | unknown | 8f162d0c-5272-470d-aa8e-abd7756516b8 | 23 | opea-semantic-v1 | 285db8d08d705935 | service, generates an animated avatar response, and sends it back to the user. The megaflow is as follows: asr -> llm -> tts -> animation
```mermaid | ai_ref_knowledge | OPEA Documentation | service, generates an animated avatar response, and sends it back to the user. The megaflow is as follows: asr -> llm -> tts -> animation
```mermaid | service, generates an animated avatar response, and sends it back to the user. The megaflow is as follows: asr -> llm -> tts -> animation
```mermaid | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
73e571bc-9c72-476f-b705-1ad9c57e85dc | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-02-OPEA-AIAvatarChatbot.md | unknown | 8f162d0c-5272-470d-aa8e-abd7756516b8 | 22 | opea-semantic-v1 | 556b77a3a7d8b09f | Support for alternative SoTA models such as [SadTalker](https://github.com/OpenTalker/SadTalker) and [LivePortrait](https://github.com/KwaiVGI/LivePortrait) are in progress.
#### AvatarChatbot megaservice
The AvatarChatbot megaservice is a new service that integrates the existing microservices that comprise AudioQnA se... | ai_ref_knowledge | OPEA Documentation | Support for alternative SoTA models such as [SadTalker](https://github.com/OpenTalker/SadTalker) and [LivePortrait](https://github.com/KwaiVGI/LivePortrait) are in progress.
#### AvatarChatbot megaservice
The AvatarChatbot megaservice is a new service that integrates the existing microservices that comprise AudioQnA se... | Support for alternative SoTA models such as [SadTalker](https://github.com/OpenTalker/SadTalker) and [LivePortrait](https://github.com/KwaiVGI/LivePortrait) are in progress.
#### AvatarChatbot megaservice
The AvatarChatbot megaservice is a new service that integrates the existing microservices that comprise AudioQnA se... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
83506b65-0fad-4253-a7e4-74f6b6f01c6f | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-02-OPEA-AIAvatarChatbot.md | unknown | 8f162d0c-5272-470d-aa8e-abd7756516b8 | 26 | opea-semantic-v1 | 4060bd86db19d8a6 | UI([UI server<br>]):::orchid end subgraph ChatQnA GateWay direction LR invis2[ ]:::invisible GW([AvatarChatbot GateWay<br>]):::orange end subgraph direction LR X([OPEA Microservice]):::blue Y{{Open Source Service}}:::thistle Z([OPEA Gateway]):::orange Z1([UI]):::orchid end
%% Services %%
WHISPER{{Whisper service<br>7... | ai_ref_knowledge | OPEA Documentation | UI([UI server<br>]):::orchid end subgraph ChatQnA GateWay direction LR invis2[ ]:::invisible GW([AvatarChatbot GateWay<br>]):::orange end subgraph direction LR X([OPEA Microservice]):::blue Y{{Open Source Service}}:::thistle Z([OPEA Gateway]):::orange Z1([UI]):::orchid end
%% Services %%
WHISPER{{Whisper service<br>7... | UI([UI server<br>]):::orchid end subgraph ChatQnA GateWay direction LR invis2[ ]:::invisible GW([AvatarChatbot GateWay<br>]):::orange end subgraph direction LR X([OPEA Microservice]):::blue Y{{Open Source Service}}:::thistle Z([OPEA Gateway]):::orange Z1([UI]):::orchid end
%% Services %%
WHISPER{{Whisper service<br>7... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
9ba070e4-5420-46ac-8002-a8b81aed59ab | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-02-OPEA-AIAvatarChatbot.md | unknown | 8f162d0c-5272-470d-aa8e-abd7756516b8 | 1 | opea-semantic-v1 | 73d9c423bf29fbc9 | Intel Developer Zone Article "Create an AI Avatar Talking Bot with PyTorch* and Open Platform for Enterprise AI (OPEA)": https://www.intel.com/content/www/us/en/developer/articles/technical/ai-avatar-talking-bot-with-pytorch-and-opea.html YouTube tech-talk video: https://youtu.be/OjaElyUB8Z0?si=6-IdxwTg0YFMraFl
## Auth... | ai_ref_knowledge | OPEA Documentation | Intel Developer Zone Article "Create an AI Avatar Talking Bot with PyTorch* and Open Platform for Enterprise AI (OPEA)": https://www.intel.com/content/www/us/en/developer/articles/technical/ai-avatar-talking-bot-with-pytorch-and-opea.html YouTube tech-talk video: https://youtu.be/OjaElyUB8Z0?si=6-IdxwTg0YFMraFl
## Auth... | Intel Developer Zone Article "Create an AI Avatar Talking Bot with PyTorch* and Open Platform for Enterprise AI (OPEA)": https://www.intel.com/content/www/us/en/developer/articles/technical/ai-avatar-talking-bot-with-pytorch-and-opea.html YouTube tech-talk video: https://youtu.be/OjaElyUB8Z0?si=6-IdxwTg0YFMraFl
## Auth... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a826c12a-f960-47f3-a349-b9c415c82613 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-02-OPEA-AIAvatarChatbot.md | unknown | 8f162d0c-5272-470d-aa8e-abd7756516b8 | 18 | opea-semantic-v1 | 5e7f4da8aaa9e4ba | C ==> E ==> G end subgraph AvatarAnimation["Avatar Animation"] direction LR I([Animation<br>3008]) end G ==> I end end subgraph Legend direction LR L([Microservice]) N[Gateway] end
The AvatarChatbot megaservice is a new service that integrates the existing AudioQnA service with the new animation microservice. The Audio... | ai_ref_knowledge | OPEA Documentation | C ==> E ==> G end subgraph AvatarAnimation["Avatar Animation"] direction LR I([Animation<br>3008]) end G ==> I end end subgraph Legend direction LR L([Microservice]) N[Gateway] end
The AvatarChatbot megaservice is a new service that integrates the existing AudioQnA service with the new animation microservice. The Audio... | C ==> E ==> G end subgraph AvatarAnimation["Avatar Animation"] direction LR I([Animation<br>3008]) end G ==> I end end subgraph Legend direction LR L([Microservice]) N[Gateway] end
The AvatarChatbot megaservice is a new service that integrates the existing AudioQnA service with the new animation microservice. The Audio... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ab06c9aa-ea1d-4eae-83c8-f15802d4bd82 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-02-OPEA-AIAvatarChatbot.md | unknown | 8f162d0c-5272-470d-aa8e-abd7756516b8 | 21 | opea-semantic-v1 | 3e75c03ebc0bf7ac | API, while providing audio and image/video inputs. The animation microservice will generate an animated avatar video response and save it to the specified output path.
Support for alternative SoTA models such as [SadTalker](https://github.com/OpenTalker/SadTalker) and [LivePortrait](https://github.com/KwaiVGI/LivePortr... | ai_ref_knowledge | OPEA Documentation | API, while providing audio and image/video inputs. The animation microservice will generate an animated avatar video response and save it to the specified output path.
Support for alternative SoTA models such as [SadTalker](https://github.com/OpenTalker/SadTalker) and [LivePortrait](https://github.com/KwaiVGI/LivePortr... | API, while providing audio and image/video inputs. The animation microservice will generate an animated avatar video response and save it to the specified output path.
Support for alternative SoTA models such as [SadTalker](https://github.com/OpenTalker/SadTalker) and [LivePortrait](https://github.com/KwaiVGI/LivePortr... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
afd5df25-e2c2-4dd8-ad4e-22cd10c2ba2b | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-02-OPEA-AIAvatarChatbot.md | unknown | 8f162d0c-5272-470d-aa8e-abd7756516b8 | 8 | opea-semantic-v1 | a8ff6d636556d0cf | 
The chatbot will:
* Be able to understand and respond to user text and audio queries, with a backend LLM model
* Synchronize audio response chunks with image/video frames, to generate a high-quality video of the avatar speaking in real-time
* Present the animated avatar re... | ai_ref_knowledge | OPEA Documentation | 
The chatbot will:
* Be able to understand and respond to user text and audio queries, with a backend LLM model
* Synchronize audio response chunks with image/video frames, to generate a high-quality video of the avatar speaking in real-time
* Present the animated avatar re... | 
The chatbot will:
* Be able to understand and respond to user text and audio queries, with a backend LLM model
* Synchronize audio response chunks with image/video frames, to generate a high-quality video of the avatar speaking in real-time
* Present the animated avatar re... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b3c04986-322f-4940-a968-838ef2566a3d | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-02-OPEA-AIAvatarChatbot.md | unknown | 8f162d0c-5272-470d-aa8e-abd7756516b8 | 11 | opea-semantic-v1 | 1bdd4280b863b284 | look for multimodal AI solutions that can process both audio and visual inputs, to build lip-synchronized and face-animated chatbots that are more engaging and human-like.
* This RFC aims to fill these gaps by introducing a new microservice, animation, that can be integrated seamlessly with existing micro- and mega-ser... | ai_ref_knowledge | OPEA Documentation | look for multimodal AI solutions that can process both audio and visual inputs, to build lip-synchronized and face-animated chatbots that are more engaging and human-like.
* This RFC aims to fill these gaps by introducing a new microservice, animation, that can be integrated seamlessly with existing micro- and mega-ser... | look for multimodal AI solutions that can process both audio and visual inputs, to build lip-synchronized and face-animated chatbots that are more engaging and human-like.
* This RFC aims to fill these gaps by introducing a new microservice, animation, that can be integrated seamlessly with existing micro- and mega-ser... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b955a385-7e33-4267-87f2-109d7824dcd3 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-02-OPEA-AIAvatarChatbot.md | unknown | 8f162d0c-5272-470d-aa8e-abd7756516b8 | 14 | opea-semantic-v1 | b6d159a078f609ef | Related works include [Nvidia Audio2Face](https://docs.nvidia.com/ace/latest/modules/a2f-docs/index.html), [Lenovo Deepbrain AI Avatar](https://www.deepbrain.io/ai-avatars), [BitHuman](https://www.bithuman.io/), etc.
## Design Proposal
<!-- This is the heart of the document, used to elaborate the design philosophy and ... | ai_ref_knowledge | OPEA Documentation | Related works include [Nvidia Audio2Face](https://docs.nvidia.com/ace/latest/modules/a2f-docs/index.html), [Lenovo Deepbrain AI Avatar](https://www.deepbrain.io/ai-avatars), [BitHuman](https://www.bithuman.io/), etc.
## Design Proposal
<!-- This is the heart of the document, used to elaborate the design philosophy and ... | Related works include [Nvidia Audio2Face](https://docs.nvidia.com/ace/latest/modules/a2f-docs/index.html), [Lenovo Deepbrain AI Avatar](https://www.deepbrain.io/ai-avatars), [BitHuman](https://www.bithuman.io/), etc.
## Design Proposal
<!-- This is the heart of the document, used to elaborate the design philosophy and ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
bace9b13-d01b-49ae-8f2b-f35c3a6d50ea | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-08-02-OPEA-AIAvatarChatbot.md | unknown | 8f162d0c-5272-470d-aa8e-abd7756516b8 | 31 | opea-semantic-v1 | 2248ef2204ee85d6 | UI. User will be able to see the animated avatar speaking the response in real-time, and can interact with the avatar by asking more questions.
<!-- <div style="display: flex; justify-content: space-between;">
<img src="assets/ui_latest_1.png" alt="alt text" style="width: 33%;"/>
<img src="assets/ui_latest_2.png" alt... | ai_ref_knowledge | OPEA Documentation | UI. User will be able to see the animated avatar speaking the response in real-time, and can interact with the avatar by asking more questions.
<!-- <div style="display: flex; justify-content: space-between;">
<img src="assets/ui_latest_1.png" alt="alt text" style="width: 33%;"/>
<img src="assets/ui_latest_2.png" alt... | UI. User will be able to see the animated avatar speaking the response in real-time, and can interact with the avatar by asking more questions.
<!-- <div style="display: flex; justify-content: space-between;">
<img src="assets/ui_latest_1.png" alt="alt text" style="width: 33%;"/>
<img src="assets/ui_latest_2.png" alt... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation |
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