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5adb543c-0ece-4d04-bb37-1d2d8e4e233b | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 21 | opea-semantic-v1 | 75b825991bc71395 | ## Design Proposal
The workflow for the proposed changes are as follows. As shown below, Ollama serving models will be added as LLM serving engine for the Xeon platform. This will work as an alternative for vLLM, TGI and OpenAI LLM engines. New change is highlighted in orange. ```mermaid | ai_ref_knowledge | OPEA Documentation | ## Design Proposal
The workflow for the proposed changes are as follows. As shown below, Ollama serving models will be added as LLM serving engine for the Xeon platform. This will work as an alternative for vLLM, TGI and OpenAI LLM engines. New change is highlighted in orange. ```mermaid | ## Design Proposal
The workflow for the proposed changes are as follows. As shown below, Ollama serving models will be added as LLM serving engine for the Xeon platform. This will work as an alternative for vLLM, TGI and OpenAI LLM engines. New change is highlighted in orange. ```mermaid | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5d924d9e-1927-469f-ba72-11030ec9fc50 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 14 | opea-semantic-v1 | b73671ca7b3f372a | - **Community and Documentation**: Ollama has a strong community and extensive documentation, providing support and resources for developers to effectively use and troubleshoot the tools.
#### Benefits of Using Ollama
- **Flexibility**: Developers can choose from a wide range of models to best fit their specific needs ... | ai_ref_knowledge | OPEA Documentation | - **Community and Documentation**: Ollama has a strong community and extensive documentation, providing support and resources for developers to effectively use and troubleshoot the tools.
#### Benefits of Using Ollama
- **Flexibility**: Developers can choose from a wide range of models to best fit their specific needs ... | - **Community and Documentation**: Ollama has a strong community and extensive documentation, providing support and resources for developers to effectively use and troubleshoot the tools.
#### Benefits of Using Ollama
- **Flexibility**: Developers can choose from a wide range of models to best fit their specific needs ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
613cab09-ceb2-4275-9d88-d15978ac216c | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 30 | opea-semantic-v1 | 16c249754db0bad3 | ### 3. Compatibility
**Compatibility with exisiting services**: Added component will be compatible with the existing components, ensuring seamless integration with the current AgentQnA workflow. Ollama's serving container will work alongside existing LLM services like vLLM, TGI, and OpenAI, maintaining the overall func... | ai_ref_knowledge | OPEA Documentation | ### 3. Compatibility
**Compatibility with exisiting services**: Added component will be compatible with the existing components, ensuring seamless integration with the current AgentQnA workflow. Ollama's serving container will work alongside existing LLM services like vLLM, TGI, and OpenAI, maintaining the overall func... | ### 3. Compatibility
**Compatibility with exisiting services**: Added component will be compatible with the existing components, ensuring seamless integration with the current AgentQnA workflow. Ollama's serving container will work alongside existing LLM services like vLLM, TGI, and OpenAI, maintaining the overall func... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6bb53eec-4802-454c-9170-e5a549e46f06 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 24 | opea-semantic-v1 | 69d9bb184708751d | Z{{Existing serv./ Microserv.}}:::blue end AG_REACT([Agent MicroService - react]):::blue AG_RAG([Agent MicroService - rag]):::blue LLM_gen{{LLM Service}} DP([Data Preparation MicroService]):::blue TEI_RER{{Reranking Service}} TEI_EM{{Embedding Service}} VDB{{Vector DB}} R_RET{{Retriever Service}}
%% Flow %%
a --> AG_R... | ai_ref_knowledge | OPEA Documentation | Z{{Existing serv./ Microserv.}}:::blue end AG_REACT([Agent MicroService - react]):::blue AG_RAG([Agent MicroService - rag]):::blue LLM_gen{{LLM Service}} DP([Data Preparation MicroService]):::blue TEI_RER{{Reranking Service}} TEI_EM{{Embedding Service}} VDB{{Vector DB}} R_RET{{Retriever Service}}
%% Flow %%
a --> AG_R... | Z{{Existing serv./ Microserv.}}:::blue end AG_REACT([Agent MicroService - react]):::blue AG_RAG([Agent MicroService - rag]):::blue LLM_gen{{LLM Service}} DP([Data Preparation MicroService]):::blue TEI_RER{{Reranking Service}} TEI_EM{{Embedding Service}} VDB{{Vector DB}} R_RET{{Retriever Service}}
%% Flow %%
a --> AG_R... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7bda9553-2dbc-4ee2-9fbd-8b4340e59f31 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 4 | opea-semantic-v1 | 34527b574dc3fb27 | reliance on external cloud services is minimized, resulting in reduced network latency and bandwidth usage. This ensures faster response times and more efficient data handling.
### Goals | ai_ref_knowledge | OPEA Documentation | reliance on external cloud services is minimized, resulting in reduced network latency and bandwidth usage. This ensures faster response times and more efficient data handling.
### Goals | reliance on external cloud services is minimized, resulting in reduced network latency and bandwidth usage. This ensures faster response times and more efficient data handling.
### Goals | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
83a25fb4-2c67-420e-bfd6-9a6539ac291d | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 37 | opea-semantic-v1 | 5fc632b8c870d074 | compliance and maintaining client confidentiality. This setup ensures that neither prompts nor any proprietary data stored in a vector database need to leave the enterprise.
6. **Enhanced Reliability**:
- **Scenario**: A manufacturing company relies on AI-driven predictive maintenance to avoid equipment downtime. - **... | ai_ref_knowledge | OPEA Documentation | compliance and maintaining client confidentiality. This setup ensures that neither prompts nor any proprietary data stored in a vector database need to leave the enterprise.
6. **Enhanced Reliability**:
- **Scenario**: A manufacturing company relies on AI-driven predictive maintenance to avoid equipment downtime. - **... | compliance and maintaining client confidentiality. This setup ensures that neither prompts nor any proprietary data stored in a vector database need to leave the enterprise.
6. **Enhanced Reliability**:
- **Scenario**: A manufacturing company relies on AI-driven predictive maintenance to avoid equipment downtime. - **... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
97f52b76-61a7-4c39-bf2f-8c82cc73843c | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 22 | opea-semantic-v1 | 0c88936c3b2b3f97 | engine for the Xeon platform. This will work as an alternative for vLLM, TGI and OpenAI LLM engines. New change is highlighted in orange. ```mermaid
config:
flowchart:
nodeSpacing: 200
rankSpacing: 50
curve: linear
themeVariables:
fontSize: 30px | ai_ref_knowledge | OPEA Documentation | engine for the Xeon platform. This will work as an alternative for vLLM, TGI and OpenAI LLM engines. New change is highlighted in orange. ```mermaid
config:
flowchart:
nodeSpacing: 200
rankSpacing: 50
curve: linear
themeVariables:
fontSize: 30px | engine for the Xeon platform. This will work as an alternative for vLLM, TGI and OpenAI LLM engines. New change is highlighted in orange. ```mermaid
config:
flowchart:
nodeSpacing: 200
rankSpacing: 50
curve: linear
themeVariables:
fontSize: 30px | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
99a73e08-27d8-4337-932d-74343e3c98c9 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 34 | opea-semantic-v1 | ce9c56d2f770b5f4 | LLM agents locally, reducing dependency on expensive cloud-based APIs like OpenAI, Anthropic, Gemini, etc. This significantly lowers operational costs and makes the solution more affordable.
3. **Low Latency and High Performance**:
- **Scenario**: A financial institution requires real-time analysis of market data to m... | ai_ref_knowledge | OPEA Documentation | LLM agents locally, reducing dependency on expensive cloud-based APIs like OpenAI, Anthropic, Gemini, etc. This significantly lowers operational costs and makes the solution more affordable.
3. **Low Latency and High Performance**:
- **Scenario**: A financial institution requires real-time analysis of market data to m... | LLM agents locally, reducing dependency on expensive cloud-based APIs like OpenAI, Anthropic, Gemini, etc. This significantly lowers operational costs and makes the solution more affordable.
3. **Low Latency and High Performance**:
- **Scenario**: A financial institution requires real-time analysis of market data to m... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a395d978-72f7-442e-a9c7-2afc2e15bd37 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 15 | opea-semantic-v1 | ae7a034eb4590e38 | language models on Intel Xeon CPUs. - **Security**: Local deployment of models through Ollama enhances data security by keeping sensitive information within the organization's infrastructure.
By incorporating Ollama into the AgentQnA workflow, the project can leverage these benefits to enhance the overall performance, ... | ai_ref_knowledge | OPEA Documentation | language models on Intel Xeon CPUs. - **Security**: Local deployment of models through Ollama enhances data security by keeping sensitive information within the organization's infrastructure.
By incorporating Ollama into the AgentQnA workflow, the project can leverage these benefits to enhance the overall performance, ... | language models on Intel Xeon CPUs. - **Security**: Local deployment of models through Ollama enhances data security by keeping sensitive information within the organization's infrastructure.
By incorporating Ollama into the AgentQnA workflow, the project can leverage these benefits to enhance the overall performance, ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a827f415-939c-453d-9a97-2fb8d9adcf37 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 28 | opea-semantic-v1 | 51706a28aae29f62 | that hosts Ollama models as an alternative LLM service engine. Hosted models can be accessed by Agent microservice at a given host ip and port.
### 2. Support for latest open-source SLMs from the Llama family and DeepSeek:
- **Add Llama 3.1, 3.2, and DeepSeek-R1 small models for Xeon**: Small language models (SLMs) fro... | ai_ref_knowledge | OPEA Documentation | that hosts Ollama models as an alternative LLM service engine. Hosted models can be accessed by Agent microservice at a given host ip and port.
### 2. Support for latest open-source SLMs from the Llama family and DeepSeek:
- **Add Llama 3.1, 3.2, and DeepSeek-R1 small models for Xeon**: Small language models (SLMs) fro... | that hosts Ollama models as an alternative LLM service engine. Hosted models can be accessed by Agent microservice at a given host ip and port.
### 2. Support for latest open-source SLMs from the Llama family and DeepSeek:
- **Add Llama 3.1, 3.2, and DeepSeek-R1 small models for Xeon**: Small language models (SLMs) fro... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ae4f369c-f23e-4254-aa51-a9791584acd0 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 18 | opea-semantic-v1 | 838d78c08fa57fd0 | ### Cost Efficiency
By integrating open-source small language models (SLMs) through Ollama, organizations can significantly reduce costs associated with language model services. The reliance on paid APIs, such as those provided by OpenAI, can be eliminated, leading to substantial savings. Open-source models do not incu... | ai_ref_knowledge | OPEA Documentation | ### Cost Efficiency
By integrating open-source small language models (SLMs) through Ollama, organizations can significantly reduce costs associated with language model services. The reliance on paid APIs, such as those provided by OpenAI, can be eliminated, leading to substantial savings. Open-source models do not incu... | ### Cost Efficiency
By integrating open-source small language models (SLMs) through Ollama, organizations can significantly reduce costs associated with language model services. The reliance on paid APIs, such as those provided by OpenAI, can be eliminated, leading to substantial savings. Open-source models do not incu... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b2c55b57-4332-404b-802e-b85fb7d6f146 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 36 | opea-semantic-v1 | 6469894c3f0391fe | CPUs enables the enterprise to scale LLM agents locally across various departments. This provides better control over the infrastructure and ensures consistent performance and reliability.
5. **Compliance with Regulations**:
- **Scenario**: A legal firm needs to process confidential client information while adhering t... | ai_ref_knowledge | OPEA Documentation | CPUs enables the enterprise to scale LLM agents locally across various departments. This provides better control over the infrastructure and ensures consistent performance and reliability.
5. **Compliance with Regulations**:
- **Scenario**: A legal firm needs to process confidential client information while adhering t... | CPUs enables the enterprise to scale LLM agents locally across various departments. This provides better control over the infrastructure and ensures consistent performance and reliability.
5. **Compliance with Regulations**:
- **Scenario**: A legal firm needs to process confidential client information while adhering t... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b6079e62-8f15-4af4-b0cd-24d67b046d35 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 25 | opea-semantic-v1 | 6b490b299864e84a | %% Flow %% a --> AG_REACT AG_REACT --> AG_RAG AG_RAG --> DocIndexRetriever EM --> RET RET --> RER Ingest --> DP
AG_RAG <--> LLM_gen
LLM_gen <--> LLMServices
AG_REACT <--> LLM_gen
EM <--> TEI_EM
RET <--> R_RET
RER <--> TEI_RER | ai_ref_knowledge | OPEA Documentation | %% Flow %% a --> AG_REACT AG_REACT --> AG_RAG AG_RAG --> DocIndexRetriever EM --> RET RET --> RER Ingest --> DP
AG_RAG <--> LLM_gen
LLM_gen <--> LLMServices
AG_REACT <--> LLM_gen
EM <--> TEI_EM
RET <--> R_RET
RER <--> TEI_RER | %% Flow %% a --> AG_REACT AG_REACT --> AG_RAG AG_RAG --> DocIndexRetriever EM --> RET RET --> RER Ingest --> DP
AG_RAG <--> LLM_gen
LLM_gen <--> LLMServices
AG_REACT <--> LLM_gen
EM <--> TEI_EM
RET <--> R_RET
RER <--> TEI_RER | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
bd76e5d2-10e0-4c1e-8d77-2f6552d92e0b | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 12 | opea-semantic-v1 | 333767683ce39eaa | the execution of open-source LLMs on local on-prem hardware. In terms of popularity, the [vLLM](https://github.com/vllm-project/vllm) GitHub repository has 35K stars, while [Ollama](https://github.com/ollama/ollama) has 114K stars.
#### Key Features of Ollama
- **Extensive Model Support**: Ollama supports a variety of ... | ai_ref_knowledge | OPEA Documentation | the execution of open-source LLMs on local on-prem hardware. In terms of popularity, the [vLLM](https://github.com/vllm-project/vllm) GitHub repository has 35K stars, while [Ollama](https://github.com/ollama/ollama) has 114K stars.
#### Key Features of Ollama
- **Extensive Model Support**: Ollama supports a variety of ... | the execution of open-source LLMs on local on-prem hardware. In terms of popularity, the [vLLM](https://github.com/vllm-project/vllm) GitHub repository has 35K stars, while [Ollama](https://github.com/ollama/ollama) has 114K stars.
#### Key Features of Ollama
- **Extensive Model Support**: Ollama supports a variety of ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
bf76d23f-3d92-465c-8965-f06993aa2f35 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 19 | opea-semantic-v1 | d3d2ab365b450205 | for expensive cloud-based infrastructure, further lowering operational expenses. This cost efficiency allows organizations to allocate resources to other critical areas, enhancing overall productivity and innovation.
### Enhanced Data Security
Processing data locally ensures that sensitive information remains secure an... | ai_ref_knowledge | OPEA Documentation | for expensive cloud-based infrastructure, further lowering operational expenses. This cost efficiency allows organizations to allocate resources to other critical areas, enhancing overall productivity and innovation.
### Enhanced Data Security
Processing data locally ensures that sensitive information remains secure an... | for expensive cloud-based infrastructure, further lowering operational expenses. This cost efficiency allows organizations to allocate resources to other critical areas, enhancing overall productivity and innovation.
### Enhanced Data Security
Processing data locally ensures that sensitive information remains secure an... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ca759c97-04af-483a-ba04-99a2c85fb834 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 26 | opea-semantic-v1 | 6f4679806298a431 | AG_RAG <--> LLM_gen LLM_gen <--> LLMServices AG_REACT <--> LLM_gen EM <--> TEI_EM RET <--> R_RET RER <--> TEI_RER
R_RET <--> VDB
DP <--> VDB | ai_ref_knowledge | OPEA Documentation | AG_RAG <--> LLM_gen LLM_gen <--> LLMServices AG_REACT <--> LLM_gen EM <--> TEI_EM RET <--> R_RET RER <--> TEI_RER
R_RET <--> VDB
DP <--> VDB | AG_RAG <--> LLM_gen LLM_gen <--> LLMServices AG_REACT <--> LLM_gen EM <--> TEI_EM RET <--> R_RET RER <--> TEI_RER
R_RET <--> VDB
DP <--> VDB | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
caf7bdce-826b-4532-83a2-a9c62b0d8dfa | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 20 | opea-semantic-v1 | 2b21ad44f4ea4022 | ### Enhanced Data Security Processing data locally ensures that sensitive information remains secure and private.
### Open-Source Flexibility
Open-source LLMs provide greater flexibility and customization options compared to proprietary solutions. | ai_ref_knowledge | OPEA Documentation | ### Enhanced Data Security Processing data locally ensures that sensitive information remains secure and private.
### Open-Source Flexibility
Open-source LLMs provide greater flexibility and customization options compared to proprietary solutions. | ### Enhanced Data Security Processing data locally ensures that sensitive information remains secure and private.
### Open-Source Flexibility
Open-source LLMs provide greater flexibility and customization options compared to proprietary solutions. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
cf610e8a-f6cf-4af6-b3ac-0aa0bc9f71db | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 13 | opea-semantic-v1 | 36cfa8a7ee80e794 | models to the workflow. - **Scalability**: Ollama's tools are built to handle different scales of deployment, from small-scale local setups to larger, more complex environments.
- **Community and Documentation**: Ollama has a strong community and extensive documentation, providing support and resources for developers t... | ai_ref_knowledge | OPEA Documentation | models to the workflow. - **Scalability**: Ollama's tools are built to handle different scales of deployment, from small-scale local setups to larger, more complex environments.
- **Community and Documentation**: Ollama has a strong community and extensive documentation, providing support and resources for developers t... | models to the workflow. - **Scalability**: Ollama's tools are built to handle different scales of deployment, from small-scale local setups to larger, more complex environments.
- **Community and Documentation**: Ollama has a strong community and extensive documentation, providing support and resources for developers t... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d8df65ca-52df-4928-b2ce-2c79275aad13 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 31 | opea-semantic-v1 | 174b9c156c63a6aa | AgentQnA workflow. Ollama's serving container will work alongside existing LLM services like vLLM, TGI, and OpenAI, maintaining the overall functionality and performance of the system.
## Use-case Stories | ai_ref_knowledge | OPEA Documentation | AgentQnA workflow. Ollama's serving container will work alongside existing LLM services like vLLM, TGI, and OpenAI, maintaining the overall functionality and performance of the system.
## Use-case Stories | AgentQnA workflow. Ollama's serving container will work alongside existing LLM services like vLLM, TGI, and OpenAI, maintaining the overall functionality and performance of the system.
## Use-case Stories | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ede5a79a-2e35-4e24-89c1-11064480c169 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 2 | opea-semantic-v1 | 880b5c522d2887ed | ## Objective
### Problems This Will Solve
- **Access to Open-source SLMs on CPU Servers**: Enables the use of open-source SLMs through Ollama on x86 CPU servers. State-of-the-art open-source SLMs are suitable for less complex agent workflows. A critical task for agents is accurately invoking the correct tools for speci... | ai_ref_knowledge | OPEA Documentation | ## Objective
### Problems This Will Solve
- **Access to Open-source SLMs on CPU Servers**: Enables the use of open-source SLMs through Ollama on x86 CPU servers. State-of-the-art open-source SLMs are suitable for less complex agent workflows. A critical task for agents is accurately invoking the correct tools for speci... | ## Objective
### Problems This Will Solve
- **Access to Open-source SLMs on CPU Servers**: Enables the use of open-source SLMs through Ollama on x86 CPU servers. State-of-the-art open-source SLMs are suitable for less complex agent workflows. A critical task for agents is accurately invoking the correct tools for speci... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f5796f1e-a6c6-48f5-bdff-b2bf1a96b86a | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 32 | opea-semantic-v1 | e360850e8e94b35f | ## Use-case Stories
1. **Data Privacy and Security**:
- **Scenario**: A healthcare organization needs to process sensitive patient data for generating medical reports and insights. - **Solution**: By using Ollama service on-prem x86 server CPUs, the organization can run LLM agents locally, ensuring that sensitive pati... | ai_ref_knowledge | OPEA Documentation | ## Use-case Stories
1. **Data Privacy and Security**:
- **Scenario**: A healthcare organization needs to process sensitive patient data for generating medical reports and insights. - **Solution**: By using Ollama service on-prem x86 server CPUs, the organization can run LLM agents locally, ensuring that sensitive pati... | ## Use-case Stories
1. **Data Privacy and Security**:
- **Scenario**: A healthcare organization needs to process sensitive patient data for generating medical reports and insights. - **Solution**: By using Ollama service on-prem x86 server CPUs, the organization can run LLM agents locally, ensuring that sensitive pati... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f66268d4-b741-4fdc-978d-08ece357a9b5 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 17 | opea-semantic-v1 | 12cba7b541c5b2ef | accuracy and efficiency. By adopting these open-source models, the AgentQnA workflow can benefit from cutting-edge technology while maintaining flexibility and control over the deployment environment.
### Cost Efficiency | ai_ref_knowledge | OPEA Documentation | accuracy and efficiency. By adopting these open-source models, the AgentQnA workflow can benefit from cutting-edge technology while maintaining flexibility and control over the deployment environment.
### Cost Efficiency | accuracy and efficiency. By adopting these open-source models, the AgentQnA workflow can benefit from cutting-edge technology while maintaining flexibility and control over the deployment environment.
### Cost Efficiency | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f8feb3b8-0f26-44d7-b1d5-583519d95eee | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 6 | opea-semantic-v1 | d7c26b0840d723cd | workflow continues to function effectively with the new setup. - **Integration of popular serving framework**: Integration of Ollama serving framework in AgentQnA workflow in OPEA.
### Non-Goals | ai_ref_knowledge | OPEA Documentation | workflow continues to function effectively with the new setup. - **Integration of popular serving framework**: Integration of Ollama serving framework in AgentQnA workflow in OPEA.
### Non-Goals | workflow continues to function effectively with the new setup. - **Integration of popular serving framework**: Integration of Ollama serving framework in AgentQnA workflow in OPEA.
### Non-Goals | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
fd1ac120-1fbb-4fc3-b201-fd28da43ac77 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/24-11-25-GenAIExamples-Ollama_support_for_cpu_server.md | unknown | 59bde873-deb0-465c-86ed-29c3c42d8327 | 11 | opea-semantic-v1 | 0683e9abbfcc6044 | incorporate SLMs into their applications. Ollama's model libraries support a wide range of open-source models, ensuring compatibility and ease of use for different use cases.
### Ollama vs vLLM
VLLM is an optimized inference engine designed for high-throughput token generation and efficient memory utilization, making i... | ai_ref_knowledge | OPEA Documentation | incorporate SLMs into their applications. Ollama's model libraries support a wide range of open-source models, ensuring compatibility and ease of use for different use cases.
### Ollama vs vLLM
VLLM is an optimized inference engine designed for high-throughput token generation and efficient memory utilization, making i... | incorporate SLMs into their applications. Ollama's model libraries support a wide range of open-source models, ensuring compatibility and ease of use for different use cases.
### Ollama vs vLLM
VLLM is an optimized inference engine designed for high-throughput token generation and efficient memory utilization, making i... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
08c4cea9-798d-4df5-9bff-c13053c77a11 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-01-10-OPEA-Benchmark.md | unknown | 19919829-d49c-4148-a977-abf336a6c85d | 1 | opea-semantic-v1 | 1e15f706d101d637 | In v1.1, those bechmark scripts are per examples. It causes many duplicated codes and bad user experience.
That is why we have motivation to improve such tool to have an unified entry for perf benchmark. | ai_ref_knowledge | OPEA Documentation | In v1.1, those bechmark scripts are per examples. It causes many duplicated codes and bad user experience.
That is why we have motivation to improve such tool to have an unified entry for perf benchmark. | In v1.1, those bechmark scripts are per examples. It causes many duplicated codes and bad user experience.
That is why we have motivation to improve such tool to have an unified entry for perf benchmark. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
2e78718c-0d05-429d-979b-4bdce8c569ea | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-01-10-OPEA-Benchmark.md | unknown | 19919829-d49c-4148-a977-abf336a6c85d | 11 | opea-semantic-v1 | 7bfd4efe57e4d3e2 | the query number to construct a single batch in serving max_latency: 20 # time to wait before combining incoming requests into a batch, unit milliseconds
benchmark:
# http request behavior related fields
concurrency: [1, 2, 4]
totoal_query_num: [2048, 4096]
duration: [5, 10] # unit minutes
query_num_per_concurrenc... | ai_ref_knowledge | OPEA Documentation | the query number to construct a single batch in serving max_latency: 20 # time to wait before combining incoming requests into a batch, unit milliseconds
benchmark:
# http request behavior related fields
concurrency: [1, 2, 4]
totoal_query_num: [2048, 4096]
duration: [5, 10] # unit minutes
query_num_per_concurrenc... | the query number to construct a single batch in serving max_latency: 20 # time to wait before combining incoming requests into a batch, unit milliseconds
benchmark:
# http request behavior related fields
concurrency: [1, 2, 4]
totoal_query_num: [2048, 4096]
duration: [5, 10] # unit minutes
query_num_per_concurrenc... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
47253f28-cdf6-46f6-a1b6-98824f8353de | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-01-10-OPEA-Benchmark.md | unknown | 19919829-d49c-4148-a977-abf336a6c85d | 13 | opea-semantic-v1 | d31fce31b29178f4 | query_token_size: 128 # if specified, means fixed query token size will be sent out data_ratio: [10%, 20%, ..., 100%] # optional, ratio from query dataset
#advance settings in each component which will impact perf. data_prep: # not target this time
chunk_size: [1024]
chunk_overlap: [1000]
retriver: # not target this... | ai_ref_knowledge | OPEA Documentation | query_token_size: 128 # if specified, means fixed query token size will be sent out data_ratio: [10%, 20%, ..., 100%] # optional, ratio from query dataset
#advance settings in each component which will impact perf. data_prep: # not target this time
chunk_size: [1024]
chunk_overlap: [1000]
retriver: # not target this... | query_token_size: 128 # if specified, means fixed query token size will be sent out data_ratio: [10%, 20%, ..., 100%] # optional, ratio from query dataset
#advance settings in each component which will impact perf. data_prep: # not target this time
chunk_size: [1024]
chunk_overlap: [1000]
retriver: # not target this... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
54bb76d8-828b-4445-88b8-51defb6a364c | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-01-10-OPEA-Benchmark.md | unknown | 19919829-d49c-4148-a977-abf336a6c85d | 4 | opea-semantic-v1 | f158fd6e7ddb3b26 | └── deploy.py │ ├── kubernetes/ │ │ ├── charts.yaml │ │ └── ... │ ├── docker-compose/ │ │ └── compose.yaml │ └── chatqna.py └── ...
## Proposed benchmark script layout | ai_ref_knowledge | OPEA Documentation | └── deploy.py │ ├── kubernetes/ │ │ ├── charts.yaml │ │ └── ... │ ├── docker-compose/ │ │ └── compose.yaml │ └── chatqna.py └── ...
## Proposed benchmark script layout | └── deploy.py │ ├── kubernetes/ │ │ ├── charts.yaml │ │ └── ... │ ├── docker-compose/ │ │ └── compose.yaml │ └── chatqna.py └── ...
## Proposed benchmark script layout | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
68e3d670-37a8-4302-b4b0-271ef8b7b200 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-01-10-OPEA-Benchmark.md | unknown | 19919829-d49c-4148-a977-abf336a6c85d | 2 | opea-semantic-v1 | b3b170768ad62a68 | That is why we have motivation to improve such tool to have an unified entry for perf benchmark.
## Original benchmark script layout | ai_ref_knowledge | OPEA Documentation | That is why we have motivation to improve such tool to have an unified entry for perf benchmark.
## Original benchmark script layout | That is why we have motivation to improve such tool to have an unified entry for perf benchmark.
## Original benchmark script layout | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
92468d03-256e-4850-9ed1-7167bf0ea0d8 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-01-10-OPEA-Benchmark.md | unknown | 19919829-d49c-4148-a977-abf336a6c85d | 7 | opea-semantic-v1 | d6793907b3ac0d79 | The pesudo code of deploy_and_benchmark.py is listed at below for your reference.
# deploy_and_benchmark.py
# below is the pesudo code to demostrate its behavior
#
# def main(yaml_file):
# # extract all deployment combinations from chatqna.yaml deploy section
# deploy_traverse_list = extract_deploy_cfg(yaml_file)
# # f... | ai_ref_knowledge | OPEA Documentation | The pesudo code of deploy_and_benchmark.py is listed at below for your reference.
# deploy_and_benchmark.py
# below is the pesudo code to demostrate its behavior
#
# def main(yaml_file):
# # extract all deployment combinations from chatqna.yaml deploy section
# deploy_traverse_list = extract_deploy_cfg(yaml_file)
# # f... | The pesudo code of deploy_and_benchmark.py is listed at below for your reference.
# deploy_and_benchmark.py
# below is the pesudo code to demostrate its behavior
#
# def main(yaml_file):
# # extract all deployment combinations from chatqna.yaml deploy section
# deploy_traverse_list = extract_deploy_cfg(yaml_file)
# # f... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
9787bb79-4346-4cae-afe8-b9471fd00cca | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-01-10-OPEA-Benchmark.md | unknown | 19919829-d49c-4148-a977-abf336a6c85d | 8 | opea-semantic-v1 | 887b8ce645da8090 | # for deploy_cfg in deploy_traverse_list: # start_k8s_service(deploy_cfg) # for benchmark_cfg in benchmark_traverse_list: # if service_ready: # ingest_dataset(benchmark_cfg.dataset) # send_http_request(benchmark_cfg) # will call stresscli.py in GenAIEval
Taking chatqna as an example, the configurable fields are listed ... | ai_ref_knowledge | OPEA Documentation | # for deploy_cfg in deploy_traverse_list: # start_k8s_service(deploy_cfg) # for benchmark_cfg in benchmark_traverse_list: # if service_ready: # ingest_dataset(benchmark_cfg.dataset) # send_http_request(benchmark_cfg) # will call stresscli.py in GenAIEval
Taking chatqna as an example, the configurable fields are listed ... | # for deploy_cfg in deploy_traverse_list: # start_k8s_service(deploy_cfg) # for benchmark_cfg in benchmark_traverse_list: # if service_ready: # ingest_dataset(benchmark_cfg.dataset) # send_http_request(benchmark_cfg) # will call stresscli.py in GenAIEval
Taking chatqna as an example, the configurable fields are listed ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
9dc8777d-eb79-442d-8dce-c7b52cdaecd8 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-01-10-OPEA-Benchmark.md | unknown | 19919829-d49c-4148-a977-abf336a6c85d | 9 | opea-semantic-v1 | e6d869013561851e | Taking chatqna as an example, the configurable fields are listed at below
# chatqna.yaml
#
# usage:
# 1) deploy_and_benchmark.py --workload chatqna [overrided parameters]
# 2) or deploy_and_benchmark.py ./chatqna/benchmark/chatqna.yaml [overrided parameters]
#
# for example, deploy_and_benchmark.sh ./chatqna/benchmark/... | ai_ref_knowledge | OPEA Documentation | Taking chatqna as an example, the configurable fields are listed at below
# chatqna.yaml
#
# usage:
# 1) deploy_and_benchmark.py --workload chatqna [overrided parameters]
# 2) or deploy_and_benchmark.py ./chatqna/benchmark/chatqna.yaml [overrided parameters]
#
# for example, deploy_and_benchmark.sh ./chatqna/benchmark/... | Taking chatqna as an example, the configurable fields are listed at below
# chatqna.yaml
#
# usage:
# 1) deploy_and_benchmark.py --workload chatqna [overrided parameters]
# 2) or deploy_and_benchmark.py ./chatqna/benchmark/chatqna.yaml [overrided parameters]
#
# for example, deploy_and_benchmark.sh ./chatqna/benchmark/... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
9f5f64b7-844c-4fa3-b1b0-58a60fa415e2 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-01-10-OPEA-Benchmark.md | unknown | 19919829-d49c-4148-a977-abf336a6c85d | 12 | opea-semantic-v1 | f1f2c80b8d7fdb4f | concurrency: [1, 2, 4] totoal_query_num: [2048, 4096] duration: [5, 10] # unit minutes query_num_per_concurrency: [4, 8, 16] possion: True possion_arrival_rate: 1.0 warmup_iterations: 10 seed: 1024
# dataset relted fields
dataset: [dummy_english, dummy_chinese, pub_med100, ...] # predefined keywords for supported data... | ai_ref_knowledge | OPEA Documentation | concurrency: [1, 2, 4] totoal_query_num: [2048, 4096] duration: [5, 10] # unit minutes query_num_per_concurrency: [4, 8, 16] possion: True possion_arrival_rate: 1.0 warmup_iterations: 10 seed: 1024
# dataset relted fields
dataset: [dummy_english, dummy_chinese, pub_med100, ...] # predefined keywords for supported data... | concurrency: [1, 2, 4] totoal_query_num: [2048, 4096] duration: [5, 10] # unit minutes query_num_per_concurrency: [4, 8, 16] possion: True possion_arrival_rate: 1.0 warmup_iterations: 10 seed: 1024
# dataset relted fields
dataset: [dummy_english, dummy_chinese, pub_med100, ...] # predefined keywords for supported data... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
beb685fc-e8ce-4cd4-b049-5718844fef25 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-01-10-OPEA-Benchmark.md | unknown | 19919829-d49c-4148-a977-abf336a6c85d | 10 | opea-semantic-v1 | 58caab01f13b4a0b | # hardware related config device: [xeon, gaudi, ...] # AMD and other h/ws could be extended into here node: [1, 2, 4] cards_per_node: [4, 8]
# components related config, by default is for OOB, if overrided, then it is for tuned version
embedding:
model_id: bge_large_v1.5
instance_num: [2, 4, 8]
cores_per_instance: ... | ai_ref_knowledge | OPEA Documentation | # hardware related config device: [xeon, gaudi, ...] # AMD and other h/ws could be extended into here node: [1, 2, 4] cards_per_node: [4, 8]
# components related config, by default is for OOB, if overrided, then it is for tuned version
embedding:
model_id: bge_large_v1.5
instance_num: [2, 4, 8]
cores_per_instance: ... | # hardware related config device: [xeon, gaudi, ...] # AMD and other h/ws could be extended into here node: [1, 2, 4] cards_per_node: [4, 8]
# components related config, by default is for OOB, if overrided, then it is for tuned version
embedding:
model_id: bge_large_v1.5
instance_num: [2, 4, 8]
cores_per_instance: ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
de25d30b-e125-4321-b549-ed76d6aaffb1 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-01-10-OPEA-Benchmark.md | unknown | 19919829-d49c-4148-a977-abf336a6c85d | 0 | opea-semantic-v1 | c5a3df6b328f28f5 | This RFC is used to describe the behavior of unified benchmark script for GenAIExamples user.
In v1.1, those bechmark scripts are per examples. It causes many duplicated codes and bad user experience. | ai_ref_knowledge | OPEA Documentation | This RFC is used to describe the behavior of unified benchmark script for GenAIExamples user.
In v1.1, those bechmark scripts are per examples. It causes many duplicated codes and bad user experience. | This RFC is used to describe the behavior of unified benchmark script for GenAIExamples user.
In v1.1, those bechmark scripts are per examples. It causes many duplicated codes and bad user experience. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
de2d0381-3c13-411c-8543-32f8438261b8 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-01-10-OPEA-Benchmark.md | unknown | 19919829-d49c-4148-a977-abf336a6c85d | 3 | opea-semantic-v1 | 01b239489b80b78c | ## Original benchmark script layout
GenAIExamples/
├── ChatQnA/
│ ├── benchmark/
│ │ ├── benchmark.sh # each example has its own script
│ │ └── deploy.py
│ ├── kubernetes/
│ │ ├── charts.yaml
│ │ └── ... │ ├── docker-compose/
│ │ └── compose.yaml
│ └── chatqna.py
└── ... | ai_ref_knowledge | OPEA Documentation | ## Original benchmark script layout
GenAIExamples/
├── ChatQnA/
│ ├── benchmark/
│ │ ├── benchmark.sh # each example has its own script
│ │ └── deploy.py
│ ├── kubernetes/
│ │ ├── charts.yaml
│ │ └── ... │ ├── docker-compose/
│ │ └── compose.yaml
│ └── chatqna.py
└── ... | ## Original benchmark script layout
GenAIExamples/
├── ChatQnA/
│ ├── benchmark/
│ │ ├── benchmark.sh # each example has its own script
│ │ └── deploy.py
│ ├── kubernetes/
│ │ ├── charts.yaml
│ │ └── ... │ ├── docker-compose/
│ │ └── compose.yaml
│ └── chatqna.py
└── ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
deed7ef9-a240-412c-8ffa-db0355286af9 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-01-10-OPEA-Benchmark.md | unknown | 19919829-d49c-4148-a977-abf336a6c85d | 6 | opea-semantic-v1 | 0472e54a7d81a47e | for deploy_and_benchmark.py │ ├── kubernetes/ │ │ ├── charts.yaml │ │ └── ... │ |── docker-compose/ │ | └── compose.yaml | └── chatqna.py └── ...
# Design | ai_ref_knowledge | OPEA Documentation | for deploy_and_benchmark.py │ ├── kubernetes/ │ │ ├── charts.yaml │ │ └── ... │ |── docker-compose/ │ | └── compose.yaml | └── chatqna.py └── ...
# Design | for deploy_and_benchmark.py │ ├── kubernetes/ │ │ ├── charts.yaml │ │ └── ... │ |── docker-compose/ │ | └── compose.yaml | └── chatqna.py └── ...
# Design | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e228df34-db33-473b-a2b0-bd7f4aa7abab | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-01-10-OPEA-Benchmark.md | unknown | 19919829-d49c-4148-a977-abf336a6c85d | 5 | opea-semantic-v1 | 8be5120189bb687b | ## Proposed benchmark script layout
GenAIExamples/
├── deploy_and_benchmark.py # main entry of GenAIExamples
├── ChatQnA/
│ ├── chatqna.yaml # default deploy and benchmark config for deploy_and_benchmark.py
│ ├── kubernetes/
│ │ ├── charts.yaml
│ │ └── ... │ |── docker-compose/
│ | └── compose.yaml
| └── chatqna.py
└─... | ai_ref_knowledge | OPEA Documentation | ## Proposed benchmark script layout
GenAIExamples/
├── deploy_and_benchmark.py # main entry of GenAIExamples
├── ChatQnA/
│ ├── chatqna.yaml # default deploy and benchmark config for deploy_and_benchmark.py
│ ├── kubernetes/
│ │ ├── charts.yaml
│ │ └── ... │ |── docker-compose/
│ | └── compose.yaml
| └── chatqna.py
└─... | ## Proposed benchmark script layout
GenAIExamples/
├── deploy_and_benchmark.py # main entry of GenAIExamples
├── ChatQnA/
│ ├── chatqna.yaml # default deploy and benchmark config for deploy_and_benchmark.py
│ ├── kubernetes/
│ │ ├── charts.yaml
│ │ └── ... │ |── docker-compose/
│ | └── compose.yaml
| └── chatqna.py
└─... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8955597f-98cc-4bef-93c0-67206b2fa0d8 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-03-GenAIComponents-001-Routing-Agent.md | unknown | b2ddf49a-3555-4ac8-99ae-94240eb140d4 | 3 | opea-semantic-v1 | a4c671a4c037051d | selection based on query complexity 3. Monitoring: Real-time metrics collection (latency, cost, accuracy) 4. This code is based on RouteLLM, which is available at https://github.com/lm-sys/RouteLLM
### Key Features:
- Dynamic model selection based on query complexity
- Returns the selected model endpoint so that develo... | ai_ref_knowledge | OPEA Documentation | selection based on query complexity 3. Monitoring: Real-time metrics collection (latency, cost, accuracy) 4. This code is based on RouteLLM, which is available at https://github.com/lm-sys/RouteLLM
### Key Features:
- Dynamic model selection based on query complexity
- Returns the selected model endpoint so that develo... | selection based on query complexity 3. Monitoring: Real-time metrics collection (latency, cost, accuracy) 4. This code is based on RouteLLM, which is available at https://github.com/lm-sys/RouteLLM
### Key Features:
- Dynamic model selection based on query complexity
- Returns the selected model endpoint so that develo... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a0c85a70-ede8-4d32-a214-b786b0b3a549 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-03-GenAIComponents-001-Routing-Agent.md | unknown | b2ddf49a-3555-4ac8-99ae-94240eb140d4 | 0 | opea-semantic-v1 | 242838b71c4ace38 | ## Status Proposed
## Objective
Create an intelligent routing layer that:
- Analyzes text-based input queries in real-time. - Selects optimal model based on criteria like cost, latency, and capability requirements
- Supports multiple cloud providers and self-hosted models | ai_ref_knowledge | OPEA Documentation | ## Status Proposed
## Objective
Create an intelligent routing layer that:
- Analyzes text-based input queries in real-time. - Selects optimal model based on criteria like cost, latency, and capability requirements
- Supports multiple cloud providers and self-hosted models | ## Status Proposed
## Objective
Create an intelligent routing layer that:
- Analyzes text-based input queries in real-time. - Selects optimal model based on criteria like cost, latency, and capability requirements
- Supports multiple cloud providers and self-hosted models | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c3fbee3e-62fe-42b6-b371-db829181369b | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-03-GenAIComponents-001-Routing-Agent.md | unknown | b2ddf49a-3555-4ac8-99ae-94240eb140d4 | 1 | opea-semantic-v1 | 8494890ba894c5ba | input queries in real-time. - Selects optimal model based on criteria like cost, latency, and capability requirements - Supports multiple cloud providers and self-hosted models
## Motivation
- Growing complexity of multi-LLM environments
- Need for cost-efficient inference without sacrificing quality
- Lack of standard... | ai_ref_knowledge | OPEA Documentation | input queries in real-time. - Selects optimal model based on criteria like cost, latency, and capability requirements - Supports multiple cloud providers and self-hosted models
## Motivation
- Growing complexity of multi-LLM environments
- Need for cost-efficient inference without sacrificing quality
- Lack of standard... | input queries in real-time. - Selects optimal model based on criteria like cost, latency, and capability requirements - Supports multiple cloud providers and self-hosted models
## Motivation
- Growing complexity of multi-LLM environments
- Need for cost-efficient inference without sacrificing quality
- Lack of standard... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d5b41358-6441-4e8c-8658-c610dd525d13 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-03-GenAIComponents-001-Routing-Agent.md | unknown | b2ddf49a-3555-4ac8-99ae-94240eb140d4 | 4 | opea-semantic-v1 | ce375a200d8e9f5f | developer can call proper model, or does actual routing to the chosen model so this process is invisible to the developer - Cost-aware routing policies
## Miscellaneous
- Performance: <5ms overhead per request
- Security: Zero-trust authentication between components
- Staging Plan:
1. Phase 1: Basic routing MVP
2. Ph... | ai_ref_knowledge | OPEA Documentation | developer can call proper model, or does actual routing to the chosen model so this process is invisible to the developer - Cost-aware routing policies
## Miscellaneous
- Performance: <5ms overhead per request
- Security: Zero-trust authentication between components
- Staging Plan:
1. Phase 1: Basic routing MVP
2. Ph... | developer can call proper model, or does actual routing to the chosen model so this process is invisible to the developer - Cost-aware routing policies
## Miscellaneous
- Performance: <5ms overhead per request
- Security: Zero-trust authentication between components
- Staging Plan:
1. Phase 1: Basic routing MVP
2. Ph... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d9ed594c-365a-4740-adfa-fde869cca201 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-03-GenAIComponents-001-Routing-Agent.md | unknown | b2ddf49a-3555-4ac8-99ae-94240eb140d4 | 2 | opea-semantic-v1 | 466d13b01561e629 | complexity of multi-LLM environments - Need for cost-efficient inference without sacrificing quality - Lack of standardized orchestration patterns - Increasing demand for hybrid cloud/on-prem deployments
## Design Proposal
### Core Components:
1. Query Analyzer: Supports several known classifiers (matrix factorization,... | ai_ref_knowledge | OPEA Documentation | complexity of multi-LLM environments - Need for cost-efficient inference without sacrificing quality - Lack of standardized orchestration patterns - Increasing demand for hybrid cloud/on-prem deployments
## Design Proposal
### Core Components:
1. Query Analyzer: Supports several known classifiers (matrix factorization,... | complexity of multi-LLM environments - Need for cost-efficient inference without sacrificing quality - Lack of standardized orchestration patterns - Increasing demand for hybrid cloud/on-prem deployments
## Design Proposal
### Core Components:
1. Query Analyzer: Supports several known classifiers (matrix factorization,... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
08f6a05d-aa07-4e3b-8639-6c809d097c39 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-07-OPEA-001-OIM-Operator.md | unknown | c5e0ff20-b210-4d27-ba54-3504c8004e04 | 9 | opea-semantic-v1 | 45db5f830f679d72 | preventing failures on resource contention). We do anticipate constant enhancements and new offerings in the model serving space and a steady influx of new models.
The following diagram shows example of the operator main use case interactions; model caching, inference service creation and usage. | ai_ref_knowledge | OPEA Documentation | preventing failures on resource contention). We do anticipate constant enhancements and new offerings in the model serving space and a steady influx of new models.
The following diagram shows example of the operator main use case interactions; model caching, inference service creation and usage. | preventing failures on resource contention). We do anticipate constant enhancements and new offerings in the model serving space and a steady influx of new models.
The following diagram shows example of the operator main use case interactions; model caching, inference service creation and usage. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1edb9152-cba1-4d53-aabd-ca791ea80d53 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-07-OPEA-001-OIM-Operator.md | unknown | c5e0ff20-b210-4d27-ba54-3504c8004e04 | 3 | opea-semantic-v1 | 44242f981056a23c | ## Motivation
Nvidia delivers this ease of use through tooling that first detects the runtime hardware, which is then used to filter for profiles matching the desired model and KPIs. A profile is a recipe specifying how to launch an inference service. It specifies not only which model server framework (such as TGI or v... | ai_ref_knowledge | OPEA Documentation | ## Motivation
Nvidia delivers this ease of use through tooling that first detects the runtime hardware, which is then used to filter for profiles matching the desired model and KPIs. A profile is a recipe specifying how to launch an inference service. It specifies not only which model server framework (such as TGI or v... | ## Motivation
Nvidia delivers this ease of use through tooling that first detects the runtime hardware, which is then used to filter for profiles matching the desired model and KPIs. A profile is a recipe specifying how to launch an inference service. It specifies not only which model server framework (such as TGI or v... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
229d3c42-5eee-4d9a-a579-0501619614e9 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-07-OPEA-001-OIM-Operator.md | unknown | c5e0ff20-b210-4d27-ba54-3504c8004e04 | 15 | opea-semantic-v1 | bc3543f39ead9ec1 | 5) Publishing the profiles and other artifacts to an OCI compatible registry, such as Docker Hub or ghcr.io.
## Alternatives Considered | ai_ref_knowledge | OPEA Documentation | 5) Publishing the profiles and other artifacts to an OCI compatible registry, such as Docker Hub or ghcr.io.
## Alternatives Considered | 5) Publishing the profiles and other artifacts to an OCI compatible registry, such as Docker Hub or ghcr.io.
## Alternatives Considered | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
2e12a45b-8670-4ce0-89c3-bbf0d169c444 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-07-OPEA-001-OIM-Operator.md | unknown | c5e0ff20-b210-4d27-ba54-3504c8004e04 | 16 | opea-semantic-v1 | ddd3e9ba7c1cc49f | ## Alternatives Considered
Creating everything from scratch was rejected because an open-source alternative exists and provides most of the features OPEA project needs. | ai_ref_knowledge | OPEA Documentation | ## Alternatives Considered
Creating everything from scratch was rejected because an open-source alternative exists and provides most of the features OPEA project needs. | ## Alternatives Considered
Creating everything from scratch was rejected because an open-source alternative exists and provides most of the features OPEA project needs. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
59a9c374-0977-4529-9a76-8a011451e9c3 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-07-OPEA-001-OIM-Operator.md | unknown | c5e0ff20-b210-4d27-ba54-3504c8004e04 | 6 | opea-semantic-v1 | b7124e91c3ad84f9 | ## Design Proposal
We seek to offer a similar ease of use experience in OPEA for the inference services. Nvidia’s open source NIM Operator is a tool that eases launching inference services. OPEA plans to extend NIM operator, or similar, to deliver an Operator that provides similar functionality but also supports multi-... | ai_ref_knowledge | OPEA Documentation | ## Design Proposal
We seek to offer a similar ease of use experience in OPEA for the inference services. Nvidia’s open source NIM Operator is a tool that eases launching inference services. OPEA plans to extend NIM operator, or similar, to deliver an Operator that provides similar functionality but also supports multi-... | ## Design Proposal
We seek to offer a similar ease of use experience in OPEA for the inference services. Nvidia’s open source NIM Operator is a tool that eases launching inference services. OPEA plans to extend NIM operator, or similar, to deliver an Operator that provides similar functionality but also supports multi-... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
97e17d97-5fe9-495d-992b-070aaf7ca637 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-07-OPEA-001-OIM-Operator.md | unknown | c5e0ff20-b210-4d27-ba54-3504c8004e04 | 14 | opea-semantic-v1 | 31416f9df86f2f0d | 4) Providing an open-source tool, such as [ORAS](https://oras.land/), to discover and list available profiles/configurations for a model.
5) Publishing the profiles and other artifacts to an OCI compatible registry, such as Docker Hub or ghcr.io. | ai_ref_knowledge | OPEA Documentation | 4) Providing an open-source tool, such as [ORAS](https://oras.land/), to discover and list available profiles/configurations for a model.
5) Publishing the profiles and other artifacts to an OCI compatible registry, such as Docker Hub or ghcr.io. | 4) Providing an open-source tool, such as [ORAS](https://oras.land/), to discover and list available profiles/configurations for a model.
5) Publishing the profiles and other artifacts to an OCI compatible registry, such as Docker Hub or ghcr.io. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
9f1a1acc-9451-44f3-a324-172742c6f924 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-07-OPEA-001-OIM-Operator.md | unknown | c5e0ff20-b210-4d27-ba54-3504c8004e04 | 10 | opea-semantic-v1 | 407725cec82728e4 | The following diagram shows example of the operator main use case interactions; model caching, inference service creation and usage.
 | ai_ref_knowledge | OPEA Documentation | The following diagram shows example of the operator main use case interactions; model caching, inference service creation and usage.
 | The following diagram shows example of the operator main use case interactions; model caching, inference service creation and usage.
 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a46f9c7c-8d3e-49ee-bd40-5605064dd9b3 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-07-OPEA-001-OIM-Operator.md | unknown | c5e0ff20-b210-4d27-ba54-3504c8004e04 | 2 | opea-semantic-v1 | a571b8ac40716d0e | away both the need to choose a model serving framework and determining its optimum parameters for the same to achieve the KPIs on provided hardware.
## Motivation | ai_ref_knowledge | OPEA Documentation | away both the need to choose a model serving framework and determining its optimum parameters for the same to achieve the KPIs on provided hardware.
## Motivation | away both the need to choose a model serving framework and determining its optimum parameters for the same to achieve the KPIs on provided hardware.
## Motivation | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b9f2b70f-bf88-4305-bd41-15917528ce11 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-07-OPEA-001-OIM-Operator.md | unknown | c5e0ff20-b210-4d27-ba54-3504c8004e04 | 8 | opea-semantic-v1 | bcb66e67e5481c82 | providers of AI platforms that operate on a multitude of hardware platforms will find the HW optimized profiles valuable in providing cost-effective performant inference services.
With regard to profiles we shall start with supporting vLLM as the model framework and build profiles for a few popular models. Profiles may... | ai_ref_knowledge | OPEA Documentation | providers of AI platforms that operate on a multitude of hardware platforms will find the HW optimized profiles valuable in providing cost-effective performant inference services.
With regard to profiles we shall start with supporting vLLM as the model framework and build profiles for a few popular models. Profiles may... | providers of AI platforms that operate on a multitude of hardware platforms will find the HW optimized profiles valuable in providing cost-effective performant inference services.
With regard to profiles we shall start with supporting vLLM as the model framework and build profiles for a few popular models. Profiles may... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
be6bc197-f693-452e-8b59-c5df1dfd88fa | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-07-OPEA-001-OIM-Operator.md | unknown | c5e0ff20-b210-4d27-ba54-3504c8004e04 | 1 | opea-semantic-v1 | 7b9a3f4fc7ca34fe | latency or throughput or quantization, in addition to the hardware they want to run on. Can we abstract some of this complexity for inference services?
Nvidia's NIM does just this. It expects a user to only specify a model and what they would like to optimize for, their key performance indicator (KPI) type, be it low l... | ai_ref_knowledge | OPEA Documentation | latency or throughput or quantization, in addition to the hardware they want to run on. Can we abstract some of this complexity for inference services?
Nvidia's NIM does just this. It expects a user to only specify a model and what they would like to optimize for, their key performance indicator (KPI) type, be it low l... | latency or throughput or quantization, in addition to the hardware they want to run on. Can we abstract some of this complexity for inference services?
Nvidia's NIM does just this. It expects a user to only specify a model and what they would like to optimize for, their key performance indicator (KPI) type, be it low l... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d4d7683c-3da2-431e-8384-a03e3afec46a | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-07-OPEA-001-OIM-Operator.md | unknown | c5e0ff20-b210-4d27-ba54-3504c8004e04 | 13 | opea-semantic-v1 | 02a383b75f93f252 | allocations for a given model and constructing profile specific configurations. These studies will be repeated to take advantage of new models, frameworks, and other enhancements.
4) Providing an open-source tool, such as [ORAS](https://oras.land/), to discover and list available profiles/configurations for a model. | ai_ref_knowledge | OPEA Documentation | allocations for a given model and constructing profile specific configurations. These studies will be repeated to take advantage of new models, frameworks, and other enhancements.
4) Providing an open-source tool, such as [ORAS](https://oras.land/), to discover and list available profiles/configurations for a model. | allocations for a given model and constructing profile specific configurations. These studies will be repeated to take advantage of new models, frameworks, and other enhancements.
4) Providing an open-source tool, such as [ORAS](https://oras.land/), to discover and list available profiles/configurations for a model. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
dceced56-b537-4150-b948-ed93925df1c1 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-07-OPEA-001-OIM-Operator.md | unknown | c5e0ff20-b210-4d27-ba54-3504c8004e04 | 7 | opea-semantic-v1 | 4f7c3b84318204cb | and potentially integrating with platforms such as KServe and RayAI. The first release will provide minimal functionality and over time with community contributions grow richer.
We anticipate providers of AI platforms that operate on a multitude of hardware platforms will find the HW optimized profiles valuable in prov... | ai_ref_knowledge | OPEA Documentation | and potentially integrating with platforms such as KServe and RayAI. The first release will provide minimal functionality and over time with community contributions grow richer.
We anticipate providers of AI platforms that operate on a multitude of hardware platforms will find the HW optimized profiles valuable in prov... | and potentially integrating with platforms such as KServe and RayAI. The first release will provide minimal functionality and over time with community contributions grow richer.
We anticipate providers of AI platforms that operate on a multitude of hardware platforms will find the HW optimized profiles valuable in prov... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e96787e7-214d-4931-8d6a-a981970443f0 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-07-OPEA-001-OIM-Operator.md | unknown | c5e0ff20-b210-4d27-ba54-3504c8004e04 | 5 | opea-semantic-v1 | 0173c409063555c8 | model frameworks are explored on different hardware to determine settings providing the best performance for a given KPIs. Constructing profiles is non-trivial, resource intensive work.
## Design Proposal | ai_ref_knowledge | OPEA Documentation | model frameworks are explored on different hardware to determine settings providing the best performance for a given KPIs. Constructing profiles is non-trivial, resource intensive work.
## Design Proposal | model frameworks are explored on different hardware to determine settings providing the best performance for a given KPIs. Constructing profiles is non-trivial, resource intensive work.
## Design Proposal | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
eae2bb5d-92ae-4fb3-968e-1fb1ce90fc0d | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-07-OPEA-001-OIM-Operator.md | unknown | c5e0ff20-b210-4d27-ba54-3504c8004e04 | 4 | opea-semantic-v1 | 78314d248ef439da | cards, amount of memory, degree of tensor parallelism, batch size etc. Lastly the profile is used to launch the inference service on the given hardware.
The profiles are established through offline experimentation using tools like MLPerf. For a model, one or more model frameworks are explored on different hardware to d... | ai_ref_knowledge | OPEA Documentation | cards, amount of memory, degree of tensor parallelism, batch size etc. Lastly the profile is used to launch the inference service on the given hardware.
The profiles are established through offline experimentation using tools like MLPerf. For a model, one or more model frameworks are explored on different hardware to d... | cards, amount of memory, degree of tensor parallelism, batch size etc. Lastly the profile is used to launch the inference service on the given hardware.
The profiles are established through offline experimentation using tools like MLPerf. For a model, one or more model frameworks are explored on different hardware to d... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ec9bc27f-eb18-457f-af0f-5a8910fa081f | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-07-OPEA-001-OIM-Operator.md | unknown | c5e0ff20-b210-4d27-ba54-3504c8004e04 | 11 | opea-semantic-v1 | 23abcce6bdf454f4 | 1) Auto-detecting hardware type and what is allocated.
2) Taking the user specified model and profile (if not specified using a default profile) to retrieve configurations that are possible on the allocated hardware type and quantity. For instance, a configuration that leverages tensor parallelism is infeasible if only... | ai_ref_knowledge | OPEA Documentation | 1) Auto-detecting hardware type and what is allocated.
2) Taking the user specified model and profile (if not specified using a default profile) to retrieve configurations that are possible on the allocated hardware type and quantity. For instance, a configuration that leverages tensor parallelism is infeasible if only... | 1) Auto-detecting hardware type and what is allocated.
2) Taking the user specified model and profile (if not specified using a default profile) to retrieve configurations that are possible on the allocated hardware type and quantity. For instance, a configuration that leverages tensor parallelism is infeasible if only... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f12de6f2-0d28-47c7-8f56-978b36bfae43 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-07-OPEA-001-OIM-Operator.md | unknown | c5e0ff20-b210-4d27-ba54-3504c8004e04 | 0 | opea-semantic-v1 | edaad9740f5a81a1 | ## Objective
OPEA seeks to ease enterprise GenAI adoption in a landscape that is fast moving, has skills gaps, and is cost conscious. A look at OPEA's GenAIExamples illustrates the choices a user must make: whether to use Docker or Kubernetes, the model serving framework, whether to optimize for latency or throughput o... | ai_ref_knowledge | OPEA Documentation | ## Objective
OPEA seeks to ease enterprise GenAI adoption in a landscape that is fast moving, has skills gaps, and is cost conscious. A look at OPEA's GenAIExamples illustrates the choices a user must make: whether to use Docker or Kubernetes, the model serving framework, whether to optimize for latency or throughput o... | ## Objective
OPEA seeks to ease enterprise GenAI adoption in a landscape that is fast moving, has skills gaps, and is cost conscious. A look at OPEA's GenAIExamples illustrates the choices a user must make: whether to use Docker or Kubernetes, the model serving framework, whether to optimize for latency or throughput o... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
fd30f76f-ead3-40be-97cd-7dc2a340b40c | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-07-OPEA-001-OIM-Operator.md | unknown | c5e0ff20-b210-4d27-ba54-3504c8004e04 | 17 | opea-semantic-v1 | c93ba85ef96d5dc7 | Creating everything from scratch was rejected because an open-source alternative exists and provides most of the features OPEA project needs.
## Compatibility | ai_ref_knowledge | OPEA Documentation | Creating everything from scratch was rejected because an open-source alternative exists and provides most of the features OPEA project needs.
## Compatibility | Creating everything from scratch was rejected because an open-source alternative exists and provides most of the features OPEA project needs.
## Compatibility | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ff29b042-9708-411a-9741-ad9fb202e545 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-07-OPEA-001-OIM-Operator.md | unknown | c5e0ff20-b210-4d27-ba54-3504c8004e04 | 12 | opea-semantic-v1 | 61fa061b2fd70d5e | possible on the allocated hardware type and quantity. For instance, a configuration that leverages tensor parallelism is infeasible if only one GPU card is allocated.
3) Automating performance studies to identify optimum settings for a given hardware with various resource allocations for a given model and constructing ... | ai_ref_knowledge | OPEA Documentation | possible on the allocated hardware type and quantity. For instance, a configuration that leverages tensor parallelism is infeasible if only one GPU card is allocated.
3) Automating performance studies to identify optimum settings for a given hardware with various resource allocations for a given model and constructing ... | possible on the allocated hardware type and quantity. For instance, a configuration that leverages tensor parallelism is infeasible if only one GPU card is allocated.
3) Automating performance studies to identify optimum settings for a given hardware with various resource allocations for a given model and constructing ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
011f1d51-fae4-4be8-b4ba-63df377e4a94 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 5 | opea-semantic-v1 | a6a0e4c8fa99c0ba | The Auto-fix step happens in agent service, and the lint check and execution steps are executed by external tools.
By introducing these agents, the system ensures that only valid code is passed to the LLM and that generated code is verified before reaching the user, thereby improving the overall efficiency and accuracy... | ai_ref_knowledge | OPEA Documentation | The Auto-fix step happens in agent service, and the lint check and execution steps are executed by external tools.
By introducing these agents, the system ensures that only valid code is passed to the LLM and that generated code is verified before reaching the user, thereby improving the overall efficiency and accuracy... | The Auto-fix step happens in agent service, and the lint check and execution steps are executed by external tools.
By introducing these agents, the system ensures that only valid code is passed to the LLM and that generated code is verified before reaching the user, thereby improving the overall efficiency and accuracy... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0268c0ff-3057-44ba-952a-b0c1e6840733 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 29 | opea-semantic-v1 | 02b5c22c83852882 | LintTool([Lint Tool]):::blue CodeExecutionTool([Sandbox Execution Tool]):::blue Output[Translated Code]:::orchid
Frontend -->|Send Request| Megaservice
Megaservice -->|Send Code| Agent1
Agent1 --> |Lint check| LintTool
Agent1 -->|Validated input code| CodeExecutionTool
Agent2 --> |Lint check| LintTool
Agent2 -->|V... | ai_ref_knowledge | OPEA Documentation | LintTool([Lint Tool]):::blue CodeExecutionTool([Sandbox Execution Tool]):::blue Output[Translated Code]:::orchid
Frontend -->|Send Request| Megaservice
Megaservice -->|Send Code| Agent1
Agent1 --> |Lint check| LintTool
Agent1 -->|Validated input code| CodeExecutionTool
Agent2 --> |Lint check| LintTool
Agent2 -->|V... | LintTool([Lint Tool]):::blue CodeExecutionTool([Sandbox Execution Tool]):::blue Output[Translated Code]:::orchid
Frontend -->|Send Request| Megaservice
Megaservice -->|Send Code| Agent1
Agent1 --> |Lint check| LintTool
Agent1 -->|Validated input code| CodeExecutionTool
Agent2 --> |Lint check| LintTool
Agent2 -->|V... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
04e84189-295e-487d-b181-33de12e8eaf6 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 43 | opea-semantic-v1 | 9c6017a925d72b39 | avoiding bugs. | [link](https://eslint.org/docs/latest/use/getting-started) | | Java | Checkstyle | A development tool to help programmers write Java code that adheres to a coding standard.
| [link](https://checkstyle.sourceforge.io/index.html) |
| C++ | cpplint | A command-line tool to check C/C++ files for style issu... | ai_ref_knowledge | OPEA Documentation | avoiding bugs. | [link](https://eslint.org/docs/latest/use/getting-started) | | Java | Checkstyle | A development tool to help programmers write Java code that adheres to a coding standard.
| [link](https://checkstyle.sourceforge.io/index.html) |
| C++ | cpplint | A command-line tool to check C/C++ files for style issu... | avoiding bugs. | [link](https://eslint.org/docs/latest/use/getting-started) | | Java | Checkstyle | A development tool to help programmers write Java code that adheres to a coding standard.
| [link](https://checkstyle.sourceforge.io/index.html) |
| C++ | cpplint | A command-line tool to check C/C++ files for style issu... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
051b00b3-fbe0-42c5-9129-fe849a3fe052 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 46 | opea-semantic-v1 | ecfc79934cd4793e | # prepare file paths for Java checkstyle CHECKSTYLE_JAR="./checkstyle.jar" CHECKSTYLE_CONFIG="./google_checks.xml"
if [[ ! -f "$SOURCE_FILE" ]]; then
echo "Source file not found: $SOURCE_FILE"
exit 1
fi | ai_ref_knowledge | OPEA Documentation | # prepare file paths for Java checkstyle CHECKSTYLE_JAR="./checkstyle.jar" CHECKSTYLE_CONFIG="./google_checks.xml"
if [[ ! -f "$SOURCE_FILE" ]]; then
echo "Source file not found: $SOURCE_FILE"
exit 1
fi | # prepare file paths for Java checkstyle CHECKSTYLE_JAR="./checkstyle.jar" CHECKSTYLE_CONFIG="./google_checks.xml"
if [[ ! -f "$SOURCE_FILE" ]]; then
echo "Source file not found: $SOURCE_FILE"
exit 1
fi | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
05348a95-afb7-4dfa-81c4-49237d1e9f28 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 57 | opea-semantic-v1 | 24905cdfdbbdedee | code = "xxx" tree = ast.parse(code) # analyze each node in ast tree for node in ast.walk(tree): # Do import checks
* Install dependencies automatically | ai_ref_knowledge | OPEA Documentation | code = "xxx" tree = ast.parse(code) # analyze each node in ast tree for node in ast.walk(tree): # Do import checks
* Install dependencies automatically | code = "xxx" tree = ast.parse(code) # analyze each node in ast tree for node in ast.walk(tree): # Do import checks
* Install dependencies automatically | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0a6f3d82-0aaf-41ba-b09a-c34a8aacf9e0 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 68 | opea-semantic-v1 | 74e84244d141ff03 | is per-user session * Code execution failing translation due to limits / sandboxing / dependency being offline * Workaround: user disables code execution / linting
## Implementation Plan | ai_ref_knowledge | OPEA Documentation | is per-user session * Code execution failing translation due to limits / sandboxing / dependency being offline * Workaround: user disables code execution / linting
## Implementation Plan | is per-user session * Code execution failing translation due to limits / sandboxing / dependency being offline * Workaround: user disables code execution / linting
## Implementation Plan | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0efd223a-b7f0-4826-a3df-bb998b072122 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 34 | opea-semantic-v1 | f7f1dd436e068807 | - If the errors cannot be resolved, the agent returns an error message, prompting the user to review and manually fix the code before proceeding.
LLM Microservice: | ai_ref_knowledge | OPEA Documentation | - If the errors cannot be resolved, the agent returns an error message, prompting the user to review and manually fix the code before proceeding.
LLM Microservice: | - If the errors cannot be resolved, the agent returns an error message, prompting the user to review and manually fix the code before proceeding.
LLM Microservice: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1182d156-b369-435f-b11e-45135cc2a084 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 59 | opea-semantic-v1 | 9d398159dedb2388 | execution tool need to support extracting and installing dependencies from source code automatically. * Use AST here to extract the `import`/`from xxx import xxx` libraries.
```python
import ast | ai_ref_knowledge | OPEA Documentation | execution tool need to support extracting and installing dependencies from source code automatically. * Use AST here to extract the `import`/`from xxx import xxx` libraries.
```python
import ast | execution tool need to support extracting and installing dependencies from source code automatically. * Use AST here to extract the `import`/`from xxx import xxx` libraries.
```python
import ast | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
136ff603-1ae6-4776-a2b6-783b509c6050 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 31 | opea-semantic-v1 | d826039eee7dd1c6 | UI Components:
- Lint Check Button: Select to do lint check for input/output codes. - Code Execution Button: Select to execute code for functionality check. (Support Python only)
- Input/output case: if `Code Execution Button` is selected, use will need to provide a set of input/output for this piece of code. - Code Bo... | ai_ref_knowledge | OPEA Documentation | UI Components:
- Lint Check Button: Select to do lint check for input/output codes. - Code Execution Button: Select to execute code for functionality check. (Support Python only)
- Input/output case: if `Code Execution Button` is selected, use will need to provide a set of input/output for this piece of code. - Code Bo... | UI Components:
- Lint Check Button: Select to do lint check for input/output codes. - Code Execution Button: Select to execute code for functionality check. (Support Python only)
- Input/output case: if `Code Execution Button` is selected, use will need to provide a set of input/output for this piece of code. - Code Bo... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
198705f2-20db-4853-9a5f-72629330ec4a | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 21 | opea-semantic-v1 | ed0d72401b9f8427 | repeatedly generate faulty code, leading to an endless loop of failed executions and retries. Without a safeguard, this could waste computation resources and frustrate users.
How the CodeTrans Helps: | ai_ref_knowledge | OPEA Documentation | repeatedly generate faulty code, leading to an endless loop of failed executions and retries. Without a safeguard, this could waste computation resources and frustrate users.
How the CodeTrans Helps: | repeatedly generate faulty code, leading to an endless loop of failed executions and retries. Without a safeguard, this could waste computation resources and frustrate users.
How the CodeTrans Helps: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1a96f47d-cf02-414f-a863-da42e5385022 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 70 | opea-semantic-v1 | b790482b219cacf1 | execution environment. - Implement the Pre-LLM Agent for input code validation. - Improve UI integration by providing a code execution interface and displaying execution results.
### Phase 3: Agent Integration, target v1.4 | ai_ref_knowledge | OPEA Documentation | execution environment. - Implement the Pre-LLM Agent for input code validation. - Improve UI integration by providing a code execution interface and displaying execution results.
### Phase 3: Agent Integration, target v1.4 | execution environment. - Implement the Pre-LLM Agent for input code validation. - Improve UI integration by providing a code execution interface and displaying execution results.
### Phase 3: Agent Integration, target v1.4 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1cfe66ad-9936-40f3-943d-cf7abf8a714e | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 54 | opea-semantic-v1 | dc45868a2fb1c5ee | * Prevent code injection
* Use Python Abstract Syntax Tree (AST) to detect and block dangerous operations such as `import os`, `exec`, and `__import__`. | ai_ref_knowledge | OPEA Documentation | * Prevent code injection
* Use Python Abstract Syntax Tree (AST) to detect and block dangerous operations such as `import os`, `exec`, and `__import__`. | * Prevent code injection
* Use Python Abstract Syntax Tree (AST) to detect and block dangerous operations such as `import os`, `exec`, and `__import__`. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1d5c5043-f93f-4a84-92d5-d7c8e8eff93b | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 0 | opea-semantic-v1 | 3204643ed2c19742 | ## Objective
This RFC proposes the integration of two Agent mechanisms into the CodeTrans Example to enhance the reliability, user experience, and code quality. The goal is to minimize the propagation of erroneous code and improve the feasibility of automated code translation. | ai_ref_knowledge | OPEA Documentation | ## Objective
This RFC proposes the integration of two Agent mechanisms into the CodeTrans Example to enhance the reliability, user experience, and code quality. The goal is to minimize the propagation of erroneous code and improve the feasibility of automated code translation. | ## Objective
This RFC proposes the integration of two Agent mechanisms into the CodeTrans Example to enhance the reliability, user experience, and code quality. The goal is to minimize the propagation of erroneous code and improve the feasibility of automated code translation. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1d5f85a0-f21d-446f-bd24-6c67a70a0eec | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 51 | opea-semantic-v1 | 2d00a33809c3b824 | go) echo "Running go vet..." go vet "$SOURCE_FILE" > "$REPORT_FILE" 2>&1 ;;
cpp)
echo "Running cpplint..."
cpplint "$SOURCE_FILE" > "$REPORT_FILE" 2>&1
;; | ai_ref_knowledge | OPEA Documentation | go) echo "Running go vet..." go vet "$SOURCE_FILE" > "$REPORT_FILE" 2>&1 ;;
cpp)
echo "Running cpplint..."
cpplint "$SOURCE_FILE" > "$REPORT_FILE" 2>&1
;; | go) echo "Running go vet..." go vet "$SOURCE_FILE" > "$REPORT_FILE" 2>&1 ;;
cpp)
echo "Running cpplint..."
cpplint "$SOURCE_FILE" > "$REPORT_FILE" 2>&1
;; | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
22ba4396-22e3-4ab0-bbe0-9ebd296e9e33 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 18 | opea-semantic-v1 | bde80259d68088a0 | error details and sends them back to the LLM. - The LLM then retries code generation, using the error context to produce a corrected version.
This automated validation ensures that developers receive functional translations without having to manually test and debug every output. | ai_ref_knowledge | OPEA Documentation | error details and sends them back to the LLM. - The LLM then retries code generation, using the error context to produce a corrected version.
This automated validation ensures that developers receive functional translations without having to manually test and debug every output. | error details and sends them back to the LLM. - The LLM then retries code generation, using the error context to produce a corrected version.
This automated validation ensures that developers receive functional translations without having to manually test and debug every output. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
25892989-a344-4541-b34a-1a526b309de7 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 19 | opea-semantic-v1 | 80e03db98f43ca3c | This automated validation ensures that developers receive functional translations without having to manually test and debug every output.
### Preventing Infinite Regeneration Loops | ai_ref_knowledge | OPEA Documentation | This automated validation ensures that developers receive functional translations without having to manually test and debug every output.
### Preventing Infinite Regeneration Loops | This automated validation ensures that developers receive functional translations without having to manually test and debug every output.
### Preventing Infinite Regeneration Loops | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
2600f122-4184-434a-b436-f5a92b8dcee5 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 66 | opea-semantic-v1 | 42d1929e33d22249 | | | Secure Execution Environment | Protects the system from malicious code. | | Error Classification | Identifies syntax, logic errors for better debugging. |
## Risks and Mitigations / User Workarounds | ai_ref_knowledge | OPEA Documentation | | | Secure Execution Environment | Protects the system from malicious code. | | Error Classification | Identifies syntax, logic errors for better debugging. |
## Risks and Mitigations / User Workarounds | | | Secure Execution Environment | Protects the system from malicious code. | | Error Classification | Identifies syntax, logic errors for better debugging. |
## Risks and Mitigations / User Workarounds | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
3460df85-408b-4ce2-ba9b-72523f401193 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 11 | opea-semantic-v1 | 35a005923212ec59 | Scenario:
A developer wants to convert a Java script to Python but unknowingly provides code with syntax errors. If the faulty code is passed directly to the LLM, it might generate an incorrect or non-functional Python version. | ai_ref_knowledge | OPEA Documentation | Scenario:
A developer wants to convert a Java script to Python but unknowingly provides code with syntax errors. If the faulty code is passed directly to the LLM, it might generate an incorrect or non-functional Python version. | Scenario:
A developer wants to convert a Java script to Python but unknowingly provides code with syntax errors. If the faulty code is passed directly to the LLM, it might generate an incorrect or non-functional Python version. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
36719c95-087c-4c32-912c-e2d584ac38e0 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 16 | opea-semantic-v1 | 41fde8facc758775 | a Python version, but there's no guarantee that it runs correctly. Without validation, the developer would have to manually check for errors, which is time-consuming.
How the CodeTrans Helps: | ai_ref_knowledge | OPEA Documentation | a Python version, but there's no guarantee that it runs correctly. Without validation, the developer would have to manually check for errors, which is time-consuming.
How the CodeTrans Helps: | a Python version, but there's no guarantee that it runs correctly. Without validation, the developer would have to manually check for errors, which is time-consuming.
How the CodeTrans Helps: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
39bc2f97-d0dc-41f0-a7fa-f377498949f5 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 1 | opea-semantic-v1 | 21a13332d66f9f43 | the reliability, user experience, and code quality. The goal is to minimize the propagation of erroneous code and improve the feasibility of automated code translation.
- Pre-LLM Agent: Validates the correctness of the input code before it is processed by the LLM. If errors are detected, the agent attempts to automatic... | ai_ref_knowledge | OPEA Documentation | the reliability, user experience, and code quality. The goal is to minimize the propagation of erroneous code and improve the feasibility of automated code translation.
- Pre-LLM Agent: Validates the correctness of the input code before it is processed by the LLM. If errors are detected, the agent attempts to automatic... | the reliability, user experience, and code quality. The goal is to minimize the propagation of erroneous code and improve the feasibility of automated code translation.
- Pre-LLM Agent: Validates the correctness of the input code before it is processed by the LLM. If errors are detected, the agent attempts to automatic... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
3e3b1081-0b53-4f5f-8923-fffd6ff1c4fb | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 6 | opea-semantic-v1 | 923e005a05fd2f5a | passed to the LLM and that generated code is verified before reaching the user, thereby improving the overall efficiency and accuracy of the translation process.
## Motivation | ai_ref_knowledge | OPEA Documentation | passed to the LLM and that generated code is verified before reaching the user, thereby improving the overall efficiency and accuracy of the translation process.
## Motivation | passed to the LLM and that generated code is verified before reaching the user, thereby improving the overall efficiency and accuracy of the translation process.
## Motivation | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
3e4c54cf-e285-4236-9aa4-53a7a77d2282 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 24 | opea-semantic-v1 | a56e9ae609925c16 | This prevents the LLM from getting stuck in an infinite loop and improves user control over the process.
These use cases demonstrate how integrating Agents into the CodeTrans example improves input validation, output verification, and error handling. By ensuring only valid code reaches the LLM and automatically validat... | ai_ref_knowledge | OPEA Documentation | This prevents the LLM from getting stuck in an infinite loop and improves user control over the process.
These use cases demonstrate how integrating Agents into the CodeTrans example improves input validation, output verification, and error handling. By ensuring only valid code reaches the LLM and automatically validat... | This prevents the LLM from getting stuck in an infinite loop and improves user control over the process.
These use cases demonstrate how integrating Agents into the CodeTrans example improves input validation, output verification, and error handling. By ensuring only valid code reaches the LLM and automatically validat... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4287f228-ba95-4c3a-9e1d-6851a4ac04b8 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 23 | opea-semantic-v1 | 0da5ee338e622ba8 | multiple attempts. Here are possible reasons and debugging suggestions." - The system provides relevant error logs and hints, helping the developer troubleshoot the issue efficiently.
This prevents the LLM from getting stuck in an infinite loop and improves user control over the process. | ai_ref_knowledge | OPEA Documentation | multiple attempts. Here are possible reasons and debugging suggestions." - The system provides relevant error logs and hints, helping the developer troubleshoot the issue efficiently.
This prevents the LLM from getting stuck in an infinite loop and improves user control over the process. | multiple attempts. Here are possible reasons and debugging suggestions." - The system provides relevant error logs and hints, helping the developer troubleshoot the issue efficiently.
This prevents the LLM from getting stuck in an infinite loop and improves user control over the process. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
42a43338-4401-4bfe-bebd-e9e3d050e263 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 38 | opea-semantic-v1 | 27f4055daa06af29 | Since Lint is static check, it does not require a separate execution environment, so it can be called and executed directly in a python script.
Code Execution Tool: | ai_ref_knowledge | OPEA Documentation | Since Lint is static check, it does not require a separate execution environment, so it can be called and executed directly in a python script.
Code Execution Tool: | Since Lint is static check, it does not require a separate execution environment, so it can be called and executed directly in a python script.
Code Execution Tool: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
46083be1-5dad-4048-9fa4-61156add92ee | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 67 | opea-semantic-v1 | e77d250abed28c94 | ## Risks and Mitigations / User Workarounds
* Node / cluster take over by execution of malicious code
* Mitigation: automated vetting of the executed code + its strict sandboxing
* Code execution exhausting node resources
* Mitigation: strict resource usage limits
* Application response taking too long due to depende... | ai_ref_knowledge | OPEA Documentation | ## Risks and Mitigations / User Workarounds
* Node / cluster take over by execution of malicious code
* Mitigation: automated vetting of the executed code + its strict sandboxing
* Code execution exhausting node resources
* Mitigation: strict resource usage limits
* Application response taking too long due to depende... | ## Risks and Mitigations / User Workarounds
* Node / cluster take over by execution of malicious code
* Mitigation: automated vetting of the executed code + its strict sandboxing
* Code execution exhausting node resources
* Mitigation: strict resource usage limits
* Application response taking too long due to depende... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
47674a61-bbc4-4d3a-865b-7c8f45226bc5 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 26 | opea-semantic-v1 | bc1f4616667c2a4e | ### Architecture Diagram
```mermaid
graph LR
%% Colors %%
classDef blue fill:#ADD8E6,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,fill-opacity:0.5
subgraph User Interface
... | ai_ref_knowledge | OPEA Documentation | ### Architecture Diagram
```mermaid
graph LR
%% Colors %%
classDef blue fill:#ADD8E6,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,fill-opacity:0.5
subgraph User Interface
... | ### Architecture Diagram
```mermaid
graph LR
%% Colors %%
classDef blue fill:#ADD8E6,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,fill-opacity:0.5
subgraph User Interface
... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5286545d-61e9-4542-8932-51e9320f782a | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 25 | opea-semantic-v1 | bf5879c75bc4648c | reduces errors, minimizes manual debugging, and improves translation accuracy. Retry limits and debugging feedback prevent infinite loops, making the process more reliable, efficient, and user-friendly.
## Design Proposal | ai_ref_knowledge | OPEA Documentation | reduces errors, minimizes manual debugging, and improves translation accuracy. Retry limits and debugging feedback prevent infinite loops, making the process more reliable, efficient, and user-friendly.
## Design Proposal | reduces errors, minimizes manual debugging, and improves translation accuracy. Retry limits and debugging feedback prevent infinite loops, making the process more reliable, efficient, and user-friendly.
## Design Proposal | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
57babdd5-c7f7-40db-8111-6ea483df432a | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 45 | opea-semantic-v1 | b53748305097e0f9 | to do static checks for different languages, we could save the target code into a temporary file, and execute the lint check command in `bash`.
This is an example script which support all of these languages. | ai_ref_knowledge | OPEA Documentation | to do static checks for different languages, we could save the target code into a temporary file, and execute the lint check command in `bash`.
This is an example script which support all of these languages. | to do static checks for different languages, we could save the target code into a temporary file, and execute the lint check command in `bash`.
This is an example script which support all of these languages. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
597fd11f-a30a-45c0-af49-1bc87380d618 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 27 | opea-semantic-v1 | 13da5d9c14d6864c | %% classDef blue fill:#ADD8E6,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,fill-opacity:0.5 subgraph User Interface %% direction TD Frontend[(Frontend Server)]:::orange UIQ[User... | ai_ref_knowledge | OPEA Documentation | %% classDef blue fill:#ADD8E6,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,fill-opacity:0.5 subgraph User Interface %% direction TD Frontend[(Frontend Server)]:::orange UIQ[User... | %% classDef blue fill:#ADD8E6,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,fill-opacity:0.5 subgraph User Interface %% direction TD Frontend[(Frontend Server)]:::orange UIQ[User... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5a6987dd-2ce5-4d07-b601-b0e0c54c634a | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 53 | opea-semantic-v1 | 06b9c5f1f4cbdc3f | *) echo "Unsupported language: $LANGUAGE" echo "Supported language: python, javascript, java, go, cpp" exit 1 ;; esac
echo "Lint check completed. Report saved to $REPORT_FILE" | ai_ref_knowledge | OPEA Documentation | *) echo "Unsupported language: $LANGUAGE" echo "Supported language: python, javascript, java, go, cpp" exit 1 ;; esac
echo "Lint check completed. Report saved to $REPORT_FILE" | *) echo "Unsupported language: $LANGUAGE" echo "Supported language: python, javascript, java, go, cpp" exit 1 ;; esac
echo "Lint check completed. Report saved to $REPORT_FILE" | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5cfe16d0-45ac-4d9d-a670-e4ed63ae498a | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 47 | opea-semantic-v1 | e21c44c10477becb | if [[ ! -f "$SOURCE_FILE" ]]; then echo "Source file not found: $SOURCE_FILE" exit 1 fi
case "$LANGUAGE" in
python)
echo "Running pylint..."
pylint "$SOURCE_FILE" > "$REPORT_FILE" 2>&1
;; | ai_ref_knowledge | OPEA Documentation | if [[ ! -f "$SOURCE_FILE" ]]; then echo "Source file not found: $SOURCE_FILE" exit 1 fi
case "$LANGUAGE" in
python)
echo "Running pylint..."
pylint "$SOURCE_FILE" > "$REPORT_FILE" 2>&1
;; | if [[ ! -f "$SOURCE_FILE" ]]; then echo "Source file not found: $SOURCE_FILE" exit 1 fi
case "$LANGUAGE" in
python)
echo "Running pylint..."
pylint "$SOURCE_FILE" > "$REPORT_FILE" 2>&1
;; | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5f56793b-c198-4fb1-9e78-bf2638ec26e9 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 20 | opea-semantic-v1 | f6640ed47922f1fc | Scenario:
In some cases, the LLM may repeatedly generate faulty code, leading to an endless loop of failed executions and retries. Without a safeguard, this could waste computation resources and frustrate users. | ai_ref_knowledge | OPEA Documentation | Scenario:
In some cases, the LLM may repeatedly generate faulty code, leading to an endless loop of failed executions and retries. Without a safeguard, this could waste computation resources and frustrate users. | Scenario:
In some cases, the LLM may repeatedly generate faulty code, leading to an endless loop of failed executions and retries. Without a safeguard, this could waste computation resources and frustrate users. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
612f06ba-08a4-4c8c-9ee0-8dfa7f1c3ea0 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 41 | opea-semantic-v1 | 81e14c8b3f1d86c9 | Here's a table of lint tools for different coding languages:
| Coding Language | Lint Tool | Introduction | Reference |
| --------------- | ---------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------... | ai_ref_knowledge | OPEA Documentation | Here's a table of lint tools for different coding languages:
| Coding Language | Lint Tool | Introduction | Reference |
| --------------- | ---------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------... | Here's a table of lint tools for different coding languages:
| Coding Language | Lint Tool | Introduction | Reference |
| --------------- | ---------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6241ad85-4799-4dcf-b167-80a2e9f9985c | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 22 | opea-semantic-v1 | 3313d0f3f360c380 | How the CodeTrans Helps:
- Both Pre- and Post-LLM Agents tracks retry attempts. - If the LLM fails to produce a correct version after configurable number of attempts, the system stops further retries. - Instead of another faulty translation, the user receives:
- ❌ "Code generation failed after multiple attempts. Here ... | ai_ref_knowledge | OPEA Documentation | How the CodeTrans Helps:
- Both Pre- and Post-LLM Agents tracks retry attempts. - If the LLM fails to produce a correct version after configurable number of attempts, the system stops further retries. - Instead of another faulty translation, the user receives:
- ❌ "Code generation failed after multiple attempts. Here ... | How the CodeTrans Helps:
- Both Pre- and Post-LLM Agents tracks retry attempts. - If the LLM fails to produce a correct version after configurable number of attempts, the system stops further retries. - Instead of another faulty translation, the user receives:
- ❌ "Code generation failed after multiple attempts. Here ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
62cdb628-3abe-4287-9df5-315338d7079c | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 17 | opea-semantic-v1 | 82b6980957e84194 | How the CodeTrans Helps:
- User selects both `Lint Check` and `Code Execution` in the web UI. - Post-LLM Agent does the lint check for the translation Python code. - Agent will automatically fix any style/semantic issues. - Post-LLM Agent executes the translated Python code:
- ✅ If the code runs successfully, the syst... | ai_ref_knowledge | OPEA Documentation | How the CodeTrans Helps:
- User selects both `Lint Check` and `Code Execution` in the web UI. - Post-LLM Agent does the lint check for the translation Python code. - Agent will automatically fix any style/semantic issues. - Post-LLM Agent executes the translated Python code:
- ✅ If the code runs successfully, the syst... | How the CodeTrans Helps:
- User selects both `Lint Check` and `Code Execution` in the web UI. - Post-LLM Agent does the lint check for the translation Python code. - Agent will automatically fix any style/semantic issues. - Post-LLM Agent executes the translated Python code:
- ✅ If the code runs successfully, the syst... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
63cafddb-a91c-4f5a-91f0-de1b59d87421 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 39 | opea-semantic-v1 | 7b7156e1cf2fdc12 | Code Execution Tool:
- Provides a secure execution environment (e.g., Docker/Sandbox) to safely run code and prevent malicious execution risks. - For reasons of complexity of implementation, only `Python` execution tool will be supported for now. | ai_ref_knowledge | OPEA Documentation | Code Execution Tool:
- Provides a secure execution environment (e.g., Docker/Sandbox) to safely run code and prevent malicious execution risks. - For reasons of complexity of implementation, only `Python` execution tool will be supported for now. | Code Execution Tool:
- Provides a secure execution environment (e.g., Docker/Sandbox) to safely run code and prevent malicious execution risks. - For reasons of complexity of implementation, only `Python` execution tool will be supported for now. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
65d19137-f39c-4bc5-8215-ebf16ab169ff | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 40 | opea-semantic-v1 | 0452f524f5802be7 | to safely run code and prevent malicious execution risks. - For reasons of complexity of implementation, only `Python` execution tool will be supported for now.
#### Lint Check Tool | ai_ref_knowledge | OPEA Documentation | to safely run code and prevent malicious execution risks. - For reasons of complexity of implementation, only `Python` execution tool will be supported for now.
#### Lint Check Tool | to safely run code and prevent malicious execution risks. - For reasons of complexity of implementation, only `Python` execution tool will be supported for now.
#### Lint Check Tool | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
68e615d0-c040-43c4-926c-5e13eb4d2837 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 7 | opea-semantic-v1 | 4d5f8a26fa4a02a7 | The current CodeTrans flow has three major issues:
1. **User input may contain syntax or logic errors.** Passing faulty code directly to the LLM can result in incorrect or unusable translations. 2. **LLM-generated code isn’t always correct.** Without an automated validation step, users have to manually review and debug... | ai_ref_knowledge | OPEA Documentation | The current CodeTrans flow has three major issues:
1. **User input may contain syntax or logic errors.** Passing faulty code directly to the LLM can result in incorrect or unusable translations. 2. **LLM-generated code isn’t always correct.** Without an automated validation step, users have to manually review and debug... | The current CodeTrans flow has three major issues:
1. **User input may contain syntax or logic errors.** Passing faulty code directly to the LLM can result in incorrect or unusable translations. 2. **LLM-generated code isn’t always correct.** Without an automated validation step, users have to manually review and debug... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6a474706-e996-4e7f-b710-b514e8a6286d | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-03-14-GenAIExample-001-CodeTrans-with-Agents.md | unknown | 0fb78f1e-537c-404c-a904-9e4493c176a3 | 36 | opea-semantic-v1 | 9083bf419d350182 | is returned to the user. - If execution fails, the error details are sent back to the LLM for regeneration (within configurable number of attempts).
Lint Check Tool: | ai_ref_knowledge | OPEA Documentation | is returned to the user. - If execution fails, the error details are sent back to the LLM for regeneration (within configurable number of attempts).
Lint Check Tool: | is returned to the user. - If execution fails, the error details are sent back to the LLM for regeneration (within configurable number of attempts).
Lint Check Tool: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation |
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