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7ec27f89-8bf8-498c-a2ef-663b0bf77cb5 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-15-01-GenAIExamples-001-Code-Generation-Using-RAG-and-Agents.md | unknown | 32f1eb78-de28-41fa-b807-f373f197eec7 | 60 | opea-semantic-v1 | 894d33cd68ef7d67 | prioritizes the most relevant context from the retrieved information. - **LLM MicroService**: Uses a large language model to generate code based on the filtered context.
#### 3. **External Services**
- **Agents Service**: Provides additional agent functionalities. - **Embedding Service**: Generates embeddings for docu... | ai_ref_knowledge | OPEA Documentation | prioritizes the most relevant context from the retrieved information. - **LLM MicroService**: Uses a large language model to generate code based on the filtered context.
#### 3. **External Services**
- **Agents Service**: Provides additional agent functionalities. - **Embedding Service**: Generates embeddings for docu... | prioritizes the most relevant context from the retrieved information. - **LLM MicroService**: Uses a large language model to generate code based on the filtered context.
#### 3. **External Services**
- **Agents Service**: Provides additional agent functionalities. - **Embedding Service**: Generates embeddings for docu... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
86f54a7f-ffaa-4328-8243-e1e3dab765b5 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-15-01-GenAIExamples-001-Code-Generation-Using-RAG-and-Agents.md | unknown | 32f1eb78-de28-41fa-b807-f373f197eec7 | 46 | opea-semantic-v1 | e1e6d89aad762912 | will be "None". - **Submit Button**: A button to submit the query. - **Response Box**: A text box to display the response from the system.
### Manage Resources Tab | ai_ref_knowledge | OPEA Documentation | will be "None". - **Submit Button**: A button to submit the query. - **Response Box**: A text box to display the response from the system.
### Manage Resources Tab | will be "None". - **Submit Button**: A button to submit the query. - **Response Box**: A text box to display the response from the system.
### Manage Resources Tab | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
88743097-c70a-43a7-8552-1a2faca6ab10 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-15-01-GenAIExamples-001-Code-Generation-Using-RAG-and-Agents.md | unknown | 32f1eb78-de28-41fa-b807-f373f197eec7 | 23 | opea-semantic-v1 | d6ff017fa793831a | the development team. By incorporating agents into the code generation process, we can achieve more robust and maintainable code, ultimately enhancing the overall development process.
Using Agents in conjunction with RAG for code generation offers several advantages that can significantly enhance the quality and releva... | ai_ref_knowledge | OPEA Documentation | the development team. By incorporating agents into the code generation process, we can achieve more robust and maintainable code, ultimately enhancing the overall development process.
Using Agents in conjunction with RAG for code generation offers several advantages that can significantly enhance the quality and releva... | the development team. By incorporating agents into the code generation process, we can achieve more robust and maintainable code, ultimately enhancing the overall development process.
Using Agents in conjunction with RAG for code generation offers several advantages that can significantly enhance the quality and releva... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
887f25b7-cb1e-4723-8079-eb0ecd5cea45 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-15-01-GenAIExamples-001-Code-Generation-Using-RAG-and-Agents.md | unknown | 32f1eb78-de28-41fa-b807-f373f197eec7 | 10 | opea-semantic-v1 | 37306642c3195e9c | **Optimizing Confidential/Experimental Code Using RAG and Agents**
A research scientist is working on a confidential and experimental software project that requires highly specialized optimizations. By submitting a code optimization request, the scientist leverages the Vector Database Microservice to retrieve relevant ... | ai_ref_knowledge | OPEA Documentation | **Optimizing Confidential/Experimental Code Using RAG and Agents**
A research scientist is working on a confidential and experimental software project that requires highly specialized optimizations. By submitting a code optimization request, the scientist leverages the Vector Database Microservice to retrieve relevant ... | **Optimizing Confidential/Experimental Code Using RAG and Agents**
A research scientist is working on a confidential and experimental software project that requires highly specialized optimizations. By submitting a code optimization request, the scientist leverages the Vector Database Microservice to retrieve relevant ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8a8c5215-422a-49b3-8742-068fb365c211 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-15-01-GenAIExamples-001-Code-Generation-Using-RAG-and-Agents.md | unknown | 32f1eb78-de28-41fa-b807-f373f197eec7 | 61 | opea-semantic-v1 | 898a2513e2a24ecf | vector representations of documents and code snippets. - **Retriever Service**: Retrieves relevant information from the vector database. - **LLM Service**: Provides large language model functionalities.
#### 4. **Data Preparation**
- This component is responsible for preparing data for ingestion into the vector databa... | ai_ref_knowledge | OPEA Documentation | vector representations of documents and code snippets. - **Retriever Service**: Retrieves relevant information from the vector database. - **LLM Service**: Provides large language model functionalities.
#### 4. **Data Preparation**
- This component is responsible for preparing data for ingestion into the vector databa... | vector representations of documents and code snippets. - **Retriever Service**: Retrieves relevant information from the vector database. - **LLM Service**: Provides large language model functionalities.
#### 4. **Data Preparation**
- This component is responsible for preparing data for ingestion into the vector databa... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8fe06c77-0333-44f9-8b19-ed0f54d3f511 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-15-01-GenAIExamples-001-Code-Generation-Using-RAG-and-Agents.md | unknown | 32f1eb78-de28-41fa-b807-f373f197eec7 | 18 | opea-semantic-v1 | 0d634ef3b536979b | would otherwise be spent searching for relevant information and manually writing code. This allows you to focus on more critical aspects of your development process.
**Scalability**: RAG can handle large-scale codebases and complex projects, making it suitable for both small and large development teams. It can provide ... | ai_ref_knowledge | OPEA Documentation | would otherwise be spent searching for relevant information and manually writing code. This allows you to focus on more critical aspects of your development process.
**Scalability**: RAG can handle large-scale codebases and complex projects, making it suitable for both small and large development teams. It can provide ... | would otherwise be spent searching for relevant information and manually writing code. This allows you to focus on more critical aspects of your development process.
**Scalability**: RAG can handle large-scale codebases and complex projects, making it suitable for both small and large development teams. It can provide ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
97eedb86-8bd3-44d9-977d-e970904c42a5 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-15-01-GenAIExamples-001-Code-Generation-Using-RAG-and-Agents.md | unknown | 32f1eb78-de28-41fa-b807-f373f197eec7 | 16 | opea-semantic-v1 | 4c2b7a04416d5110 | and code repositories. This ensures that the code generation is based on the most current and relevant information, helping you stay ahead of the curve.
**Improved Code Quality**: By leveraging a large corpus of high-quality code examples and best practices, RAG can suggest code generations that improve the overall qua... | ai_ref_knowledge | OPEA Documentation | and code repositories. This ensures that the code generation is based on the most current and relevant information, helping you stay ahead of the curve.
**Improved Code Quality**: By leveraging a large corpus of high-quality code examples and best practices, RAG can suggest code generations that improve the overall qua... | and code repositories. This ensures that the code generation is based on the most current and relevant information, helping you stay ahead of the curve.
**Improved Code Quality**: By leveraging a large corpus of high-quality code examples and best practices, RAG can suggest code generations that improve the overall qua... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
9a3b755f-377c-409a-b93b-5660f76a66d9 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-15-01-GenAIExamples-001-Code-Generation-Using-RAG-and-Agents.md | unknown | 32f1eb78-de28-41fa-b807-f373f197eec7 | 28 | opea-semantic-v1 | f5685fd4e21802ff | needs. This adaptability ensures that the generated code is tailored to the unique context and preferences of the development team, enhancing overall satisfaction and usability.
**Reduced Cognitive Load**: By automating the filtering and prioritization of relevant context, Agents reduce the cognitive load on developers... | ai_ref_knowledge | OPEA Documentation | needs. This adaptability ensures that the generated code is tailored to the unique context and preferences of the development team, enhancing overall satisfaction and usability.
**Reduced Cognitive Load**: By automating the filtering and prioritization of relevant context, Agents reduce the cognitive load on developers... | needs. This adaptability ensures that the generated code is tailored to the unique context and preferences of the development team, enhancing overall satisfaction and usability.
**Reduced Cognitive Load**: By automating the filtering and prioritization of relevant context, Agents reduce the cognitive load on developers... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a081bfff-b501-4329-a153-0b11622af9e5 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-15-01-GenAIExamples-001-Code-Generation-Using-RAG-and-Agents.md | unknown | 32f1eb78-de28-41fa-b807-f373f197eec7 | 62 | opea-semantic-v1 | 48c9825178474033 | #### 4. **Data Preparation** - This component is responsible for preparing data for ingestion into the vector database.
#### 5. **CodeGen Gateway**
- This gateway handles the communication between the UI server and the CodeGen-MegaService. | ai_ref_knowledge | OPEA Documentation | #### 4. **Data Preparation** - This component is responsible for preparing data for ingestion into the vector database.
#### 5. **CodeGen Gateway**
- This gateway handles the communication between the UI server and the CodeGen-MegaService. | #### 4. **Data Preparation** - This component is responsible for preparing data for ingestion into the vector database.
#### 5. **CodeGen Gateway**
- This gateway handles the communication between the UI server and the CodeGen-MegaService. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a1a10fa3-4f4b-4658-81c7-24570988e389 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-15-01-GenAIExamples-001-Code-Generation-Using-RAG-and-Agents.md | unknown | 32f1eb78-de28-41fa-b807-f373f197eec7 | 50 | opea-semantic-v1 | df34eb5ecf9a803e | saved resources effectively. This ensures that users can easily access and utilize the new functionalities, enhancing the overall code optimization process with RAG and Agents.
## Design Proposal and Diagram | ai_ref_knowledge | OPEA Documentation | saved resources effectively. This ensures that users can easily access and utilize the new functionalities, enhancing the overall code optimization process with RAG and Agents.
## Design Proposal and Diagram | saved resources effectively. This ensures that users can easily access and utilize the new functionalities, enhancing the overall code optimization process with RAG and Agents.
## Design Proposal and Diagram | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a448c102-5652-4e60-8c56-24c487a95f8f | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-15-01-GenAIExamples-001-Code-Generation-Using-RAG-and-Agents.md | unknown | 32f1eb78-de28-41fa-b807-f373f197eec7 | 39 | opea-semantic-v1 | 3e046c1a322c3f2b | Results show that even though we have a generic question, the proposed improvement is able to filter the correct contents and create the desired answer.
## Proposed UI Changes for Enhanced Code Generation with RAG and Agents | ai_ref_knowledge | OPEA Documentation | Results show that even though we have a generic question, the proposed improvement is able to filter the correct contents and create the desired answer.
## Proposed UI Changes for Enhanced Code Generation with RAG and Agents | Results show that even though we have a generic question, the proposed improvement is able to filter the correct contents and create the desired answer.
## Proposed UI Changes for Enhanced Code Generation with RAG and Agents | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ad567768-ebc1-4e46-9631-d051b45b8796 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-15-01-GenAIExamples-001-Code-Generation-Using-RAG-and-Agents.md | unknown | 32f1eb78-de28-41fa-b807-f373f197eec7 | 54 | opea-semantic-v1 | b4180e22e7223c89 | LOCAL_RER{{Agents<br>service}} CLIP_EM{{Embedding<br>service}} VDB{{Vector DB}} V_RET{{Retriever<br>service}} Ingest{{Ingest data}} DP([Data Preparation]):::blue LLM_gen{{LLM Service}} GW([CodeGen GateWay]):::orange
%% Data Preparation flow
%% Ingest data flow
direction LR
Ingest[Ingest data] --> UI
UI --> DP
DP <... | ai_ref_knowledge | OPEA Documentation | LOCAL_RER{{Agents<br>service}} CLIP_EM{{Embedding<br>service}} VDB{{Vector DB}} V_RET{{Retriever<br>service}} Ingest{{Ingest data}} DP([Data Preparation]):::blue LLM_gen{{LLM Service}} GW([CodeGen GateWay]):::orange
%% Data Preparation flow
%% Ingest data flow
direction LR
Ingest[Ingest data] --> UI
UI --> DP
DP <... | LOCAL_RER{{Agents<br>service}} CLIP_EM{{Embedding<br>service}} VDB{{Vector DB}} V_RET{{Retriever<br>service}} Ingest{{Ingest data}} DP([Data Preparation]):::blue LLM_gen{{LLM Service}} GW([CodeGen GateWay]):::orange
%% Data Preparation flow
%% Ingest data flow
direction LR
Ingest[Ingest data] --> UI
UI --> DP
DP <... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
af0dcd08-9283-432a-908a-a5d354c62a2c | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-15-01-GenAIExamples-001-Code-Generation-Using-RAG-and-Agents.md | unknown | 32f1eb78-de28-41fa-b807-f373f197eec7 | 67 | opea-semantic-v1 | 04c29cc3057ae791 | We have planned the following development phases based on the priority of the features and their development effort:
* Phase 1:
- Implement UI
- Data prep and ingestion
- Embedding Service
- Retrieval Service
- Vector Database | ai_ref_knowledge | OPEA Documentation | We have planned the following development phases based on the priority of the features and their development effort:
* Phase 1:
- Implement UI
- Data prep and ingestion
- Embedding Service
- Retrieval Service
- Vector Database | We have planned the following development phases based on the priority of the features and their development effort:
* Phase 1:
- Implement UI
- Data prep and ingestion
- Embedding Service
- Retrieval Service
- Vector Database | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
af7af4b0-0ea3-4a65-8795-c2864f0656ed | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-15-01-GenAIExamples-001-Code-Generation-Using-RAG-and-Agents.md | unknown | 32f1eb78-de28-41fa-b807-f373f197eec7 | 49 | opea-semantic-v1 | ee2c0e56d0ed7bbf | - **Actions**: Options to edit or delete the resource.
The proposed UI changes aim to provide a seamless and efficient user experience for submitting direct queries and saving resources to the vector database. The main interface will have a new tab for managing resources, while the existing query submission functionali... | ai_ref_knowledge | OPEA Documentation | - **Actions**: Options to edit or delete the resource.
The proposed UI changes aim to provide a seamless and efficient user experience for submitting direct queries and saving resources to the vector database. The main interface will have a new tab for managing resources, while the existing query submission functionali... | - **Actions**: Options to edit or delete the resource.
The proposed UI changes aim to provide a seamless and efficient user experience for submitting direct queries and saving resources to the vector database. The main interface will have a new tab for managing resources, while the existing query submission functionali... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b272e82f-8398-4589-95a8-2fef3b9141b1 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-15-01-GenAIExamples-001-Code-Generation-Using-RAG-and-Agents.md | unknown | 32f1eb78-de28-41fa-b807-f373f197eec7 | 47 | opea-semantic-v1 | c240bad3e701621a | ### Manage Resources Tab
This new tab will allow users to save/update documents or online resources to the vector database. Users can upload files or provide URLs, and manage their saved resources. The components of this tab will include:
- **Form**: A form for uploading files or entering URLs. - **File Upload**: An op... | ai_ref_knowledge | OPEA Documentation | ### Manage Resources Tab
This new tab will allow users to save/update documents or online resources to the vector database. Users can upload files or provide URLs, and manage their saved resources. The components of this tab will include:
- **Form**: A form for uploading files or entering URLs. - **File Upload**: An op... | ### Manage Resources Tab
This new tab will allow users to save/update documents or online resources to the vector database. Users can upload files or provide URLs, and manage their saved resources. The components of this tab will include:
- **Form**: A form for uploading files or entering URLs. - **File Upload**: An op... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b94c4ac6-823e-4178-9e8e-b8c7133a9dbb | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-15-01-GenAIExamples-001-Code-Generation-Using-RAG-and-Agents.md | unknown | 32f1eb78-de28-41fa-b807-f373f197eec7 | 42 | opea-semantic-v1 | 298783ab719c6a52 | ### Main Interface
The main interface will now include a new tab for managing resources. The existing functionality for submitting direct queries will remain, but with an enhanced layout for better usability. The navigation bar will have two tabs: "Submit Query" and "Manage Resources". | ai_ref_knowledge | OPEA Documentation | ### Main Interface
The main interface will now include a new tab for managing resources. The existing functionality for submitting direct queries will remain, but with an enhanced layout for better usability. The navigation bar will have two tabs: "Submit Query" and "Manage Resources". | ### Main Interface
The main interface will now include a new tab for managing resources. The existing functionality for submitting direct queries will remain, but with an enhanced layout for better usability. The navigation bar will have two tabs: "Submit Query" and "Manage Resources". | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b9ee0b76-3992-4b9d-985d-705dfb4b868c | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-15-01-GenAIExamples-001-Code-Generation-Using-RAG-and-Agents.md | unknown | 32f1eb78-de28-41fa-b807-f373f197eec7 | 33 | opea-semantic-v1 | 46e5a00b50b61516 | We used the same question and the same LLM model to generate answers. The results are shown below:
- **Question:** "Can you create a Python function example that will read a CSV file?"
- **LLM:** Qwen/Qwen2.5-Coder-7B-Instruct | ai_ref_knowledge | OPEA Documentation | We used the same question and the same LLM model to generate answers. The results are shown below:
- **Question:** "Can you create a Python function example that will read a CSV file?"
- **LLM:** Qwen/Qwen2.5-Coder-7B-Instruct | We used the same question and the same LLM model to generate answers. The results are shown below:
- **Question:** "Can you create a Python function example that will read a CSV file?"
- **LLM:** Qwen/Qwen2.5-Coder-7B-Instruct | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
bbe3abfa-d0f3-4e6c-93fb-041430171ad8 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-15-01-GenAIExamples-001-Code-Generation-Using-RAG-and-Agents.md | unknown | 32f1eb78-de28-41fa-b807-f373f197eec7 | 9 | opea-semantic-v1 | 0f0311014daab585 | the new hardware. The engineer applies these optimizations, resulting in a software application that runs efficiently and takes full advantage of the new hardware's capabilities.
**Optimizing Confidential/Experimental Code Using RAG and Agents** | ai_ref_knowledge | OPEA Documentation | the new hardware. The engineer applies these optimizations, resulting in a software application that runs efficiently and takes full advantage of the new hardware's capabilities.
**Optimizing Confidential/Experimental Code Using RAG and Agents** | the new hardware. The engineer applies these optimizations, resulting in a software application that runs efficiently and takes full advantage of the new hardware's capabilities.
**Optimizing Confidential/Experimental Code Using RAG and Agents** | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c55d931f-66cd-45fd-9a68-27682f200395 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-15-01-GenAIExamples-001-Code-Generation-Using-RAG-and-Agents.md | unknown | 32f1eb78-de28-41fa-b807-f373f197eec7 | 4 | opea-semantic-v1 | 3151d4b03c562c91 | **Adapting a Codebase to New Coding Standards Using RAG and Agents**
A software architect is responsible for updating an existing codebase to align with newly adopted coding standards. By submitting a code optimization request, the architect utilizes the Vector Database Microservice to gather information on the new sta... | ai_ref_knowledge | OPEA Documentation | **Adapting a Codebase to New Coding Standards Using RAG and Agents**
A software architect is responsible for updating an existing codebase to align with newly adopted coding standards. By submitting a code optimization request, the architect utilizes the Vector Database Microservice to gather information on the new sta... | **Adapting a Codebase to New Coding Standards Using RAG and Agents**
A software architect is responsible for updating an existing codebase to align with newly adopted coding standards. By submitting a code optimization request, the architect utilizes the Vector Database Microservice to gather information on the new sta... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c68fbc5b-48fc-4b30-a2b3-932ccb3a2bc6 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-15-01-GenAIExamples-001-Code-Generation-Using-RAG-and-Agents.md | unknown | 32f1eb78-de28-41fa-b807-f373f197eec7 | 36 | opea-semantic-v1 | 950de9c802135316 | **Proposed Improvement**
We uploaded the content of these URLs to a vector database, and retrieval results are filtered by agents. | ai_ref_knowledge | OPEA Documentation | **Proposed Improvement**
We uploaded the content of these URLs to a vector database, and retrieval results are filtered by agents. | **Proposed Improvement**
We uploaded the content of these URLs to a vector database, and retrieval results are filtered by agents. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c764c600-4c5b-4fdc-a47b-8023434478de | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-15-01-GenAIExamples-001-Code-Generation-Using-RAG-and-Agents.md | unknown | 32f1eb78-de28-41fa-b807-f373f197eec7 | 12 | opea-semantic-v1 | 4a9c0b09c8e23903 | enhance various aspects of the code generation process, including performance improvement, adherence to coding standards, efficient feature development, hardware-specific optimization, and optimization for confidential/experimental implementations.
## Benefits of Using RAG for Code Generation | ai_ref_knowledge | OPEA Documentation | enhance various aspects of the code generation process, including performance improvement, adherence to coding standards, efficient feature development, hardware-specific optimization, and optimization for confidential/experimental implementations.
## Benefits of Using RAG for Code Generation | enhance various aspects of the code generation process, including performance improvement, adherence to coding standards, efficient feature development, hardware-specific optimization, and optimization for confidential/experimental implementations.
## Benefits of Using RAG for Code Generation | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ce4462df-d8a9-4ee1-94c7-e230b7ef3ad2 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-15-01-GenAIExamples-001-Code-Generation-Using-RAG-and-Agents.md | unknown | 32f1eb78-de28-41fa-b807-f373f197eec7 | 26 | opea-semantic-v1 | c79f21bc9b51f89c | is significantly improved. Agents can identify and focus on the most critical aspects of the retrieved information, reducing the likelihood of errors or suboptimal suggestions.
**Enhanced Efficiency**: Agents streamline the retrieval process by quickly filtering out unnecessary information and highlighting the most rel... | ai_ref_knowledge | OPEA Documentation | is significantly improved. Agents can identify and focus on the most critical aspects of the retrieved information, reducing the likelihood of errors or suboptimal suggestions.
**Enhanced Efficiency**: Agents streamline the retrieval process by quickly filtering out unnecessary information and highlighting the most rel... | is significantly improved. Agents can identify and focus on the most critical aspects of the retrieved information, reducing the likelihood of errors or suboptimal suggestions.
**Enhanced Efficiency**: Agents streamline the retrieval process by quickly filtering out unnecessary information and highlighting the most rel... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d66355df-67bd-4353-8040-7906a9fdcdfe | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-15-01-GenAIExamples-001-Code-Generation-Using-RAG-and-Agents.md | unknown | 32f1eb78-de28-41fa-b807-f373f197eec7 | 32 | opea-semantic-v1 | 85e32a357bf34751 | ## PoC Results
We used the same question and the same LLM model to generate answers. The results are shown below: | ai_ref_knowledge | OPEA Documentation | ## PoC Results
We used the same question and the same LLM model to generate answers. The results are shown below: | ## PoC Results
We used the same question and the same LLM model to generate answers. The results are shown below: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
de367e42-f50f-43dd-96d9-e2d631a7dabb | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-15-01-GenAIExamples-001-Code-Generation-Using-RAG-and-Agents.md | unknown | 32f1eb78-de28-41fa-b807-f373f197eec7 | 27 | opea-semantic-v1 | 9def3384e581beeb | information and highlighting the most relevant data. This efficiency reduces the time and computational resources required to generate high-quality code, allowing for faster development cycles.
**Dynamic Adaptability**: Agents can dynamically adapt to different coding styles, project requirements, and domain-specific n... | ai_ref_knowledge | OPEA Documentation | information and highlighting the most relevant data. This efficiency reduces the time and computational resources required to generate high-quality code, allowing for faster development cycles.
**Dynamic Adaptability**: Agents can dynamically adapt to different coding styles, project requirements, and domain-specific n... | information and highlighting the most relevant data. This efficiency reduces the time and computational resources required to generate high-quality code, allowing for faster development cycles.
**Dynamic Adaptability**: Agents can dynamically adapt to different coding styles, project requirements, and domain-specific n... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e09de15e-9325-4ce9-b47b-5093e46c0a03 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-15-01-GenAIExamples-001-Code-Generation-Using-RAG-and-Agents.md | unknown | 32f1eb78-de28-41fa-b807-f373f197eec7 | 45 | opea-semantic-v1 | d33f39dade3233c3 | be added to allow users to select the database to be used in the RAG process. The default option for the dropdown will be "None".
**Components**:
- **Query Input**: An input box for users to enter their query. - **Database Selection Dropdown**: A dropdown menu where users can select the database to be used in the RAG p... | ai_ref_knowledge | OPEA Documentation | be added to allow users to select the database to be used in the RAG process. The default option for the dropdown will be "None".
**Components**:
- **Query Input**: An input box for users to enter their query. - **Database Selection Dropdown**: A dropdown menu where users can select the database to be used in the RAG p... | be added to allow users to select the database to be used in the RAG process. The default option for the dropdown will be "None".
**Components**:
- **Query Input**: An input box for users to enter their query. - **Database Selection Dropdown**: A dropdown menu where users can select the database to be used in the RAG p... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e5bcb0cb-c326-4f4b-b064-519fd2ac08fd | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-15-01-GenAIExamples-001-Code-Generation-Using-RAG-and-Agents.md | unknown | 32f1eb78-de28-41fa-b807-f373f197eec7 | 14 | opea-semantic-v1 | 054f9e7c29b59f53 | Using Retrieval-Augmented Generation (RAG) for code generation offers several advantages that can significantly enhance the development process:
**Enhanced Contextual Understanding**: RAG combines the strengths of retrieval-based models and generative models. It retrieves relevant information from a large corpus of doc... | ai_ref_knowledge | OPEA Documentation | Using Retrieval-Augmented Generation (RAG) for code generation offers several advantages that can significantly enhance the development process:
**Enhanced Contextual Understanding**: RAG combines the strengths of retrieval-based models and generative models. It retrieves relevant information from a large corpus of doc... | Using Retrieval-Augmented Generation (RAG) for code generation offers several advantages that can significantly enhance the development process:
**Enhanced Contextual Understanding**: RAG combines the strengths of retrieval-based models and generative models. It retrieves relevant information from a large corpus of doc... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e75cd2e5-8423-4b97-b618-ffb158571139 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-15-01-GenAIExamples-001-Code-Generation-Using-RAG-and-Agents.md | unknown | 32f1eb78-de28-41fa-b807-f373f197eec7 | 11 | opea-semantic-v1 | 4c6f3f4c4f4d5d7d | and experimental requirements. The scientist reviews and applies these optimizations, resulting in a high-quality, optimized implementation that meets the project's unique needs while maintaining confidentiality.
These use-case stories illustrate how the integration of RAG and Agents can enhance various aspects of the ... | ai_ref_knowledge | OPEA Documentation | and experimental requirements. The scientist reviews and applies these optimizations, resulting in a high-quality, optimized implementation that meets the project's unique needs while maintaining confidentiality.
These use-case stories illustrate how the integration of RAG and Agents can enhance various aspects of the ... | and experimental requirements. The scientist reviews and applies these optimizations, resulting in a high-quality, optimized implementation that meets the project's unique needs while maintaining confidentiality.
These use-case stories illustrate how the integration of RAG and Agents can enhance various aspects of the ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f271696a-0ae5-4bb8-b543-2bfa3ce29220 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-15-01-GenAIExamples-001-Code-Generation-Using-RAG-and-Agents.md | unknown | 32f1eb78-de28-41fa-b807-f373f197eec7 | 20 | opea-semantic-v1 | fb6fb0239b7d6fc3 | cater to specific coding styles, project requirements, and domain-specific needs. This adaptability ensures that the generated code is tailored to your unique context and preferences.
**Continuous Learning and Improvement**: RAG models can continuously learn and improve from new data, ensuring that the code generation ... | ai_ref_knowledge | OPEA Documentation | cater to specific coding styles, project requirements, and domain-specific needs. This adaptability ensures that the generated code is tailored to your unique context and preferences.
**Continuous Learning and Improvement**: RAG models can continuously learn and improve from new data, ensuring that the code generation ... | cater to specific coding styles, project requirements, and domain-specific needs. This adaptability ensures that the generated code is tailored to your unique context and preferences.
**Continuous Learning and Improvement**: RAG models can continuously learn and improve from new data, ensuring that the code generation ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f2d2bae9-7422-4251-a11d-0536622c16a0 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-15-01-GenAIExamples-001-Code-Generation-Using-RAG-and-Agents.md | unknown | 32f1eb78-de28-41fa-b807-f373f197eec7 | 48 | opea-semantic-v1 | e0e9e623de342eb0 | of saved resources. - **Name**: Name of the saved resource. - **Type**: Type of resource (e.g., Document, URL). - **Tags**: Tags associated with the resource.
- **Actions**: Options to edit or delete the resource. | ai_ref_knowledge | OPEA Documentation | of saved resources. - **Name**: Name of the saved resource. - **Type**: Type of resource (e.g., Document, URL). - **Tags**: Tags associated with the resource.
- **Actions**: Options to edit or delete the resource. | of saved resources. - **Name**: Name of the saved resource. - **Type**: Type of resource (e.g., Document, URL). - **Tags**: Tags associated with the resource.
- **Actions**: Options to edit or delete the resource. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f32f411e-1991-452d-9c22-2ed3ddd95e5b | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-15-01-GenAIExamples-001-Code-Generation-Using-RAG-and-Agents.md | unknown | 32f1eb78-de28-41fa-b807-f373f197eec7 | 53 | opea-semantic-v1 | 0feea95659935b57 | %% Subgraphs %% subgraph CodeGen-MegaService["CodeGen-MegaService"] direction LR EM([Embedding<br>MicroService]):::blue RET([Retrieval<br>MicroService]):::blue RER([Agents<br>MicroService]):::blue LLM([LLM<br>MicroService]):::blue end subgraph User Interface direction LR a([Submit Query Tab]):::orchid UI([UI server])::... | ai_ref_knowledge | OPEA Documentation | %% Subgraphs %% subgraph CodeGen-MegaService["CodeGen-MegaService"] direction LR EM([Embedding<br>MicroService]):::blue RET([Retrieval<br>MicroService]):::blue RER([Agents<br>MicroService]):::blue LLM([LLM<br>MicroService]):::blue end subgraph User Interface direction LR a([Submit Query Tab]):::orchid UI([UI server])::... | %% Subgraphs %% subgraph CodeGen-MegaService["CodeGen-MegaService"] direction LR EM([Embedding<br>MicroService]):::blue RET([Retrieval<br>MicroService]):::blue RER([Agents<br>MicroService]):::blue LLM([LLM<br>MicroService]):::blue end subgraph User Interface direction LR a([Submit Query Tab]):::orchid UI([UI server])::... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f5369cbe-0b16-495d-beb9-4a22d0431ddd | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-15-01-GenAIExamples-001-Code-Generation-Using-RAG-and-Agents.md | unknown | 32f1eb78-de28-41fa-b807-f373f197eec7 | 52 | opea-semantic-v1 | 78eaa85c9e888f36 | config: flowchart: nodeSpacing: 400 rankSpacing: 100 curve: linear themeVariables: fontSize: 25px
flowchart 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:#A... | ai_ref_knowledge | OPEA Documentation | config: flowchart: nodeSpacing: 400 rankSpacing: 100 curve: linear themeVariables: fontSize: 25px
flowchart 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:#A... | config: flowchart: nodeSpacing: 400 rankSpacing: 100 curve: linear themeVariables: fontSize: 25px
flowchart 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:#A... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f8e2e8b7-229e-4e7e-9f43-7da8b0f97ef1 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-15-01-GenAIExamples-001-Code-Generation-Using-RAG-and-Agents.md | unknown | 32f1eb78-de28-41fa-b807-f373f197eec7 | 43 | opea-semantic-v1 | 4c93502be94a03bd | submitting direct queries will remain, but with an enhanced layout for better usability. The navigation bar will have two tabs: "Submit Query" and "Manage Resources".
### Submit Query Tab | ai_ref_knowledge | OPEA Documentation | submitting direct queries will remain, but with an enhanced layout for better usability. The navigation bar will have two tabs: "Submit Query" and "Manage Resources".
### Submit Query Tab | submitting direct queries will remain, but with an enhanced layout for better usability. The navigation bar will have two tabs: "Submit Query" and "Manage Resources".
### Submit Query Tab | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f9f4f000-3600-48cd-87bd-a8b03874bda6 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-15-01-GenAIExamples-001-Code-Generation-Using-RAG-and-Agents.md | unknown | 32f1eb78-de28-41fa-b807-f373f197eec7 | 7 | opea-semantic-v1 | 6c067069237cc6a0 | Microservice generates code tailored to the new feature. The engineer incorporates these code generations, resulting in a high-quality, efficiently developed feature that meets all requirements.
**Optimizing Code for Specific Hardware Using RAG and Agents** | ai_ref_knowledge | OPEA Documentation | Microservice generates code tailored to the new feature. The engineer incorporates these code generations, resulting in a high-quality, efficiently developed feature that meets all requirements.
**Optimizing Code for Specific Hardware Using RAG and Agents** | Microservice generates code tailored to the new feature. The engineer incorporates these code generations, resulting in a high-quality, efficiently developed feature that meets all requirements.
**Optimizing Code for Specific Hardware Using RAG and Agents** | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
fd467675-7fc1-41ce-9367-9fdc6998d183 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-15-01-GenAIExamples-001-Code-Generation-Using-RAG-and-Agents.md | unknown | 32f1eb78-de28-41fa-b807-f373f197eec7 | 3 | opea-semantic-v1 | f30778d9f787048a | time efficiency, scalability, customization, and continuous learning. By leveraging these technologies, we can achieve more robust and maintainable code, ultimately enhancing the overall development process.
## Use-Cases | ai_ref_knowledge | OPEA Documentation | time efficiency, scalability, customization, and continuous learning. By leveraging these technologies, we can achieve more robust and maintainable code, ultimately enhancing the overall development process.
## Use-Cases | time efficiency, scalability, customization, and continuous learning. By leveraging these technologies, we can achieve more robust and maintainable code, ultimately enhancing the overall development process.
## Use-Cases | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ff626046-4ade-4bc3-b407-f4350fff8e71 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-15-01-GenAIExamples-001-Code-Generation-Using-RAG-and-Agents.md | unknown | 32f1eb78-de28-41fa-b807-f373f197eec7 | 31 | opea-semantic-v1 | 7a9af874c4b99d87 | relevant, and efficient code generation. Agents enhance the overall quality and effectiveness of the generated code, ultimately leading to a more robust and maintainable codebase.
## PoC Results | ai_ref_knowledge | OPEA Documentation | relevant, and efficient code generation. Agents enhance the overall quality and effectiveness of the generated code, ultimately leading to a more robust and maintainable codebase.
## PoC Results | relevant, and efficient code generation. Agents enhance the overall quality and effectiveness of the generated code, ultimately leading to a more robust and maintainable codebase.
## PoC Results | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
02c8b50c-bce1-4a7b-8f82-c8350228d565 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-19-03-GenAIExamples-HybridRag-personal_assistant.md | unknown | a565ed76-96be-49f4-9b51-f29b91f8523c | 16 | opea-semantic-v1 | 873f30ec76bf8aab | sensitive sectors like healthcare. This approach represents a strategic evolution in enterprise AI, enabling systems to reason across data types while maintaining scalability and compliance.
## Design Proposal
This RFC proposes a Hybrid Retrieval-Augmented Generation (Hybrid-RAG) architecture detailed in the following ... | ai_ref_knowledge | OPEA Documentation | sensitive sectors like healthcare. This approach represents a strategic evolution in enterprise AI, enabling systems to reason across data types while maintaining scalability and compliance.
## Design Proposal
This RFC proposes a Hybrid Retrieval-Augmented Generation (Hybrid-RAG) architecture detailed in the following ... | sensitive sectors like healthcare. This approach represents a strategic evolution in enterprise AI, enabling systems to reason across data types while maintaining scalability and compliance.
## Design Proposal
This RFC proposes a Hybrid Retrieval-Augmented Generation (Hybrid-RAG) architecture detailed in the following ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0cef9093-83d0-4dca-a1dd-3156cdeb99a3 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-19-03-GenAIExamples-HybridRag-personal_assistant.md | unknown | a565ed76-96be-49f4-9b51-f29b91f8523c | 22 | opea-semantic-v1 | d4689018cd8a586d | Performance Evaluation HybridRAG outperforms traditional VectorRAG and GraphRAG techniques when used individually. This superior performance is observed in both:
- Retrieval accuracy
- Answer generation quality | ai_ref_knowledge | OPEA Documentation | Performance Evaluation HybridRAG outperforms traditional VectorRAG and GraphRAG techniques when used individually. This superior performance is observed in both:
- Retrieval accuracy
- Answer generation quality | Performance Evaluation HybridRAG outperforms traditional VectorRAG and GraphRAG techniques when used individually. This superior performance is observed in both:
- Retrieval accuracy
- Answer generation quality | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
10d1fc26-36ce-454f-8a99-f2be6bb5ae8a | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-19-03-GenAIExamples-HybridRag-personal_assistant.md | unknown | a565ed76-96be-49f4-9b51-f29b91f8523c | 19 | opea-semantic-v1 | 3c00acf86d545f29 | context - Retriever for graph DB: take in query, generate triplets (text2cypher) and extract context in graph db. - Merge this and generate final results.
Proof of Concept: Medical Assistant
- Focused on a subset of common diseases
- Built using Wikidata for unstructured medical knowledge. - Extracted and built structu... | ai_ref_knowledge | OPEA Documentation | context - Retriever for graph DB: take in query, generate triplets (text2cypher) and extract context in graph db. - Merge this and generate final results.
Proof of Concept: Medical Assistant
- Focused on a subset of common diseases
- Built using Wikidata for unstructured medical knowledge. - Extracted and built structu... | context - Retriever for graph DB: take in query, generate triplets (text2cypher) and extract context in graph db. - Merge this and generate final results.
Proof of Concept: Medical Assistant
- Focused on a subset of common diseases
- Built using Wikidata for unstructured medical knowledge. - Extracted and built structu... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
174a7b1e-bcdb-4dd7-8f04-dba403684e1b | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-19-03-GenAIExamples-HybridRag-personal_assistant.md | unknown | a565ed76-96be-49f4-9b51-f29b91f8523c | 14 | opea-semantic-v1 | 07d5fb2927bb5183 | Hybrid Integration:
- Simultaneously retrieves context from graph and vector databases during queries. - Outperforms individual approaches in retrieval accuracy and answer quality. | ai_ref_knowledge | OPEA Documentation | Hybrid Integration:
- Simultaneously retrieves context from graph and vector databases during queries. - Outperforms individual approaches in retrieval accuracy and answer quality. | Hybrid Integration:
- Simultaneously retrieves context from graph and vector databases during queries. - Outperforms individual approaches in retrieval accuracy and answer quality. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1ee4ec5b-66d2-4baf-a8bd-141afe2c430b | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-19-03-GenAIExamples-HybridRag-personal_assistant.md | unknown | a565ed76-96be-49f4-9b51-f29b91f8523c | 1 | opea-semantic-v1 | 3403eb5e555111eb | building advanced question-answering (Q&A) systems. HybridRAG combines two key techniques: 1. GraphRAG: Utilizes Knowledge Graphs (KGs) for information retrieval 2. VectorRAG: Employs vector-based retrieval methods
The integration of these techniques has demonstrated superior performance in generating accurate and cont... | ai_ref_knowledge | OPEA Documentation | building advanced question-answering (Q&A) systems. HybridRAG combines two key techniques: 1. GraphRAG: Utilizes Knowledge Graphs (KGs) for information retrieval 2. VectorRAG: Employs vector-based retrieval methods
The integration of these techniques has demonstrated superior performance in generating accurate and cont... | building advanced question-answering (Q&A) systems. HybridRAG combines two key techniques: 1. GraphRAG: Utilizes Knowledge Graphs (KGs) for information retrieval 2. VectorRAG: Employs vector-based retrieval methods
The integration of these techniques has demonstrated superior performance in generating accurate and cont... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
3155fb7d-f963-46c0-8e4e-2838864d2d0f | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-19-03-GenAIExamples-HybridRag-personal_assistant.md | unknown | a565ed76-96be-49f4-9b51-f29b91f8523c | 9 | opea-semantic-v1 | 6580f2c661407ce1 | Hybrid RAG approach offers a versatile solution for enterprise applications requiring advanced data retrieval and generation capabilities integrating unstructured and structured data from various sources.
## Motivation | ai_ref_knowledge | OPEA Documentation | Hybrid RAG approach offers a versatile solution for enterprise applications requiring advanced data retrieval and generation capabilities integrating unstructured and structured data from various sources.
## Motivation | Hybrid RAG approach offers a versatile solution for enterprise applications requiring advanced data retrieval and generation capabilities integrating unstructured and structured data from various sources.
## Motivation | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
32071922-4f3e-4105-9573-4f598e9f0dfd | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-19-03-GenAIExamples-HybridRag-personal_assistant.md | unknown | a565ed76-96be-49f4-9b51-f29b91f8523c | 3 | opea-semantic-v1 | 4a951c6b287e5b5a | - Enhances information extraction in Q&A systems - Combines the strengths of graph-based and vector-based retrieval - Improves both retrieval accuracy and answer generation quality
Some examples highlight the versatility and potential impact of the HybridRAG framework across different industries and use cases: Personal... | ai_ref_knowledge | OPEA Documentation | - Enhances information extraction in Q&A systems - Combines the strengths of graph-based and vector-based retrieval - Improves both retrieval accuracy and answer generation quality
Some examples highlight the versatility and potential impact of the HybridRAG framework across different industries and use cases: Personal... | - Enhances information extraction in Q&A systems - Combines the strengths of graph-based and vector-based retrieval - Improves both retrieval accuracy and answer generation quality
Some examples highlight the versatility and potential impact of the HybridRAG framework across different industries and use cases: Personal... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
35f77d7f-1399-4b21-af30-35f3ab6067b1 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-19-03-GenAIExamples-HybridRag-personal_assistant.md | unknown | a565ed76-96be-49f4-9b51-f29b91f8523c | 2 | opea-semantic-v1 | 58faafd677487fbd | The integration of these techniques has demonstrated superior performance in generating accurate and contextually relevant answers compared to using either method alone.
Key Features of HybridRAG
- Enhances information extraction in Q&A systems
- Combines the strengths of graph-based and vector-based retrieval
- Improv... | ai_ref_knowledge | OPEA Documentation | The integration of these techniques has demonstrated superior performance in generating accurate and contextually relevant answers compared to using either method alone.
Key Features of HybridRAG
- Enhances information extraction in Q&A systems
- Combines the strengths of graph-based and vector-based retrieval
- Improv... | The integration of these techniques has demonstrated superior performance in generating accurate and contextually relevant answers compared to using either method alone.
Key Features of HybridRAG
- Enhances information extraction in Q&A systems
- Combines the strengths of graph-based and vector-based retrieval
- Improv... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
3875a2fd-6fbd-4d7e-a409-9dd1a6c30a80 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-19-03-GenAIExamples-HybridRag-personal_assistant.md | unknown | a565ed76-96be-49f4-9b51-f29b91f8523c | 21 | opea-semantic-v1 | 1efa607e367c02f2 | vector and graph databases - The system then generates a final answer based on the combined retrieved information - The indexing for vector and graph
Performance Evaluation
HybridRAG outperforms traditional VectorRAG and GraphRAG techniques when used individually. This superior performance is observed in both: | ai_ref_knowledge | OPEA Documentation | vector and graph databases - The system then generates a final answer based on the combined retrieved information - The indexing for vector and graph
Performance Evaluation
HybridRAG outperforms traditional VectorRAG and GraphRAG techniques when used individually. This superior performance is observed in both: | vector and graph databases - The system then generates a final answer based on the combined retrieved information - The indexing for vector and graph
Performance Evaluation
HybridRAG outperforms traditional VectorRAG and GraphRAG techniques when used individually. This superior performance is observed in both: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
39f0c833-8baa-4028-94ec-f52fd223cec4 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-19-03-GenAIExamples-HybridRag-personal_assistant.md | unknown | a565ed76-96be-49f4-9b51-f29b91f8523c | 13 | opea-semantic-v1 | b40e7017e842367c | - Uses knowledge graphs to model entities, relationships, and hierarchical clusters from data. - Enables global context understanding (e.g., identifying indirect connections between entities).
VectorRAG: | ai_ref_knowledge | OPEA Documentation | - Uses knowledge graphs to model entities, relationships, and hierarchical clusters from data. - Enables global context understanding (e.g., identifying indirect connections between entities).
VectorRAG: | - Uses knowledge graphs to model entities, relationships, and hierarchical clusters from data. - Enables global context understanding (e.g., identifying indirect connections between entities).
VectorRAG: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4e6f8294-2270-4081-8c1b-ff964f6705ec | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-19-03-GenAIExamples-HybridRag-personal_assistant.md | unknown | a565ed76-96be-49f4-9b51-f29b91f8523c | 12 | opea-semantic-v1 | 749677f75a3f01cd | GraphRAG:
- Uses knowledge graphs to model entities, relationships, and hierarchical clusters from data. - Enables global context understanding (e.g., identifying indirect connections between entities). | ai_ref_knowledge | OPEA Documentation | GraphRAG:
- Uses knowledge graphs to model entities, relationships, and hierarchical clusters from data. - Enables global context understanding (e.g., identifying indirect connections between entities). | GraphRAG:
- Uses knowledge graphs to model entities, relationships, and hierarchical clusters from data. - Enables global context understanding (e.g., identifying indirect connections between entities). | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4ef2c5cd-94d8-447d-8609-caad6cee2cc9 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-19-03-GenAIExamples-HybridRag-personal_assistant.md | unknown | a565ed76-96be-49f4-9b51-f29b91f8523c | 10 | opea-semantic-v1 | 6b058f4e4d7d77d3 | ## Motivation
Enterprise AI systems require solutions that handle both structured data (databases, transactions, CSVs, JSON) and unstructured data (documents, images, audio). While traditional VectorRAG excels at semantic search across documents, it struggles with complex queries requiring global context or relationshi... | ai_ref_knowledge | OPEA Documentation | ## Motivation
Enterprise AI systems require solutions that handle both structured data (databases, transactions, CSVs, JSON) and unstructured data (documents, images, audio). While traditional VectorRAG excels at semantic search across documents, it struggles with complex queries requiring global context or relationshi... | ## Motivation
Enterprise AI systems require solutions that handle both structured data (databases, transactions, CSVs, JSON) and unstructured data (documents, images, audio). While traditional VectorRAG excels at semantic search across documents, it struggles with complex queries requiring global context or relationshi... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
563b5a1d-ea77-4f74-b0ff-a494960884c5 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-19-03-GenAIExamples-HybridRag-personal_assistant.md | unknown | a565ed76-96be-49f4-9b51-f29b91f8523c | 0 | opea-semantic-v1 | ed8bccebff9a1eeb | # HybridRAG
This RFC introduces the HybridRAG framework, a novel approach to building advanced question-answering (Q&A) systems. HybridRAG combines two key techniques:
1. GraphRAG: Utilizes Knowledge Graphs (KGs) for information retrieval
2. VectorRAG: Employs vector-based retrieval methods | ai_ref_knowledge | OPEA Documentation | # HybridRAG
This RFC introduces the HybridRAG framework, a novel approach to building advanced question-answering (Q&A) systems. HybridRAG combines two key techniques:
1. GraphRAG: Utilizes Knowledge Graphs (KGs) for information retrieval
2. VectorRAG: Employs vector-based retrieval methods | # HybridRAG
This RFC introduces the HybridRAG framework, a novel approach to building advanced question-answering (Q&A) systems. HybridRAG combines two key techniques:
1. GraphRAG: Utilizes Knowledge Graphs (KGs) for information retrieval
2. VectorRAG: Employs vector-based retrieval methods | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
57dee870-8efe-4f65-9d75-312f3e6a7c4b | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-19-03-GenAIExamples-HybridRag-personal_assistant.md | unknown | a565ed76-96be-49f4-9b51-f29b91f8523c | 7 | opea-semantic-v1 | 8e453b0e0db561c7 | We are building and example to showcase Medical Bot personal assistant application with:
- Preloaded database with detailed information on ~100 diseases
- Augmented with structured data on medicines, symptoms, home remedies, and care
- Demonstrates the architecture's capability in handling complex, multi-modal informat... | ai_ref_knowledge | OPEA Documentation | We are building and example to showcase Medical Bot personal assistant application with:
- Preloaded database with detailed information on ~100 diseases
- Augmented with structured data on medicines, symptoms, home remedies, and care
- Demonstrates the architecture's capability in handling complex, multi-modal informat... | We are building and example to showcase Medical Bot personal assistant application with:
- Preloaded database with detailed information on ~100 diseases
- Augmented with structured data on medicines, symptoms, home remedies, and care
- Demonstrates the architecture's capability in handling complex, multi-modal informat... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5810ba86-b70f-464d-8448-74a1d41522f6 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-19-03-GenAIExamples-HybridRag-personal_assistant.md | unknown | a565ed76-96be-49f4-9b51-f29b91f8523c | 8 | opea-semantic-v1 | ff9f2ad202d44a96 | on ~100 diseases - Augmented with structured data on medicines, symptoms, home remedies, and care - Demonstrates the architecture's capability in handling complex, multi-modal information
This Hybrid RAG approach offers a versatile solution for enterprise applications requiring advanced data retrieval and generation ca... | ai_ref_knowledge | OPEA Documentation | on ~100 diseases - Augmented with structured data on medicines, symptoms, home remedies, and care - Demonstrates the architecture's capability in handling complex, multi-modal information
This Hybrid RAG approach offers a versatile solution for enterprise applications requiring advanced data retrieval and generation ca... | on ~100 diseases - Augmented with structured data on medicines, symptoms, home remedies, and care - Demonstrates the architecture's capability in handling complex, multi-modal information
This Hybrid RAG approach offers a versatile solution for enterprise applications requiring advanced data retrieval and generation ca... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5f03d36e-b882-46ec-8037-2e6277bcf63a | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-19-03-GenAIExamples-HybridRag-personal_assistant.md | unknown | a565ed76-96be-49f4-9b51-f29b91f8523c | 4 | opea-semantic-v1 | e10f48dae8d477a3 | HybridRAG framework across different industries and use cases: Personal assistant, Insurance claim processing, Transaction processing, Fraud detection and risk management etc. to name a few.
## Author(s)
[Sharath Raghava] (https://github.com/intelsharath) | ai_ref_knowledge | OPEA Documentation | HybridRAG framework across different industries and use cases: Personal assistant, Insurance claim processing, Transaction processing, Fraud detection and risk management etc. to name a few.
## Author(s)
[Sharath Raghava] (https://github.com/intelsharath) | HybridRAG framework across different industries and use cases: Personal assistant, Insurance claim processing, Transaction processing, Fraud detection and risk management etc. to name a few.
## Author(s)
[Sharath Raghava] (https://github.com/intelsharath) | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
60f8e354-c7b8-4ef5-a837-5cec2b1c56c1 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-19-03-GenAIExamples-HybridRag-personal_assistant.md | unknown | a565ed76-96be-49f4-9b51-f29b91f8523c | 17 | opea-semantic-v1 | 52d1ee7c0f7be948 | 
The proposed architecture involves the creation of a Hybrid rag mega services. The megaservice functions as the core pipeline, comprising of the following microservices:
Indexing: | ai_ref_knowledge | OPEA Documentation | 
The proposed architecture involves the creation of a Hybrid rag mega services. The megaservice functions as the core pipeline, comprising of the following microservices:
Indexing: | 
The proposed architecture involves the creation of a Hybrid rag mega services. The megaservice functions as the core pipeline, comprising of the following microservices:
Indexing: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7e7934fb-2dde-4d09-a135-33f76fca08af | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-19-03-GenAIExamples-HybridRag-personal_assistant.md | unknown | a565ed76-96be-49f4-9b51-f29b91f8523c | 6 | opea-semantic-v1 | abdd3dc0527f3e2f | from unstructured data by extracting nodes and relationships. HybridRAG effectively handles both unstructured and structured data adopting a flexible microservice approach for enterprise AI applications.
We are building and example to showcase Medical Bot personal assistant application with: | ai_ref_knowledge | OPEA Documentation | from unstructured data by extracting nodes and relationships. HybridRAG effectively handles both unstructured and structured data adopting a flexible microservice approach for enterprise AI applications.
We are building and example to showcase Medical Bot personal assistant application with: | from unstructured data by extracting nodes and relationships. HybridRAG effectively handles both unstructured and structured data adopting a flexible microservice approach for enterprise AI applications.
We are building and example to showcase Medical Bot personal assistant application with: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8c87f2fe-a75d-4e6f-b77c-54646af3b1e8 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-19-03-GenAIExamples-HybridRag-personal_assistant.md | unknown | a565ed76-96be-49f4-9b51-f29b91f8523c | 11 | opea-semantic-v1 | 4452340b2f61db1d | context or relationship-aware reasoning. HybridRAG addresses these gaps by combining GraphRAG (knowledge graph-based retrieval) and VectorRAG (vector database retrieval) for enhanced accuracy and contextual relevance.
GraphRAG: | ai_ref_knowledge | OPEA Documentation | context or relationship-aware reasoning. HybridRAG addresses these gaps by combining GraphRAG (knowledge graph-based retrieval) and VectorRAG (vector database retrieval) for enhanced accuracy and contextual relevance.
GraphRAG: | context or relationship-aware reasoning. HybridRAG addresses these gaps by combining GraphRAG (knowledge graph-based retrieval) and VectorRAG (vector database retrieval) for enhanced accuracy and contextual relevance.
GraphRAG: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
9023fcf6-a857-42d5-8166-671f026d7bfd | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-19-03-GenAIExamples-HybridRag-personal_assistant.md | unknown | a565ed76-96be-49f4-9b51-f29b91f8523c | 15 | opea-semantic-v1 | ad622b59a76db33c | - Simultaneously retrieves context from graph and vector databases during queries. - Outperforms individual approaches in retrieval accuracy and answer quality.
HybridRAG’s hybrid architecture also supports secure on-premise/cloud deployments, critical for sensitive sectors like healthcare. This approach represents a s... | ai_ref_knowledge | OPEA Documentation | - Simultaneously retrieves context from graph and vector databases during queries. - Outperforms individual approaches in retrieval accuracy and answer quality.
HybridRAG’s hybrid architecture also supports secure on-premise/cloud deployments, critical for sensitive sectors like healthcare. This approach represents a s... | - Simultaneously retrieves context from graph and vector databases during queries. - Outperforms individual approaches in retrieval accuracy and answer quality.
HybridRAG’s hybrid architecture also supports secure on-premise/cloud deployments, critical for sensitive sectors like healthcare. This approach represents a s... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
9b104e39-49e7-403b-8438-6903b2bff904 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-19-03-GenAIExamples-HybridRag-personal_assistant.md | unknown | a565ed76-96be-49f4-9b51-f29b91f8523c | 5 | opea-semantic-v1 | 8d4f10ef8835a093 | Under Review
## Objective
This RFC proposes a Hybrid RAG architecture framework that enhances RAG technology. The input data can be unstructured data or structured data sources. Structured data sources such as CSVs, SQL databases can be used to integrate graph sources. Structured information can also be extracted from ... | ai_ref_knowledge | OPEA Documentation | Under Review
## Objective
This RFC proposes a Hybrid RAG architecture framework that enhances RAG technology. The input data can be unstructured data or structured data sources. Structured data sources such as CSVs, SQL databases can be used to integrate graph sources. Structured information can also be extracted from ... | Under Review
## Objective
This RFC proposes a Hybrid RAG architecture framework that enhances RAG technology. The input data can be unstructured data or structured data sources. Structured data sources such as CSVs, SQL databases can be used to integrate graph sources. Structured information can also be extracted from ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
edbfd03b-280b-441f-89ff-e5f6a6d8278e | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-19-03-GenAIExamples-HybridRag-personal_assistant.md | unknown | a565ed76-96be-49f4-9b51-f29b91f8523c | 20 | opea-semantic-v1 | 9b58dfc2c664f04a | - Extracted and built structured dataset from unstructured medical knowledge (diseases, symptoms, treatments, home remedies). - Designed to provide more precise answers to medical queries
How It Works
- When a query is received, HybridRAG retrieves relevant context from both vector and graph databases
- The system then... | ai_ref_knowledge | OPEA Documentation | - Extracted and built structured dataset from unstructured medical knowledge (diseases, symptoms, treatments, home remedies). - Designed to provide more precise answers to medical queries
How It Works
- When a query is received, HybridRAG retrieves relevant context from both vector and graph databases
- The system then... | - Extracted and built structured dataset from unstructured medical knowledge (diseases, symptoms, treatments, home remedies). - Designed to provide more precise answers to medical queries
How It Works
- When a query is received, HybridRAG retrieves relevant context from both vector and graph databases
- The system then... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f90745e3-2b0c-4b04-bf4d-ac6d650095f4 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-19-03-GenAIExamples-HybridRag-personal_assistant.md | unknown | a565ed76-96be-49f4-9b51-f29b91f8523c | 18 | opea-semantic-v1 | ab1327e55949e452 | The proposed architecture involves the creation of a Hybrid rag mega services. The megaservice functions as the core pipeline, comprising of the following microservices: Indexing:
- Unstructured data into semantic chunk and loading into vector db. - Structured data load CSV/SQLdb into graph DB. - Optional unstructured ... | ai_ref_knowledge | OPEA Documentation | The proposed architecture involves the creation of a Hybrid rag mega services. The megaservice functions as the core pipeline, comprising of the following microservices: Indexing:
- Unstructured data into semantic chunk and loading into vector db. - Structured data load CSV/SQLdb into graph DB. - Optional unstructured ... | The proposed architecture involves the creation of a Hybrid rag mega services. The megaservice functions as the core pipeline, comprising of the following microservices: Indexing:
- Unstructured data into semantic chunk and loading into vector db. - Structured data load CSV/SQLdb into graph DB. - Optional unstructured ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0550852e-1061-4812-a147-bfdd1ab4acc3 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-23-04-GenAIComps-001-Air-Gap-Support.md | unknown | 1b59c519-3e7f-4844-a484-d0f6adeb8dd6 | 17 | opea-semantic-v1 | 690c5e09e15b0a6d | We also need to automate downloading of that data, and document how to `mount` in the deployment document of that microservice.
### Other hard-coded data | ai_ref_knowledge | OPEA Documentation | We also need to automate downloading of that data, and document how to `mount` in the deployment document of that microservice.
### Other hard-coded data | We also need to automate downloading of that data, and document how to `mount` in the deployment document of that microservice.
### Other hard-coded data | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
149a9e0f-57ff-416c-ab72-c7e18029aec5 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-23-04-GenAIComps-001-Air-Gap-Support.md | unknown | 1b59c519-3e7f-4844-a484-d0f6adeb8dd6 | 4 | opea-semantic-v1 | aadefbca23647310 | be pre-downloaded and used by the OPEA microservices running in air-gapped environment, but it's rather manual process and not documented for all the applications yet.
#### Hard-coded AI model data
Some OPEA microservices silently download some AI model data from the Internet during runtime. This kind of data to be dow... | ai_ref_knowledge | OPEA Documentation | be pre-downloaded and used by the OPEA microservices running in air-gapped environment, but it's rather manual process and not documented for all the applications yet.
#### Hard-coded AI model data
Some OPEA microservices silently download some AI model data from the Internet during runtime. This kind of data to be dow... | be pre-downloaded and used by the OPEA microservices running in air-gapped environment, but it's rather manual process and not documented for all the applications yet.
#### Hard-coded AI model data
Some OPEA microservices silently download some AI model data from the Internet during runtime. This kind of data to be dow... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
15a5a96d-6d11-4725-907c-028c04a0f0f4 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-23-04-GenAIComps-001-Air-Gap-Support.md | unknown | 1b59c519-3e7f-4844-a484-d0f6adeb8dd6 | 20 | opea-semantic-v1 | f839283a0065aafb | size significantly. For large size data, we should follow the `Hard-coded AI model data` method to support running the microservice in the air gapped mode.
## Alternatives Considered | ai_ref_knowledge | OPEA Documentation | size significantly. For large size data, we should follow the `Hard-coded AI model data` method to support running the microservice in the air gapped mode.
## Alternatives Considered | size significantly. For large size data, we should follow the `Hard-coded AI model data` method to support running the microservice in the air gapped mode.
## Alternatives Considered | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
3797cac6-adc3-4ab3-94ca-bd315e372ecd | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-23-04-GenAIComps-001-Air-Gap-Support.md | unknown | 1b59c519-3e7f-4844-a484-d0f6adeb8dd6 | 11 | opea-semantic-v1 | 0f4454fa864e4843 | quickly verify if a OPEA microservice support air-gapped mode or not, and what kind of online data it's downloading, we can use the following steps:
1. Deploy the microservice in one of the 3 following environment:
- A real air-gapped environment (Docker or K8s)
- A K8s environment which disables K8s DNS forwarding(i... | ai_ref_knowledge | OPEA Documentation | quickly verify if a OPEA microservice support air-gapped mode or not, and what kind of online data it's downloading, we can use the following steps:
1. Deploy the microservice in one of the 3 following environment:
- A real air-gapped environment (Docker or K8s)
- A K8s environment which disables K8s DNS forwarding(i... | quickly verify if a OPEA microservice support air-gapped mode or not, and what kind of online data it's downloading, we can use the following steps:
1. Deploy the microservice in one of the 3 following environment:
- A real air-gapped environment (Docker or K8s)
- A K8s environment which disables K8s DNS forwarding(i... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
38a144ff-9ed7-4e2a-9462-347d835b7a4e | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-23-04-GenAIComps-001-Air-Gap-Support.md | unknown | 1b59c519-3e7f-4844-a484-d0f6adeb8dd6 | 10 | opea-semantic-v1 | e39825bb89286539 | ## Design Proposal
### Ways to verify
To quickly verify if a OPEA microservice support air-gapped mode or not, and what kind of online data it's downloading, we can use the following steps: | ai_ref_knowledge | OPEA Documentation | ## Design Proposal
### Ways to verify
To quickly verify if a OPEA microservice support air-gapped mode or not, and what kind of online data it's downloading, we can use the following steps: | ## Design Proposal
### Ways to verify
To quickly verify if a OPEA microservice support air-gapped mode or not, and what kind of online data it's downloading, we can use the following steps: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4029c41d-0f69-4da4-a2d5-d64f027c8078 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-23-04-GenAIComps-001-Air-Gap-Support.md | unknown | 1b59c519-3e7f-4844-a484-d0f6adeb8dd6 | 24 | opea-semantic-v1 | 6070672594438a0f | - Engineering Impact: - increase container image build time - decrease the container image startup time.
- Staging plan:
- Using the methods listed in the above section `Ways to verify` to find all the microservices which does not support air-gapped mode, and create corresponding Github issues. - For each such microse... | ai_ref_knowledge | OPEA Documentation | - Engineering Impact: - increase container image build time - decrease the container image startup time.
- Staging plan:
- Using the methods listed in the above section `Ways to verify` to find all the microservices which does not support air-gapped mode, and create corresponding Github issues. - For each such microse... | - Engineering Impact: - increase container image build time - decrease the container image startup time.
- Staging plan:
- Using the methods listed in the above section `Ways to verify` to find all the microservices which does not support air-gapped mode, and create corresponding Github issues. - For each such microse... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
60cea64e-fc9f-4cd4-8b38-5a1596d5047a | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-23-04-GenAIComps-001-Air-Gap-Support.md | unknown | 1b59c519-3e7f-4844-a484-d0f6adeb8dd6 | 22 | opea-semantic-v1 | 9d0f91ea5fda1b69 | into the microservice's runtime. However, since different microservices may use different forms of hard-coded online data, this methods introduces much more complexity to the deployment.
## Compatibility | ai_ref_knowledge | OPEA Documentation | into the microservice's runtime. However, since different microservices may use different forms of hard-coded online data, this methods introduces much more complexity to the deployment.
## Compatibility | into the microservice's runtime. However, since different microservices may use different forms of hard-coded online data, this methods introduces much more complexity to the deployment.
## Compatibility | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
67c91624-df65-4fc5-aa99-1c018e793d43 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-23-04-GenAIComps-001-Air-Gap-Support.md | unknown | 1b59c519-3e7f-4844-a484-d0f6adeb8dd6 | 5 | opea-semantic-v1 | be902899a999ea19 | the `dataprep` microservice requires to download the AI model `unstructuredio/yolo_x_layout` during runtime to process unstructured input data, and currently is not configurable by the end-user.
#### Other hard-coded data
Some OPEA microservices silently download additional data other than AI models during runtime, e.g... | ai_ref_knowledge | OPEA Documentation | the `dataprep` microservice requires to download the AI model `unstructuredio/yolo_x_layout` during runtime to process unstructured input data, and currently is not configurable by the end-user.
#### Other hard-coded data
Some OPEA microservices silently download additional data other than AI models during runtime, e.g... | the `dataprep` microservice requires to download the AI model `unstructuredio/yolo_x_layout` during runtime to process unstructured input data, and currently is not configurable by the end-user.
#### Other hard-coded data
Some OPEA microservices silently download additional data other than AI models during runtime, e.g... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
693836c0-c2c3-4d6c-80a3-c0f44e11ea59 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-23-04-GenAIComps-001-Air-Gap-Support.md | unknown | 1b59c519-3e7f-4844-a484-d0f6adeb8dd6 | 25 | opea-semantic-v1 | 52da0aa1b503ede0 | mode, and create corresponding Github issues. - For each such microservice, figure out the online data type and enhance it to support air gapped mode.
- CI env to test this functionality: Since this is a common requirement to all OPEA microservices, we need to setup an CI/CD test task. | ai_ref_knowledge | OPEA Documentation | mode, and create corresponding Github issues. - For each such microservice, figure out the online data type and enhance it to support air gapped mode.
- CI env to test this functionality: Since this is a common requirement to all OPEA microservices, we need to setup an CI/CD test task. | mode, and create corresponding Github issues. - For each such microservice, figure out the online data type and enhance it to support air gapped mode.
- CI env to test this functionality: Since this is a common requirement to all OPEA microservices, we need to setup an CI/CD test task. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
78850247-8710-4adb-908e-3f30687c5582 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-23-04-GenAIComps-001-Air-Gap-Support.md | unknown | 1b59c519-3e7f-4844-a484-d0f6adeb8dd6 | 14 | opea-semantic-v1 | fdb6a37589e88c08 | requests return status and microservice logs to find out whether it supports air-gapped mode and what kind of online data it's downloading if it's not.
### Hard-coded AI model data | ai_ref_knowledge | OPEA Documentation | requests return status and microservice logs to find out whether it supports air-gapped mode and what kind of online data it's downloading if it's not.
### Hard-coded AI model data | requests return status and microservice logs to find out whether it supports air-gapped mode and what kind of online data it's downloading if it's not.
### Hard-coded AI model data | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
85b8ca1d-0647-477f-a1ef-2184b0c07d1b | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-23-04-GenAIComps-001-Air-Gap-Support.md | unknown | 1b59c519-3e7f-4844-a484-d0f6adeb8dd6 | 21 | opea-semantic-v1 | 1b7ab548b026ee08 | ## Alternatives Considered
Using the `Hard-coded AI model data` method for `Other hard-coded data` type, a.k.a pre-download the other hard coded data and make sure there is a way to `mount` the pre-downloaded data into the microservice's runtime. However, since different microservices may use different forms of hard-co... | ai_ref_knowledge | OPEA Documentation | ## Alternatives Considered
Using the `Hard-coded AI model data` method for `Other hard-coded data` type, a.k.a pre-download the other hard coded data and make sure there is a way to `mount` the pre-downloaded data into the microservice's runtime. However, since different microservices may use different forms of hard-co... | ## Alternatives Considered
Using the `Hard-coded AI model data` method for `Other hard-coded data` type, a.k.a pre-download the other hard coded data and make sure there is a way to `mount` the pre-downloaded data into the microservice's runtime. However, since different microservices may use different forms of hard-co... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
99835be0-cbcd-4c33-b1f9-5ecdfc49607b | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-23-04-GenAIComps-001-Air-Gap-Support.md | unknown | 1b59c519-3e7f-4844-a484-d0f6adeb8dd6 | 19 | opea-semantic-v1 | b8bf791d0b782664 | shared by multiple microservices, we should have them downloaded in the container image, so that the microservice itself doesn't need to download it during runtime.
For online data which are used by multiple microservices, e.g. `nltk` data, depends on its size, we can have them pre downloaded in the container image if ... | ai_ref_knowledge | OPEA Documentation | shared by multiple microservices, we should have them downloaded in the container image, so that the microservice itself doesn't need to download it during runtime.
For online data which are used by multiple microservices, e.g. `nltk` data, depends on its size, we can have them pre downloaded in the container image if ... | shared by multiple microservices, we should have them downloaded in the container image, so that the microservice itself doesn't need to download it during runtime.
For online data which are used by multiple microservices, e.g. `nltk` data, depends on its size, we can have them pre downloaded in the container image if ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
9aede770-d5c2-4dff-a6d3-d4b6b4e1f43a | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-23-04-GenAIComps-001-Air-Gap-Support.md | unknown | 1b59c519-3e7f-4844-a484-d0f6adeb8dd6 | 23 | opea-semantic-v1 | cff139dc2e9204d5 | List other information user and developer may care about, such as:
- Engineering Impact:
- increase container image build time
- decrease the container image startup time. | ai_ref_knowledge | OPEA Documentation | List other information user and developer may care about, such as:
- Engineering Impact:
- increase container image build time
- decrease the container image startup time. | List other information user and developer may care about, such as:
- Engineering Impact:
- increase container image build time
- decrease the container image startup time. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a37657f2-f13f-46fe-b733-cf2ea3b7b795 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-23-04-GenAIComps-001-Air-Gap-Support.md | unknown | 1b59c519-3e7f-4844-a484-d0f6adeb8dd6 | 16 | opea-semantic-v1 | fafc43366057eb18 | a way to `mount` the user pre-downloaded AI model data into the microservice's runtime so that the microservice itself can run in the air-gapped environment.
We also need to automate downloading of that data, and document how to `mount` in the deployment document of that microservice. | ai_ref_knowledge | OPEA Documentation | a way to `mount` the user pre-downloaded AI model data into the microservice's runtime so that the microservice itself can run in the air-gapped environment.
We also need to automate downloading of that data, and document how to `mount` in the deployment document of that microservice. | a way to `mount` the user pre-downloaded AI model data into the microservice's runtime so that the microservice itself can run in the air-gapped environment.
We also need to automate downloading of that data, and document how to `mount` in the deployment document of that microservice. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
aa264aca-667a-4764-bf0c-86ba9ba0100d | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-23-04-GenAIComps-001-Air-Gap-Support.md | unknown | 1b59c519-3e7f-4844-a484-d0f6adeb8dd6 | 18 | opea-semantic-v1 | 5b30875311f5aaed | ### Other hard-coded data
To minimize the deployment complexity, for small size online data which is NOT shared by multiple microservices, we should have them downloaded in the container image, so that the microservice itself doesn't need to download it during runtime. | ai_ref_knowledge | OPEA Documentation | ### Other hard-coded data
To minimize the deployment complexity, for small size online data which is NOT shared by multiple microservices, we should have them downloaded in the container image, so that the microservice itself doesn't need to download it during runtime. | ### Other hard-coded data
To minimize the deployment complexity, for small size online data which is NOT shared by multiple microservices, we should have them downloaded in the container image, so that the microservice itself doesn't need to download it during runtime. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
aa502424-29ad-43a3-a667-803a0867688b | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-23-04-GenAIComps-001-Air-Gap-Support.md | unknown | 1b59c519-3e7f-4844-a484-d0f6adeb8dd6 | 9 | opea-semantic-v1 | 9feeb9ab88d91547 | requiring special tweaks to download. We want to make sure that all OPEA microservices can be run in the air-gapped environment in a uniform way.
## Design Proposal | ai_ref_knowledge | OPEA Documentation | requiring special tweaks to download. We want to make sure that all OPEA microservices can be run in the air-gapped environment in a uniform way.
## Design Proposal | requiring special tweaks to download. We want to make sure that all OPEA microservices can be run in the air-gapped environment in a uniform way.
## Design Proposal | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c13303a4-d0ff-4bef-bd8f-3e8402630988 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-23-04-GenAIComps-001-Air-Gap-Support.md | unknown | 1b59c519-3e7f-4844-a484-d0f6adeb8dd6 | 1 | opea-semantic-v1 | c06d9fdb861b932f | network security policies that prohibit the computers in the enterprise internal network to have any kind of ability to send/receive data to/from the outside networks.
This RFC discusses how to support running OPEA microservices in such an air-gapped environment, including the methodology to find out the what kind of d... | ai_ref_knowledge | OPEA Documentation | network security policies that prohibit the computers in the enterprise internal network to have any kind of ability to send/receive data to/from the outside networks.
This RFC discusses how to support running OPEA microservices in such an air-gapped environment, including the methodology to find out the what kind of d... | network security policies that prohibit the computers in the enterprise internal network to have any kind of ability to send/receive data to/from the outside networks.
This RFC discusses how to support running OPEA microservices in such an air-gapped environment, including the methodology to find out the what kind of d... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
cdea6ce9-22d6-4ee5-b1c3-cfea77f00251 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-23-04-GenAIComps-001-Air-Gap-Support.md | unknown | 1b59c519-3e7f-4844-a484-d0f6adeb8dd6 | 6 | opea-semantic-v1 | 4dcf8d9999a27b09 | AI models during runtime, e.g. `dataprep`, `retriever` and `gpt-sovits` need to download a subset of `nltk` data, `speecht5` needs to download data from [intel-extension-for-transformers](https://github.com/intel/intel-extension-for-transformers/tree/main/intel_extension_for_transformers/neural_chat/assets/speaker_embe... | ai_ref_knowledge | OPEA Documentation | AI models during runtime, e.g. `dataprep`, `retriever` and `gpt-sovits` need to download a subset of `nltk` data, `speecht5` needs to download data from [intel-extension-for-transformers](https://github.com/intel/intel-extension-for-transformers/tree/main/intel_extension_for_transformers/neural_chat/assets/speaker_embe... | AI models during runtime, e.g. `dataprep`, `retriever` and `gpt-sovits` need to download a subset of `nltk` data, `speecht5` needs to download data from [intel-extension-for-transformers](https://github.com/intel/intel-extension-for-transformers/tree/main/intel_extension_for_transformers/neural_chat/assets/speaker_embe... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d3681a92-b814-429a-8c0f-5f58c19c304b | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-23-04-GenAIComps-001-Air-Gap-Support.md | unknown | 1b59c519-3e7f-4844-a484-d0f6adeb8dd6 | 12 | opea-semantic-v1 | 46eef231fe301d92 | edit cm coredns`, and restart `coredns` related pods) - Environment requiring proxy, with `http_proxy` and `https_proxy` set to a non existent proxy servers, e.g. `http://localhost:54321`.
2. Send requests to the microservice under verification
We need to make sure that all the sent out requests should have a decent c... | ai_ref_knowledge | OPEA Documentation | edit cm coredns`, and restart `coredns` related pods) - Environment requiring proxy, with `http_proxy` and `https_proxy` set to a non existent proxy servers, e.g. `http://localhost:54321`.
2. Send requests to the microservice under verification
We need to make sure that all the sent out requests should have a decent c... | edit cm coredns`, and restart `coredns` related pods) - Environment requiring proxy, with `http_proxy` and `https_proxy` set to a non existent proxy servers, e.g. `http://localhost:54321`.
2. Send requests to the microservice under verification
We need to make sure that all the sent out requests should have a decent c... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
db626ae4-8862-4ebc-afd7-5d38e33b7df5 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-23-04-GenAIComps-001-Air-Gap-Support.md | unknown | 1b59c519-3e7f-4844-a484-d0f6adeb8dd6 | 7 | opea-semantic-v1 | ab2e0c7bc929e038 | In this RFC, we'll mainly cover the hard-coded online data, given that user-configurable model data is already handled for pre-download.
## Motivation | ai_ref_knowledge | OPEA Documentation | In this RFC, we'll mainly cover the hard-coded online data, given that user-configurable model data is already handled for pre-download.
## Motivation | In this RFC, we'll mainly cover the hard-coded online data, given that user-configurable model data is already handled for pre-download.
## Motivation | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
dc513ba1-30d9-4349-9f21-02aed2c5d150 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-23-04-GenAIComps-001-Air-Gap-Support.md | unknown | 1b59c519-3e7f-4844-a484-d0f6adeb8dd6 | 3 | opea-semantic-v1 | 99d075be7d6b08a4 | ### Online data types There are some OPEA microservices require downloading data from Internet during runtime, this kind of data includes:
#### User configurable AI model data
Some OPEA microservices allow the user to configure the AI model to use. Model data can already be pre-downloaded and used by the OPEA microserv... | ai_ref_knowledge | OPEA Documentation | ### Online data types There are some OPEA microservices require downloading data from Internet during runtime, this kind of data includes:
#### User configurable AI model data
Some OPEA microservices allow the user to configure the AI model to use. Model data can already be pre-downloaded and used by the OPEA microserv... | ### Online data types There are some OPEA microservices require downloading data from Internet during runtime, this kind of data includes:
#### User configurable AI model data
Some OPEA microservices allow the user to configure the AI model to use. Model data can already be pre-downloaded and used by the OPEA microserv... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e40648ff-80e7-49cf-a026-78f7b79215dc | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-23-04-GenAIComps-001-Air-Gap-Support.md | unknown | 1b59c519-3e7f-4844-a484-d0f6adeb8dd6 | 2 | opea-semantic-v1 | 6d452e12ce4db7bb | find out the what kind of data need to be pre-downloaded, where to store the pre-downloaded data, and how this will affect the deployment, etc.
### Online data types
There are some OPEA microservices require downloading data from Internet during runtime, this kind of data includes: | ai_ref_knowledge | OPEA Documentation | find out the what kind of data need to be pre-downloaded, where to store the pre-downloaded data, and how this will affect the deployment, etc.
### Online data types
There are some OPEA microservices require downloading data from Internet during runtime, this kind of data includes: | find out the what kind of data need to be pre-downloaded, where to store the pre-downloaded data, and how this will affect the deployment, etc.
### Online data types
There are some OPEA microservices require downloading data from Internet during runtime, this kind of data includes: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e8534b61-f4d4-4e73-bbd2-e1ccea8455a2 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-23-04-GenAIComps-001-Air-Gap-Support.md | unknown | 1b59c519-3e7f-4844-a484-d0f6adeb8dd6 | 13 | opea-semantic-v1 | 1f22b8e40d7d9ff3 | decent coverage of the internal data flow of that microservices, because sometimes it's not the microservice itself is downloading online data, but the dependent modules.
3. Check the requests return status and microservice logs to find out whether it supports air-gapped mode and what kind of online data it's downloadi... | ai_ref_knowledge | OPEA Documentation | decent coverage of the internal data flow of that microservices, because sometimes it's not the microservice itself is downloading online data, but the dependent modules.
3. Check the requests return status and microservice logs to find out whether it supports air-gapped mode and what kind of online data it's downloadi... | decent coverage of the internal data flow of that microservices, because sometimes it's not the microservice itself is downloading online data, but the dependent modules.
3. Check the requests return status and microservice logs to find out whether it supports air-gapped mode and what kind of online data it's downloadi... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
fb1af349-c41e-4bc7-bebb-7b5ef4f088cf | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-23-04-GenAIComps-001-Air-Gap-Support.md | unknown | 1b59c519-3e7f-4844-a484-d0f6adeb8dd6 | 8 | opea-semantic-v1 | ba6eaf189f3a5239 | ## Motivation
When trying to deploy the OPEA microservices in some customer air-gapped environments, we've found that there are quite some OPEA microservices that need to download data from the internet during runtime, many of them requiring special tweaks to download. We want to make sure that all OPEA microservices c... | ai_ref_knowledge | OPEA Documentation | ## Motivation
When trying to deploy the OPEA microservices in some customer air-gapped environments, we've found that there are quite some OPEA microservices that need to download data from the internet during runtime, many of them requiring special tweaks to download. We want to make sure that all OPEA microservices c... | ## Motivation
When trying to deploy the OPEA microservices in some customer air-gapped environments, we've found that there are quite some OPEA microservices that need to download data from the internet during runtime, many of them requiring special tweaks to download. We want to make sure that all OPEA microservices c... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ff524b82-703e-43a7-a2c9-5baebe7cf4ad | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-23-04-GenAIComps-001-Air-Gap-Support.md | unknown | 1b59c519-3e7f-4844-a484-d0f6adeb8dd6 | 15 | opea-semantic-v1 | 06dbeabd073dd667 | ### Hard-coded AI model data
Since we're not allowed to distribute AI model data in the microservice's container image, we need treat that like user-configurable model data; make sure there is a way to `mount` the user pre-downloaded AI model data into the microservice's runtime so that the microservice itself can run ... | ai_ref_knowledge | OPEA Documentation | ### Hard-coded AI model data
Since we're not allowed to distribute AI model data in the microservice's container image, we need treat that like user-configurable model data; make sure there is a way to `mount` the user pre-downloaded AI model data into the microservice's runtime so that the microservice itself can run ... | ### Hard-coded AI model data
Since we're not allowed to distribute AI model data in the microservice's container image, we need treat that like user-configurable model data; make sure there is a way to `mount` the user pre-downloaded AI model data into the microservice's runtime so that the microservice itself can run ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ffca7f3b-2401-4e30-ae1e-51825cfce4d8 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/25-23-04-GenAIComps-001-Air-Gap-Support.md | unknown | 1b59c519-3e7f-4844-a484-d0f6adeb8dd6 | 0 | opea-semantic-v1 | 65b0dfe6d5cb475d | ## Objective
An air-gapped computer or network is one that has no network interfaces, either wired or wireless, connected to outside networks(e.g. Internet, etc.). Many enterprises have network security policies that prohibit the computers in the enterprise internal network to have any kind of ability to send/receive d... | ai_ref_knowledge | OPEA Documentation | ## Objective
An air-gapped computer or network is one that has no network interfaces, either wired or wireless, connected to outside networks(e.g. Internet, etc.). Many enterprises have network security policies that prohibit the computers in the enterprise internal network to have any kind of ability to send/receive d... | ## Objective
An air-gapped computer or network is one that has no network interfaces, either wired or wireless, connected to outside networks(e.g. Internet, etc.). Many enterprises have network security policies that prohibit the computers in the enterprise internal network to have any kind of ability to send/receive d... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b07fbf85-43a2-4a90-b2e6-0956b9c1c845 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/README.md | unknown | 0fde9c2c-66f4-4545-aa2b-f474788cbcbc | 0 | opea-semantic-v1 | f9047ed0e86e02ef | # RFC Archive
This folder is used to archive all RFCs contributed by OPEA community. Either users directly contribute RFC to this folder or submit to each OPEA repository's `Issues` page with the `[RFC]: xxx` string pattern in title. The latter will be automatically stored to here by an archieve tool. | ai_ref_knowledge | OPEA Documentation | # RFC Archive
This folder is used to archive all RFCs contributed by OPEA community. Either users directly contribute RFC to this folder or submit to each OPEA repository's `Issues` page with the `[RFC]: xxx` string pattern in title. The latter will be automatically stored to here by an archieve tool. | # RFC Archive
This folder is used to archive all RFCs contributed by OPEA community. Either users directly contribute RFC to this folder or submit to each OPEA repository's `Issues` page with the `[RFC]: xxx` string pattern in title. The latter will be automatically stored to here by an archieve tool. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b1fcb59f-1790-41eb-b0c9-f67b321ddd93 | OPEA Documentation | file://datasets/opea-docs/community/rfcs/README.md | unknown | 0fde9c2c-66f4-4545-aa2b-f474788cbcbc | 1 | opea-semantic-v1 | d52357423d78489b | each OPEA repository's `Issues` page with the `[RFC]: xxx` string pattern in title. The latter will be automatically stored to here by an archieve tool.
The file naming convention follows this rule: yy-mm-dd-[OPEA Project Name]-[index]-title.md | ai_ref_knowledge | OPEA Documentation | each OPEA repository's `Issues` page with the `[RFC]: xxx` string pattern in title. The latter will be automatically stored to here by an archieve tool.
The file naming convention follows this rule: yy-mm-dd-[OPEA Project Name]-[index]-title.md | each OPEA repository's `Issues` page with the `[RFC]: xxx` string pattern in title. The latter will be automatically stored to here by an archieve tool.
The file naming convention follows this rule: yy-mm-dd-[OPEA Project Name]-[index]-title.md | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
19787276-0653-4ac9-92a2-9807584ed8e1 | OPEA Documentation | file://datasets/opea-docs/developer-guides/primary_readme_genai_examples_template.md | unknown | 544a9d38-28c7-4bcc-8b3c-b8f7fa2b7255 | 6 | opea-semantic-v1 | 53188f42efe8c102 | This table describes how you deploy the <sample name> application on cloud service providers (CSP) using Terraform:
| CSP | Hardware | Cloud Module for Terraform | Notes |
| ------------------------------------| ------------------------------ | ---------------------------------------------------------------------------... | ai_ref_knowledge | OPEA Documentation | This table describes how you deploy the <sample name> application on cloud service providers (CSP) using Terraform:
| CSP | Hardware | Cloud Module for Terraform | Notes |
| ------------------------------------| ------------------------------ | ---------------------------------------------------------------------------... | This table describes how you deploy the <sample name> application on cloud service providers (CSP) using Terraform:
| CSP | Hardware | Cloud Module for Terraform | Notes |
| ------------------------------------| ------------------------------ | ---------------------------------------------------------------------------... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
40ea8170-6c91-4009-9795-b09fad0e4531 | OPEA Documentation | file://datasets/opea-docs/developer-guides/primary_readme_genai_examples_template.md | unknown | 544a9d38-28c7-4bcc-8b3c-b8f7fa2b7255 | 4 | opea-semantic-v1 | 045e56ec1df23b51 | | Hardware A <link to secondary README> | | Intel | Single Node with Benchmarking | Hardware B <link to secondary README> | | ...
| HELM | <link to secondary README> | | ai_ref_knowledge | OPEA Documentation | | Hardware A <link to secondary README> | | Intel | Single Node with Benchmarking | Hardware B <link to secondary README> | | ...
| HELM | <link to secondary README> | | | Hardware A <link to secondary README> | | Intel | Single Node with Benchmarking | Hardware B <link to secondary README> | | ...
| HELM | <link to secondary README> | | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
88b62542-c0f7-4a69-9b24-4d88dde3f044 | OPEA Documentation | file://datasets/opea-docs/developer-guides/primary_readme_genai_examples_template.md | unknown | 544a9d38-28c7-4bcc-8b3c-b8f7fa2b7255 | 0 | opea-semantic-v1 | 4213bfd4fa358555 | ## Contents - Overview - Architecture - Deployment
## Overview
<What does this sample do?>
<Where can developers use this sample?>
<What tasks or problems can this sample solve?> | ai_ref_knowledge | OPEA Documentation | ## Contents - Overview - Architecture - Deployment
## Overview
<What does this sample do?>
<Where can developers use this sample?>
<What tasks or problems can this sample solve?> | ## Contents - Overview - Architecture - Deployment
## Overview
<What does this sample do?>
<Where can developers use this sample?>
<What tasks or problems can this sample solve?> | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8e011555-cb31-4cb3-9334-26fa2c26d52b | OPEA Documentation | file://datasets/opea-docs/developer-guides/primary_readme_genai_examples_template.md | unknown | 544a9d38-28c7-4bcc-8b3c-b8f7fa2b7255 | 5 | opea-semantic-v1 | dc281f9f2da22e13 | | HELM | <link to secondary README> |
This table describes how you deploy the <sample name> application on cloud service providers (CSP) using Terraform: | ai_ref_knowledge | OPEA Documentation | | HELM | <link to secondary README> |
This table describes how you deploy the <sample name> application on cloud service providers (CSP) using Terraform: | | HELM | <link to secondary README> |
This table describes how you deploy the <sample name> application on cloud service providers (CSP) using Terraform: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a95fd4c4-bfcc-4eda-a592-48528874a35f | OPEA Documentation | file://datasets/opea-docs/developer-guides/primary_readme_genai_examples_template.md | unknown | 544a9d38-28c7-4bcc-8b3c-b8f7fa2b7255 | 2 | opea-semantic-v1 | 6e777affaca376f4 | <What is the flow of information? Include a flow diagram, if available.> <What OPEA microservices does this sample use? Include an architecture diagram, if available.>
## Deployment
This table describes options to deploy the <sample name> application. See relevant secondary README files to learn more about these option... | ai_ref_knowledge | OPEA Documentation | <What is the flow of information? Include a flow diagram, if available.> <What OPEA microservices does this sample use? Include an architecture diagram, if available.>
## Deployment
This table describes options to deploy the <sample name> application. See relevant secondary README files to learn more about these option... | <What is the flow of information? Include a flow diagram, if available.> <What OPEA microservices does this sample use? Include an architecture diagram, if available.>
## Deployment
This table describes options to deploy the <sample name> application. See relevant secondary README files to learn more about these option... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
bb791e49-7d81-4781-b054-4bec481ebbf2 | OPEA Documentation | file://datasets/opea-docs/developer-guides/primary_readme_genai_examples_template.md | unknown | 544a9d38-28c7-4bcc-8b3c-b8f7fa2b7255 | 3 | opea-semantic-v1 | 4559af3814383154 | name> application. See relevant secondary README files to learn more about these options. You can also find information about implementing this sample on specific hardware.
| Hardware | Deployment Mode | Description |
| ------------------------------------| ------------------------------ | -----------------------------... | ai_ref_knowledge | OPEA Documentation | name> application. See relevant secondary README files to learn more about these options. You can also find information about implementing this sample on specific hardware.
| Hardware | Deployment Mode | Description |
| ------------------------------------| ------------------------------ | -----------------------------... | name> application. See relevant secondary README files to learn more about these options. You can also find information about implementing this sample on specific hardware.
| Hardware | Deployment Mode | Description |
| ------------------------------------| ------------------------------ | -----------------------------... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ceab7af6-6336-4064-8493-45e7cfcc53d4 | OPEA Documentation | file://datasets/opea-docs/developer-guides/primary_readme_genai_examples_template.md | unknown | 544a9d38-28c7-4bcc-8b3c-b8f7fa2b7255 | 1 | opea-semantic-v1 | 5be0bb565d8362f7 | ## Overview <What does this sample do?> <Where can developers use this sample?> <What tasks or problems can this sample solve?>
## Architecture
<How does the sample work?>
<What is the flow of information? Include a flow diagram, if available.>
<What OPEA microservices does this sample use? Include an architecture d... | ai_ref_knowledge | OPEA Documentation | ## Overview <What does this sample do?> <Where can developers use this sample?> <What tasks or problems can this sample solve?>
## Architecture
<How does the sample work?>
<What is the flow of information? Include a flow diagram, if available.>
<What OPEA microservices does this sample use? Include an architecture d... | ## Overview <What does this sample do?> <Where can developers use this sample?> <What tasks or problems can this sample solve?>
## Architecture
<How does the sample work?>
<What is the flow of information? Include a flow diagram, if available.>
<What OPEA microservices does this sample use? Include an architecture d... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0cb32565-a5df-46dd-b80c-11ac6625aed8 | OPEA Documentation | file://datasets/opea-docs/developer-guides/secondary_readme_genai_examples_template.md | unknown | ca0fbeba-d63a-43cb-b651-8873c4101fe4 | 3 | opea-semantic-v1 | bf9d13946cafaab7 | ## Deployment <What are the prerequisites before you deploy the sample on the target hardware?> <What environment variables should be set before running Docker Compose?>
| Environment Variable | Description | Default Value |
| ------------------------------------| -------------------------------------------------------... | ai_ref_knowledge | OPEA Documentation | ## Deployment <What are the prerequisites before you deploy the sample on the target hardware?> <What environment variables should be set before running Docker Compose?>
| Environment Variable | Description | Default Value |
| ------------------------------------| -------------------------------------------------------... | ## Deployment <What are the prerequisites before you deploy the sample on the target hardware?> <What environment variables should be set before running Docker Compose?>
| Environment Variable | Description | Default Value |
| ------------------------------------| -------------------------------------------------------... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1cc30552-dd84-456f-aa05-9e09e6d5b9b4 | OPEA Documentation | file://datasets/opea-docs/developer-guides/secondary_readme_genai_examples_template.md | unknown | ca0fbeba-d63a-43cb-b651-8873c4101fe4 | 1 | opea-semantic-v1 | a9a94dfae220d52b | ## Contents - Overview - Deployment - Additional Options for Deployment - Validation - Profiling - Termination - Troubleshooting
## Overview
<What is the purpose of this README file?> | ai_ref_knowledge | OPEA Documentation | ## Contents - Overview - Deployment - Additional Options for Deployment - Validation - Profiling - Termination - Troubleshooting
## Overview
<What is the purpose of this README file?> | ## Contents - Overview - Deployment - Additional Options for Deployment - Validation - Profiling - Termination - Troubleshooting
## Overview
<What is the purpose of this README file?> | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
272c014c-e0c0-4421-97ef-0334ee0c896d | OPEA Documentation | file://datasets/opea-docs/developer-guides/secondary_readme_genai_examples_template.md | unknown | ca0fbeba-d63a-43cb-b651-8873c4101fe4 | 13 | opea-semantic-v1 | a84e150f9552c3be | ## Troubleshooting <Describe common problems encountered when deploying this specific use case. Include general troubleshooting information in the primary README.>
## Related Information
<Include links to:>
- <Relevant GenAI Examples>
- <Relevant microservices in GenAI Components>
- <Relevant OPEA tutorials> | ai_ref_knowledge | OPEA Documentation | ## Troubleshooting <Describe common problems encountered when deploying this specific use case. Include general troubleshooting information in the primary README.>
## Related Information
<Include links to:>
- <Relevant GenAI Examples>
- <Relevant microservices in GenAI Components>
- <Relevant OPEA tutorials> | ## Troubleshooting <Describe common problems encountered when deploying this specific use case. Include general troubleshooting information in the primary README.>
## Related Information
<Include links to:>
- <Relevant GenAI Examples>
- <Relevant microservices in GenAI Components>
- <Relevant OPEA tutorials> | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
31be5350-4fd5-47e9-934a-de505eeb7d7b | OPEA Documentation | file://datasets/opea-docs/developer-guides/secondary_readme_genai_examples_template.md | unknown | ca0fbeba-d63a-43cb-b651-8873c4101fe4 | 0 | opea-semantic-v1 | 1ac04b49e8cf5e08 | **Deploy <sample name> Application on <hardware>**
## Contents
- Overview
- Deployment
- Additional Options for Deployment
- Validation
- Profiling
- Termination
- Troubleshooting | ai_ref_knowledge | OPEA Documentation | **Deploy <sample name> Application on <hardware>**
## Contents
- Overview
- Deployment
- Additional Options for Deployment
- Validation
- Profiling
- Termination
- Troubleshooting | **Deploy <sample name> Application on <hardware>**
## Contents
- Overview
- Deployment
- Additional Options for Deployment
- Validation
- Profiling
- Termination
- Troubleshooting | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
46e4309c-5676-4066-b494-e5c76486af04 | OPEA Documentation | file://datasets/opea-docs/developer-guides/secondary_readme_genai_examples_template.md | unknown | ca0fbeba-d63a-43cb-b651-8873c4101fe4 | 10 | opea-semantic-v1 | 9705171fd459c39e | is a load balancer necessary?> - <Do specific instructions apply for different UIs?> - <What should the sample input and output look like? Include screenshots.>
## Profiling
<If supported, how do you profile the microservices that are used in this sample?>
<How do you prepare dashboards in Prometheus or Grafana for thi... | ai_ref_knowledge | OPEA Documentation | is a load balancer necessary?> - <Do specific instructions apply for different UIs?> - <What should the sample input and output look like? Include screenshots.>
## Profiling
<If supported, how do you profile the microservices that are used in this sample?>
<How do you prepare dashboards in Prometheus or Grafana for thi... | is a load balancer necessary?> - <Do specific instructions apply for different UIs?> - <What should the sample input and output look like? Include screenshots.>
## Profiling
<If supported, how do you profile the microservices that are used in this sample?>
<How do you prepare dashboards in Prometheus or Grafana for thi... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
58a875bf-30e6-460b-bf15-1b04e1c8f45a | OPEA Documentation | file://datasets/opea-docs/developer-guides/secondary_readme_genai_examples_template.md | unknown | ca0fbeba-d63a-43cb-b651-8873c4101fe4 | 12 | opea-semantic-v1 | e1ffcf3d1443e332 | ## Termination <How do you stop the microservices?>
## Troubleshooting
<Describe common problems encountered when deploying this specific use case. Include general troubleshooting information in the primary README.> | ai_ref_knowledge | OPEA Documentation | ## Termination <How do you stop the microservices?>
## Troubleshooting
<Describe common problems encountered when deploying this specific use case. Include general troubleshooting information in the primary README.> | ## Termination <How do you stop the microservices?>
## Troubleshooting
<Describe common problems encountered when deploying this specific use case. Include general troubleshooting information in the primary README.> | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5a01518f-45bb-45b0-9d24-9dab5e45ba5e | OPEA Documentation | file://datasets/opea-docs/developer-guides/secondary_readme_genai_examples_template.md | unknown | ca0fbeba-d63a-43cb-b651-8873c4101fe4 | 8 | opea-semantic-v1 | 9dc771be63eaf4dd | ## Validation <How do you validate the health of the microservices that are used in this sample?>
<For each microservice, your validation should display:>
- <The name of the microservice>
- <The test procedure used>
- <Applicable CURL commands>
- <An example of the expected output> | ai_ref_knowledge | OPEA Documentation | ## Validation <How do you validate the health of the microservices that are used in this sample?>
<For each microservice, your validation should display:>
- <The name of the microservice>
- <The test procedure used>
- <Applicable CURL commands>
- <An example of the expected output> | ## Validation <How do you validate the health of the microservices that are used in this sample?>
<For each microservice, your validation should display:>
- <The name of the microservice>
- <The test procedure used>
- <Applicable CURL commands>
- <An example of the expected output> | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation |
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