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6e0bc540-a623-4d74-aaa7-5c0e26c1e5c9 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 119 | opea-semantic-v1 | 128388f5293feea6 | Features evaluated for interoperability, platform capabilities, user experience (ease of use), AI methods being applied, and specialized functionality.
* Level 1 – Single model and accesses few data sources; Limited data ingest;
Basic or no development tools; basic UI; bare metal, manual install. * Level 2 - Multiple ... | ai_ref_knowledge | OPEA Documentation | Features evaluated for interoperability, platform capabilities, user experience (ease of use), AI methods being applied, and specialized functionality.
* Level 1 – Single model and accesses few data sources; Limited data ingest;
Basic or no development tools; basic UI; bare metal, manual install. * Level 2 - Multiple ... | Features evaluated for interoperability, platform capabilities, user experience (ease of use), AI methods being applied, and specialized functionality.
* Level 1 – Single model and accesses few data sources; Limited data ingest;
Basic or no development tools; basic UI; bare metal, manual install. * Level 2 - Multiple ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
700be18d-32c2-4219-bd43-e2d77ecbe6ba | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 130 | opea-semantic-v1 | 071dcb0c524ee628 | of evaluating the ability of the overall solution to be deployed in production in an enterprise environment. The following criteria will be taken into account:
* Ability to have on-prem and cloud deployments
* At least two types of solution instances (on-premise installation, cloud, hybrid option)
* Cloud/Edge-native... | ai_ref_knowledge | OPEA Documentation | of evaluating the ability of the overall solution to be deployed in production in an enterprise environment. The following criteria will be taken into account:
* Ability to have on-prem and cloud deployments
* At least two types of solution instances (on-premise installation, cloud, hybrid option)
* Cloud/Edge-native... | of evaluating the ability of the overall solution to be deployed in production in an enterprise environment. The following criteria will be taken into account:
* Ability to have on-prem and cloud deployments
* At least two types of solution instances (on-premise installation, cloud, hybrid option)
* Cloud/Edge-native... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
711bf585-ea35-4756-97bf-19d282018874 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 51 | opea-semantic-v1 | b70c97f23d841e61 | evaluation that aggregates multiple individual assessments into one of three levels, in each of the four evaluation domains – performance, features, Trustworthiness and Enterprise readiness.
The following draft of a grading system is for illustration and discussion purposes only. A grading
system should be defined and ... | ai_ref_knowledge | OPEA Documentation | evaluation that aggregates multiple individual assessments into one of three levels, in each of the four evaluation domains – performance, features, Trustworthiness and Enterprise readiness.
The following draft of a grading system is for illustration and discussion purposes only. A grading
system should be defined and ... | evaluation that aggregates multiple individual assessments into one of three levels, in each of the four evaluation domains – performance, features, Trustworthiness and Enterprise readiness.
The following draft of a grading system is for illustration and discussion purposes only. A grading
system should be defined and ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
724a6553-e9fb-4fdd-a771-d3ab85c4622f | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 156 | opea-semantic-v1 | a4c95a53dc6b36a9 | video frames relevant to a user’s query and providing them as extra context to a Large Vision-Language Model (LVLM), which then answers the user’s question.
Specifically, this reference solution takes images and video files as input. The inputs are encoded in a
joint multimodal embedding space by BridgeTower, which is ... | ai_ref_knowledge | OPEA Documentation | video frames relevant to a user’s query and providing them as extra context to a Large Vision-Language Model (LVLM), which then answers the user’s question.
Specifically, this reference solution takes images and video files as input. The inputs are encoded in a
joint multimodal embedding space by BridgeTower, which is ... | video frames relevant to a user’s query and providing them as extra context to a Large Vision-Language Model (LVLM), which then answers the user’s question.
Specifically, this reference solution takes images and video files as input. The inputs are encoded in a
joint multimodal embedding space by BridgeTower, which is ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
73f4784a-0ee7-4781-90b0-6555bb16213f | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 38 | opea-semantic-v1 | 0c603651af2f3b7c | It is also expected that there will be a regular cadence of updates to the spec to reflect the rapidly shifting State-of-the-Art in the space.
## 4. Assessing GenAI components and flows | ai_ref_knowledge | OPEA Documentation | It is also expected that there will be a regular cadence of updates to the spec to reflect the rapidly shifting State-of-the-Art in the space.
## 4. Assessing GenAI components and flows | It is also expected that there will be a regular cadence of updates to the spec to reflect the rapidly shifting State-of-the-Art in the space.
## 4. Assessing GenAI components and flows | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
749ec453-9003-4906-a897-355a908fa4bb | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 163 | opea-semantic-v1 | c6de40cacc531508 | Figure A6.2.2 Multimodal Chat Over Images and Videos – demo screen
#### A6.3 – Optimized Text and Multimodal RAG pipeline | ai_ref_knowledge | OPEA Documentation | Figure A6.2.2 Multimodal Chat Over Images and Videos – demo screen
#### A6.3 – Optimized Text and Multimodal RAG pipeline | Figure A6.2.2 Multimodal Chat Over Images and Videos – demo screen
#### A6.3 – Optimized Text and Multimodal RAG pipeline | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
75e670f0-eb5e-48e6-b474-e58e7ecfd166 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 20 | opea-semantic-v1 | 2e532075957d55d4 | could be part of OPEA repository, or published in stable open repository (e.g., Hugging Face) or offered by the ecosystem (like LangChain, LlamaIndex and Haystack).
An important part of the compositional offering will be a set of validated reference flows that are ready
for downloading and recreation in the users’ envi... | ai_ref_knowledge | OPEA Documentation | could be part of OPEA repository, or published in stable open repository (e.g., Hugging Face) or offered by the ecosystem (like LangChain, LlamaIndex and Haystack).
An important part of the compositional offering will be a set of validated reference flows that are ready
for downloading and recreation in the users’ envi... | could be part of OPEA repository, or published in stable open repository (e.g., Hugging Face) or offered by the ecosystem (like LangChain, LlamaIndex and Haystack).
An important part of the compositional offering will be a set of validated reference flows that are ready
for downloading and recreation in the users’ envi... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
781ff342-09b2-4d03-994d-73f2d78a1ff0 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 15 | opea-semantic-v1 | ef39751d70fabe9b | #### 2.1.1 Construction of GenAI solutions, including retrieval augmentation
Composing an end-to-end AI solution (including retrieval augmentation) can be done by combining
models and modules from multiple providers. | ai_ref_knowledge | OPEA Documentation | #### 2.1.1 Construction of GenAI solutions, including retrieval augmentation
Composing an end-to-end AI solution (including retrieval augmentation) can be done by combining
models and modules from multiple providers. | #### 2.1.1 Construction of GenAI solutions, including retrieval augmentation
Composing an end-to-end AI solution (including retrieval augmentation) can be done by combining
models and modules from multiple providers. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7900463b-d41e-4c6b-8baa-3e030953778e | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 149 | opea-semantic-v1 | 85eb80185bf96e88 | A complete reference implementation of this flow is available in the ChatQnA example in Intel’s GenAI examples repository.
 | ai_ref_knowledge | OPEA Documentation | A complete reference implementation of this flow is available in the ChatQnA example in Intel’s GenAI examples repository.
 | A complete reference implementation of this flow is available in the ChatQnA example in Intel’s GenAI examples repository.
 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7914e318-56dd-430d-9762-6878514eef07 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 6 | opea-semantic-v1 | 8765175b273bbe43 | it is important to note that this term might be replaced or updated based on more precise characterization and applying the Linux Foundation licensing considerations.
 | ai_ref_knowledge | OPEA Documentation | it is important to note that this term might be replaced or updated based on more precise characterization and applying the Linux Foundation licensing considerations.
 | it is important to note that this term might be replaced or updated based on more precise characterization and applying the Linux Foundation licensing considerations.
 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7dcb6bd6-6e85-4561-a1b5-e0a14e585897 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 32 | opea-semantic-v1 | 1b41e7941473d619 | OPEA definition (see Appendix A) includes characterization of components of State-of-the-Art (SotA) composite systems including retrieval-augmentation and their architecture as a flow and SW stack.
There are six sections in the Appendix A which will provide a starting point for a more detailed and
elaborate joint OPEA ... | ai_ref_knowledge | OPEA Documentation | OPEA definition (see Appendix A) includes characterization of components of State-of-the-Art (SotA) composite systems including retrieval-augmentation and their architecture as a flow and SW stack.
There are six sections in the Appendix A which will provide a starting point for a more detailed and
elaborate joint OPEA ... | OPEA definition (see Appendix A) includes characterization of components of State-of-the-Art (SotA) composite systems including retrieval-augmentation and their architecture as a flow and SW stack.
There are six sections in the Appendix A which will provide a starting point for a more detailed and
elaborate joint OPEA ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7eadf8c3-c456-41e9-87fb-08ec80db1290 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 33 | opea-semantic-v1 | 232175dee0f7c9d3 | There are six sections in the Appendix A which will provide a starting point for a more detailed and elaborate joint OPEA definition effort:
* A1: System Components - List of ingredients that comprise a composed system,
along with their key characteristics. Some systems that will be evaluated may
only include a subse... | ai_ref_knowledge | OPEA Documentation | There are six sections in the Appendix A which will provide a starting point for a more detailed and elaborate joint OPEA definition effort:
* A1: System Components - List of ingredients that comprise a composed system,
along with their key characteristics. Some systems that will be evaluated may
only include a subse... | There are six sections in the Appendix A which will provide a starting point for a more detailed and elaborate joint OPEA definition effort:
* A1: System Components - List of ingredients that comprise a composed system,
along with their key characteristics. Some systems that will be evaluated may
only include a subse... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7f15d05f-7c30-4aa5-ae9f-4486165c0cd8 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 8 | opea-semantic-v1 | 4fba7c62ed5ce90e | Figure 1-2 OPEA – proposed Construction and Evaluation Framework for AI Solutions
We are now in an era where AI algorithms and models, that were initially developed in research
environments and later introduced into consumer-focused settings, are now transitioning to widespread
enterprise deployment. This transition pr... | ai_ref_knowledge | OPEA Documentation | Figure 1-2 OPEA – proposed Construction and Evaluation Framework for AI Solutions
We are now in an era where AI algorithms and models, that were initially developed in research
environments and later introduced into consumer-focused settings, are now transitioning to widespread
enterprise deployment. This transition pr... | Figure 1-2 OPEA – proposed Construction and Evaluation Framework for AI Solutions
We are now in an era where AI algorithms and models, that were initially developed in research
environments and later introduced into consumer-focused settings, are now transitioning to widespread
enterprise deployment. This transition pr... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
80022582-f699-4a7f-8540-19d374de4a35 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 120 | opea-semantic-v1 | ff7182c1f35bc5f4 | agent controls. * Level 3 – Natively supports multimodal models and data source; Advanced development tools with SotA fine-tuning and optimizations capabilities; leading specialized features
#### A5.3 Trustworthiness Grading | ai_ref_knowledge | OPEA Documentation | agent controls. * Level 3 – Natively supports multimodal models and data source; Advanced development tools with SotA fine-tuning and optimizations capabilities; leading specialized features
#### A5.3 Trustworthiness Grading | agent controls. * Level 3 – Natively supports multimodal models and data source; Advanced development tools with SotA fine-tuning and optimizations capabilities; leading specialized features
#### A5.3 Trustworthiness Grading | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
806fd348-62d7-4241-bead-1bdcb3306fb5 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 145 | opea-semantic-v1 | 25a7d298dce0ccc3 | flow enables users to interact with LLMs and query about information that is unknown to the LLMs, or for example, consists of proprietary data sources.
The reference flow consists of the following detailed process: a data storage which is used by a
retrieving module to retrieve relevant information given a query from t... | ai_ref_knowledge | OPEA Documentation | flow enables users to interact with LLMs and query about information that is unknown to the LLMs, or for example, consists of proprietary data sources.
The reference flow consists of the following detailed process: a data storage which is used by a
retrieving module to retrieve relevant information given a query from t... | flow enables users to interact with LLMs and query about information that is unknown to the LLMs, or for example, consists of proprietary data sources.
The reference flow consists of the following detailed process: a data storage which is used by a
retrieving module to retrieve relevant information given a query from t... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
832704f0-6d73-4b29-a62b-81a45d5667ca | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 135 | opea-semantic-v1 | b9f3e744aba18189 | ### A6: Reference Flows
This section includes descriptions of reference flows that will be available for loading and reproducing
with minimal effort. | ai_ref_knowledge | OPEA Documentation | ### A6: Reference Flows
This section includes descriptions of reference flows that will be available for loading and reproducing
with minimal effort. | ### A6: Reference Flows
This section includes descriptions of reference flows that will be available for loading and reproducing
with minimal effort. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
833cee01-7f13-4b0b-b411-5c3adaac5d02 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 100 | opea-semantic-v1 | 427b9ac4eff126fe | #### A4.2 Individual Components Assessment
Evaluation of individual components (modules) will include:
* Data preprocessing pipeline
* Embedding – Quality/Storage/Processing time
* Chunker, Retriever & Re-ranker
* Generator LLM – quality/latency/context length/reasoning ability/function calling/tool usage
* Auto evalua... | ai_ref_knowledge | OPEA Documentation | #### A4.2 Individual Components Assessment
Evaluation of individual components (modules) will include:
* Data preprocessing pipeline
* Embedding – Quality/Storage/Processing time
* Chunker, Retriever & Re-ranker
* Generator LLM – quality/latency/context length/reasoning ability/function calling/tool usage
* Auto evalua... | #### A4.2 Individual Components Assessment
Evaluation of individual components (modules) will include:
* Data preprocessing pipeline
* Embedding – Quality/Storage/Processing time
* Chunker, Retriever & Re-ranker
* Generator LLM – quality/latency/context length/reasoning ability/function calling/tool usage
* Auto evalua... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8442365f-a002-4ac0-bd92-29ef0b3f329d | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 127 | opea-semantic-v1 | 11586c993b624122 | ##### Trustworthiness Grade
Evaluating transparency, privacy protection and security aspects
* Level 1 – Documentation of aspects called for in trustworthiness domain
* Level 2 - Supports role-based access controls - information being accessed/retrieved is
available based on approval for the user (even if all users ac... | ai_ref_knowledge | OPEA Documentation | ##### Trustworthiness Grade
Evaluating transparency, privacy protection and security aspects
* Level 1 – Documentation of aspects called for in trustworthiness domain
* Level 2 - Supports role-based access controls - information being accessed/retrieved is
available based on approval for the user (even if all users ac... | ##### Trustworthiness Grade
Evaluating transparency, privacy protection and security aspects
* Level 1 – Documentation of aspects called for in trustworthiness domain
* Level 2 - Supports role-based access controls - information being accessed/retrieved is
available based on approval for the user (even if all users ac... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8a61ffad-bde1-4721-af7c-0de08ca398f2 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 21 | opea-semantic-v1 | 6bbf2b5db5b860fa | for different HW providers and settings. There will also be domain-specific flows like financial service end-to-end flow or nutrition adviser, which are sometimes called microservices.
There is a common visualizing language that is used to depict the component of each reference flow
being provided. | ai_ref_knowledge | OPEA Documentation | for different HW providers and settings. There will also be domain-specific flows like financial service end-to-end flow or nutrition adviser, which are sometimes called microservices.
There is a common visualizing language that is used to depict the component of each reference flow
being provided. | for different HW providers and settings. There will also be domain-specific flows like financial service end-to-end flow or nutrition adviser, which are sometimes called microservices.
There is a common visualizing language that is used to depict the component of each reference flow
being provided. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8b8a9b03-e70d-41c5-926d-8906a1716e50 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 7 | opea-semantic-v1 | 0d607e7af4cb5091 | 
Figure 1-2 OPEA – proposed Construction and Evaluation Framework for AI Solutions | ai_ref_knowledge | OPEA Documentation | 
Figure 1-2 OPEA – proposed Construction and Evaluation Framework for AI Solutions | 
Figure 1-2 OPEA – proposed Construction and Evaluation Framework for AI Solutions | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8b986c90-347c-4f6a-aaeb-a7f74edbbddb | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 50 | opea-semantic-v1 | 7d8ba0686b449957 | ## 5. Grading Structure
OPEA evaluation structure refers to specific tests and benchmarks as ‘assessments’ – see previous
section for details. ‘Grading’ is the part of OPEA evaluation that aggregates multiple individual
assessments into one of three levels, in each of the four evaluation domains – performance, features... | ai_ref_knowledge | OPEA Documentation | ## 5. Grading Structure
OPEA evaluation structure refers to specific tests and benchmarks as ‘assessments’ – see previous
section for details. ‘Grading’ is the part of OPEA evaluation that aggregates multiple individual
assessments into one of three levels, in each of the four evaluation domains – performance, features... | ## 5. Grading Structure
OPEA evaluation structure refers to specific tests and benchmarks as ‘assessments’ – see previous
section for details. ‘Grading’ is the part of OPEA evaluation that aggregates multiple individual
assessments into one of three levels, in each of the four evaluation domains – performance, features... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8c276d8b-74cc-42cc-96c1-94125b16d3ed | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 151 | opea-semantic-v1 | 93a8b787e48f750b | Figure A6-1.2 Xeon + Gaudi2 LLM RAG flow for Chat QnA
A demo user Interface looks like below, which also shows the difference with and without RAG. | ai_ref_knowledge | OPEA Documentation | Figure A6-1.2 Xeon + Gaudi2 LLM RAG flow for Chat QnA
A demo user Interface looks like below, which also shows the difference with and without RAG. | Figure A6-1.2 Xeon + Gaudi2 LLM RAG flow for Chat QnA
A demo user Interface looks like below, which also shows the difference with and without RAG. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8d1e9e33-f937-4faa-9ea5-57f89c4a8d0e | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 14 | opea-semantic-v1 | ab5a0d854468432f | OPEA will provide the means to assess and grade end-to-end composite GenAI solutions on aspects derived from four domains – performance, features, trustworthiness and Enterprise-readiness.
#### 2.1.1 Construction of GenAI solutions, including retrieval augmentation | ai_ref_knowledge | OPEA Documentation | OPEA will provide the means to assess and grade end-to-end composite GenAI solutions on aspects derived from four domains – performance, features, trustworthiness and Enterprise-readiness.
#### 2.1.1 Construction of GenAI solutions, including retrieval augmentation | OPEA will provide the means to assess and grade end-to-end composite GenAI solutions on aspects derived from four domains – performance, features, trustworthiness and Enterprise-readiness.
#### 2.1.1 Construction of GenAI solutions, including retrieval augmentation | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8d4ce71d-b0c9-44b3-81e6-512d43b12a6b | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 165 | opea-semantic-v1 | 2a49784415d3a935 | The reference flow below demonstrates an optimized Text and Multimodal RAG pipeline which can be leveraged by Enterprise customers on Intel Xeon processor.
This flow demonstrates RAG inference flow on unstructured data and images with 4th and 5th Gen Intel
Xeon processor using Haystack. It is based on fastRAG for optim... | ai_ref_knowledge | OPEA Documentation | The reference flow below demonstrates an optimized Text and Multimodal RAG pipeline which can be leveraged by Enterprise customers on Intel Xeon processor.
This flow demonstrates RAG inference flow on unstructured data and images with 4th and 5th Gen Intel
Xeon processor using Haystack. It is based on fastRAG for optim... | The reference flow below demonstrates an optimized Text and Multimodal RAG pipeline which can be leveraged by Enterprise customers on Intel Xeon processor.
This flow demonstrates RAG inference flow on unstructured data and images with 4th and 5th Gen Intel
Xeon processor using Haystack. It is based on fastRAG for optim... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8dbb4fe3-4f93-4b4d-bc3a-91243b1f5feb | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 117 | opea-semantic-v1 | 324371adbafc3eaa | Deployment models * Orchestration * K8, hypervisor * Compliance * Potential certification (if and when it becomes part of the framework) based on functional testing
##### Features Grade | ai_ref_knowledge | OPEA Documentation | Deployment models * Orchestration * K8, hypervisor * Compliance * Potential certification (if and when it becomes part of the framework) based on functional testing
##### Features Grade | Deployment models * Orchestration * K8, hypervisor * Compliance * Potential certification (if and when it becomes part of the framework) based on functional testing
##### Features Grade | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8f179b7a-9827-4d2b-91be-f30cfeb82963 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 25 | opea-semantic-v1 | 6db7f4af9b750a3b | Some of the evaluation tools will be part of the OPEA repository, while others will be references to selected benchmarks offered by the ecosystem.
OPEA will offer tests for self-evaluation that can be done by the users. Furthermore, it will have the
engineering setup and staffing to provide evaluations per request. | ai_ref_knowledge | OPEA Documentation | Some of the evaluation tools will be part of the OPEA repository, while others will be references to selected benchmarks offered by the ecosystem.
OPEA will offer tests for self-evaluation that can be done by the users. Furthermore, it will have the
engineering setup and staffing to provide evaluations per request. | Some of the evaluation tools will be part of the OPEA repository, while others will be references to selected benchmarks offered by the ecosystem.
OPEA will offer tests for self-evaluation that can be done by the users. Furthermore, it will have the
engineering setup and staffing to provide evaluations per request. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
929ddad0-72e0-47c5-9a33-464f99ac5d29 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 42 | opea-semantic-v1 | b419add9cde0fa3b | perform defined tasks. The term ‘performance’ refers to aspects of speed (e.g., latency), capacity (e.g., memory or context size) as well as accuracy or results.
OPEA can utilize existing evaluation specs like those used by SotA RAG systems and other standard
benchmarks wherever possible (e.g., MMLU). As for functional... | ai_ref_knowledge | OPEA Documentation | perform defined tasks. The term ‘performance’ refers to aspects of speed (e.g., latency), capacity (e.g., memory or context size) as well as accuracy or results.
OPEA can utilize existing evaluation specs like those used by SotA RAG systems and other standard
benchmarks wherever possible (e.g., MMLU). As for functional... | perform defined tasks. The term ‘performance’ refers to aspects of speed (e.g., latency), capacity (e.g., memory or context size) as well as accuracy or results.
OPEA can utilize existing evaluation specs like those used by SotA RAG systems and other standard
benchmarks wherever possible (e.g., MMLU). As for functional... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
93f6e57e-c366-4797-8fbe-48457b44b23c | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 162 | opea-semantic-v1 | f97af4277df6f511 | 
Figure A6.2.2 Multimodal Chat Over Images and Videos – demo screen | ai_ref_knowledge | OPEA Documentation | 
Figure A6.2.2 Multimodal Chat Over Images and Videos – demo screen | 
Figure A6.2.2 Multimodal Chat Over Images and Videos – demo screen | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
946eb124-3060-484b-83b5-2a105bfad00c | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 87 | opea-semantic-v1 | 31c1285e325bda2a | Tuning of the solutions leverage platform optimizations via popular domain frameworks such as Hugging Face ecosystem to reduce developer complexity and provide flexibility across platforms.
 | ai_ref_knowledge | OPEA Documentation | components of the reference flow can be executed on CPU. A complete end-to-end open-source implementation of this reference flow is available via Multimodal Cognitive AI.
 | components of the reference flow can be executed on CPU. A complete end-to-end open-source implementation of this reference flow is available via Multimodal Cognitive AI.
 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ae7647c1-071b-49b2-b62d-62fe15c6dd49 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 170 | opea-semantic-v1 | c5feae5db120ed51 | Figure A6-3.1 Optimized Text and Multimodal RAG pipeline Reference Flow
Below is a visual snapshot of the chat implemented using this flow. It shows how a RAG-enabled chatbot
in Figure A6-3.2 improves the response for a Superbowl query over a non-RAG implementation in Figure
A6-3.3. | ai_ref_knowledge | OPEA Documentation | Figure A6-3.1 Optimized Text and Multimodal RAG pipeline Reference Flow
Below is a visual snapshot of the chat implemented using this flow. It shows how a RAG-enabled chatbot
in Figure A6-3.2 improves the response for a Superbowl query over a non-RAG implementation in Figure
A6-3.3. | Figure A6-3.1 Optimized Text and Multimodal RAG pipeline Reference Flow
Below is a visual snapshot of the chat implemented using this flow. It shows how a RAG-enabled chatbot
in Figure A6-3.2 improves the response for a Superbowl query over a non-RAG implementation in Figure
A6-3.3. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b18abe48-3232-485d-a169-7cda22855299 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 154 | opea-semantic-v1 | 83633df7a9af8e3e | Figure A6-1.3 Xeon + Gaudi2 LLM RAG flow for Chat QnA – demo screen
#### A6.2 - Multimodal Chat Over Images and Videos | ai_ref_knowledge | OPEA Documentation | Figure A6-1.3 Xeon + Gaudi2 LLM RAG flow for Chat QnA – demo screen
#### A6.2 - Multimodal Chat Over Images and Videos | Figure A6-1.3 Xeon + Gaudi2 LLM RAG flow for Chat QnA – demo screen
#### A6.2 - Multimodal Chat Over Images and Videos | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b2606632-5a5e-4305-add2-a93a0117a625 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 22 | opea-semantic-v1 | b7b97d7cdd594d9d | There is a common visualizing language that is used to depict the component of each reference flow being provided.
#### 2.1.2 Evaluation of GenAI solutions, including retrieval augmentation: | ai_ref_knowledge | OPEA Documentation | There is a common visualizing language that is used to depict the component of each reference flow being provided.
#### 2.1.2 Evaluation of GenAI solutions, including retrieval augmentation: | There is a common visualizing language that is used to depict the component of each reference flow being provided.
#### 2.1.2 Evaluation of GenAI solutions, including retrieval augmentation: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b32439a5-d5a3-4785-abb6-6f7e0c709278 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 103 | opea-semantic-v1 | 47a57af51779378d | Metric: Normalized Discounted Cumulative Gain@10 with BEIR benchmark datasets or other QA datasets * Metric: Context Recall@k * Metric: Context Precision@k * Metric: Hit Rate
Component Name: LLM/Generation
* Metric: Faithfulness – How factually correct is the generated answer (computed as a ragas metrics between 0 and ... | ai_ref_knowledge | OPEA Documentation | Metric: Normalized Discounted Cumulative Gain@10 with BEIR benchmark datasets or other QA datasets * Metric: Context Recall@k * Metric: Context Precision@k * Metric: Hit Rate
Component Name: LLM/Generation
* Metric: Faithfulness – How factually correct is the generated answer (computed as a ragas metrics between 0 and ... | Metric: Normalized Discounted Cumulative Gain@10 with BEIR benchmark datasets or other QA datasets * Metric: Context Recall@k * Metric: Context Precision@k * Metric: Hit Rate
Component Name: LLM/Generation
* Metric: Faithfulness – How factually correct is the generated answer (computed as a ragas metrics between 0 and ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b473211a-f6db-499e-a213-d3a3acd8aa73 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 97 | opea-semantic-v1 | 1c1dda346fe6b484 | serving * Integrations with different enterprise systems such as Slack/workday/SAP/Databases * Enterprise grade RAS capabilities * Service Level Agreements (SLAs) on factuality, verifiability, performance enforceability
Updatability includes capability for
* Rolling upgrade
* Online upgrade
* Component level upgrade | ai_ref_knowledge | OPEA Documentation | serving * Integrations with different enterprise systems such as Slack/workday/SAP/Databases * Enterprise grade RAS capabilities * Service Level Agreements (SLAs) on factuality, verifiability, performance enforceability
Updatability includes capability for
* Rolling upgrade
* Online upgrade
* Component level upgrade | serving * Integrations with different enterprise systems such as Slack/workday/SAP/Databases * Enterprise grade RAS capabilities * Service Level Agreements (SLAs) on factuality, verifiability, performance enforceability
Updatability includes capability for
* Rolling upgrade
* Online upgrade
* Component level upgrade | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b60d9f81-4483-4bad-9c91-62e564763452 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 169 | opea-semantic-v1 | 9162c0e15effd81b | The below diagram shows the end-to-end flow for this optimized text and multimodal chat with RAG.
 | ai_ref_knowledge | OPEA Documentation | The below diagram shows the end-to-end flow for this optimized text and multimodal chat with RAG.
 | The below diagram shows the end-to-end flow for this optimized text and multimodal chat with RAG.
 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b659343b-6914-4ee0-8261-a7da2f56267f | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 1 | opea-semantic-v1 | 7957c6a164e6bf57 | ## 1. Summary
OPEA (Open Platform for Enterprise AI) is a framework that enables the creation and evaluation of
open, multi-provider, robust and composable GenAI solutions that harness the best innovation across
the ecosystem. | ai_ref_knowledge | OPEA Documentation | ## 1. Summary
OPEA (Open Platform for Enterprise AI) is a framework that enables the creation and evaluation of
open, multi-provider, robust and composable GenAI solutions that harness the best innovation across
the ecosystem. | ## 1. Summary
OPEA (Open Platform for Enterprise AI) is a framework that enables the creation and evaluation of
open, multi-provider, robust and composable GenAI solutions that harness the best innovation across
the ecosystem. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b68aa20b-e94e-4492-8aa3-586d75c8ccb3 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 80 | opea-semantic-v1 | 53bb359b1bfd046e | it is important to note that this term might be replaced or updated based on more precise characterization and applying the Linux Foundation licensing considerations.
### A1: System Components | ai_ref_knowledge | OPEA Documentation | it is important to note that this term might be replaced or updated based on more precise characterization and applying the Linux Foundation licensing considerations.
### A1: System Components | it is important to note that this term might be replaced or updated based on more precise characterization and applying the Linux Foundation licensing considerations.
### A1: System Components | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b7be6fae-6eb7-4930-9d40-6b45cdac5839 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 37 | opea-semantic-v1 | 55c52a8091e305b8 | experiment with solution variations - e.g. What is the impact (E2E system performance) when replacing a generic re-ranking component with a particular provider’s re-ranking component.
It should be noted that the final shaping of the framework components, architecture and flows will be
jointly defined by a technical com... | ai_ref_knowledge | OPEA Documentation | experiment with solution variations - e.g. What is the impact (E2E system performance) when replacing a generic re-ranking component with a particular provider’s re-ranking component.
It should be noted that the final shaping of the framework components, architecture and flows will be
jointly defined by a technical com... | experiment with solution variations - e.g. What is the impact (E2E system performance) when replacing a generic re-ranking component with a particular provider’s re-ranking component.
It should be noted that the final shaping of the framework components, architecture and flows will be
jointly defined by a technical com... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
bae6edeb-f145-468a-a9e7-60e340f80019 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 95 | opea-semantic-v1 | 6f707c78af1d32ca | 1. Scalability 2. Production deployability 3. Updatability 4. Observability/Debuggability
Scalability is associated with the ability of RAG system to scale the size/dimensions of different components such as the following example metrics:
* Vector DB size
* Dimensionality of retriever (the value of K in top-K documents... | ai_ref_knowledge | OPEA Documentation | 1. Scalability 2. Production deployability 3. Updatability 4. Observability/Debuggability
Scalability is associated with the ability of RAG system to scale the size/dimensions of different components such as the following example metrics:
* Vector DB size
* Dimensionality of retriever (the value of K in top-K documents... | 1. Scalability 2. Production deployability 3. Updatability 4. Observability/Debuggability
Scalability is associated with the ability of RAG system to scale the size/dimensions of different components such as the following example metrics:
* Vector DB size
* Dimensionality of retriever (the value of K in top-K documents... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
bbc15be1-25e7-4005-b739-81775f1a4af1 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 126 | opea-semantic-v1 | 1e227264ddb62610 | divergence of the test subject from the training dataset is an indicator of applicability risk, confidence in the response (alluded to in data transparency above).
##### Trustworthiness Grade | ai_ref_knowledge | OPEA Documentation | divergence of the test subject from the training dataset is an indicator of applicability risk, confidence in the response (alluded to in data transparency above).
##### Trustworthiness Grade | divergence of the test subject from the training dataset is an indicator of applicability risk, confidence in the response (alluded to in data transparency above).
##### Trustworthiness Grade | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
bcb56d00-b147-436b-9686-ba1662b74a7d | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 111 | opea-semantic-v1 | 6ea9489b7eec9777 | Performance grading is based on running a set of vertical-specific end-to-end use cases on full system and capturing the relevant metrics during the run.
* E2E/System View
* Vendors have flexibility to innovate/differentiate their implementations within the black box
* Running a fixed set of use cases
* Covering diff... | ai_ref_knowledge | OPEA Documentation | Performance grading is based on running a set of vertical-specific end-to-end use cases on full system and capturing the relevant metrics during the run.
* E2E/System View
* Vendors have flexibility to innovate/differentiate their implementations within the black box
* Running a fixed set of use cases
* Covering diff... | Performance grading is based on running a set of vertical-specific end-to-end use cases on full system and capturing the relevant metrics during the run.
* E2E/System View
* Vendors have flexibility to innovate/differentiate their implementations within the black box
* Running a fixed set of use cases
* Covering diff... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
bcc2abd1-341b-4076-986f-2e92ebbef5c2 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 131 | opea-semantic-v1 | 39e41537a92908bc | as root or have more capabilities than necessary. OWASP container best practices. * Ensure by-products/interim results if saved to disk are done so after encrypting.
* Quality assurance
* Accuracy & Uncertainty Metrics for domain-specific enterprise tasks
* Documentation
* High availability
* Replication & Data/Inst... | ai_ref_knowledge | OPEA Documentation | as root or have more capabilities than necessary. OWASP container best practices. * Ensure by-products/interim results if saved to disk are done so after encrypting.
* Quality assurance
* Accuracy & Uncertainty Metrics for domain-specific enterprise tasks
* Documentation
* High availability
* Replication & Data/Inst... | as root or have more capabilities than necessary. OWASP container best practices. * Ensure by-products/interim results if saved to disk are done so after encrypting.
* Quality assurance
* Accuracy & Uncertainty Metrics for domain-specific enterprise tasks
* Documentation
* High availability
* Replication & Data/Inst... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c115b31b-0ccd-472c-8fd9-18f1041266b9 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 166 | opea-semantic-v1 | 3d4115a3b9c27f59 | inference flow on unstructured data and images with 4th and 5th Gen Intel Xeon processor using Haystack. It is based on fastRAG for optimized retrieval.
The first step is to create index for the vector database (i.e. Qdrant in this case). For unstructured text
data, sentence-transformers is used. For images, BridgeTowe... | ai_ref_knowledge | OPEA Documentation | inference flow on unstructured data and images with 4th and 5th Gen Intel Xeon processor using Haystack. It is based on fastRAG for optimized retrieval.
The first step is to create index for the vector database (i.e. Qdrant in this case). For unstructured text
data, sentence-transformers is used. For images, BridgeTowe... | inference flow on unstructured data and images with 4th and 5th Gen Intel Xeon processor using Haystack. It is based on fastRAG for optimized retrieval.
The first step is to create index for the vector database (i.e. Qdrant in this case). For unstructured text
data, sentence-transformers is used. For images, BridgeTowe... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c11e7c77-0ce6-4929-830c-e70d5a5a74ab | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 134 | opea-semantic-v1 | ddbe975b66e29c14 | and instrumentation for the enterprise deployment environment. High resiliency – meeting fast time to relaunch an instance. Allows for L2 + 24/7 support mode out-of-the-box
### A6: Reference Flows | ai_ref_knowledge | OPEA Documentation | and instrumentation for the enterprise deployment environment. High resiliency – meeting fast time to relaunch an instance. Allows for L2 + 24/7 support mode out-of-the-box
### A6: Reference Flows | and instrumentation for the enterprise deployment environment. High resiliency – meeting fast time to relaunch an instance. Allows for L2 + 24/7 support mode out-of-the-box
### A6: Reference Flows | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c2271974-43ac-456d-9d0f-574579144e9f | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 18 | opea-semantic-v1 | 2f5189b29a9f6ede | system components (other than LLM/LVM models) including Ingest/Data Processing module, Embedding Models/Services, Vector Databases (aka Indexing or Graph data stores), Prompt Engines, Memory systems, etc.
Each module for the system will be characterized with its expected functionality and attributes. Those
will be eval... | ai_ref_knowledge | OPEA Documentation | system components (other than LLM/LVM models) including Ingest/Data Processing module, Embedding Models/Services, Vector Databases (aka Indexing or Graph data stores), Prompt Engines, Memory systems, etc.
Each module for the system will be characterized with its expected functionality and attributes. Those
will be eval... | system components (other than LLM/LVM models) including Ingest/Data Processing module, Embedding Models/Services, Vector Databases (aka Indexing or Graph data stores), Prompt Engines, Memory systems, etc.
Each module for the system will be characterized with its expected functionality and attributes. Those
will be eval... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c4420ccb-f1e7-43fc-8d38-09e0f97513a4 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 105 | opea-semantic-v1 | e320ea99be6d1459 | ### A5: Grading
To ensure that compositional systems are addressing the range of care-abouts for enterprise
deployment, the grading system has four categories: | ai_ref_knowledge | OPEA Documentation | ### A5: Grading
To ensure that compositional systems are addressing the range of care-abouts for enterprise
deployment, the grading system has four categories: | ### A5: Grading
To ensure that compositional systems are addressing the range of care-abouts for enterprise
deployment, the grading system has four categories: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c7748ca4-97d7-41d0-8f30-8f00ccf5b03b | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 57 | opea-semantic-v1 | a3ce6a43869cc777 | A grading system establishes a mechanism to evaluate different constructed AI solutions (such as particular RAG flows) in the context of the OPEA framework.
For each category, the assessments will be set with 3 levels: | ai_ref_knowledge | OPEA Documentation | A grading system establishes a mechanism to evaluate different constructed AI solutions (such as particular RAG flows) in the context of the OPEA framework.
For each category, the assessments will be set with 3 levels: | A grading system establishes a mechanism to evaluate different constructed AI solutions (such as particular RAG flows) in the context of the OPEA framework.
For each category, the assessments will be set with 3 levels: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c89ad2fd-7965-4dd0-8224-dfbf289576c5 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 34 | opea-semantic-v1 | 928dd9d8c8d1c65f | List of reference flows that demonstrate key use-cases and allow for downloading and replication for a faster path to create an instantiation of the flow.
Assumptions for the development of OPEA sections include: | ai_ref_knowledge | OPEA Documentation | List of reference flows that demonstrate key use-cases and allow for downloading and replication for a faster path to create an instantiation of the flow.
Assumptions for the development of OPEA sections include: | List of reference flows that demonstrate key use-cases and allow for downloading and replication for a faster path to create an instantiation of the flow.
Assumptions for the development of OPEA sections include: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c9c2d215-53fc-4a8f-93a4-7353ec4713c6 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 61 | opea-semantic-v1 | d84bb679f11d99eb | 
Figure 5-1 Overall view of the grading system across four domains | ai_ref_knowledge | OPEA Documentation | 
Figure 5-1 Overall view of the grading system across four domains | 
Figure 5-1 Overall view of the grading system across four domains | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
cb853b3b-e554-4580-a89f-2f7127909b0b | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 99 | opea-semantic-v1 | 29214ea71347b52b | * Early detection of component degradation * Trace generation to debug failures (functional and performance) * Traceability of each intermediate step (prompts for chained LLMs)
Examples for observability include Databricks Inference Tables/Phoenix Open Inference Traces or
Langsmith Observability/monitoring features. | ai_ref_knowledge | OPEA Documentation | * Early detection of component degradation * Trace generation to debug failures (functional and performance) * Traceability of each intermediate step (prompts for chained LLMs)
Examples for observability include Databricks Inference Tables/Phoenix Open Inference Traces or
Langsmith Observability/monitoring features. | * Early detection of component degradation * Trace generation to debug failures (functional and performance) * Traceability of each intermediate step (prompts for chained LLMs)
Examples for observability include Databricks Inference Tables/Phoenix Open Inference Traces or
Langsmith Observability/monitoring features. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
cb95176b-6e2f-4dc3-bc4a-1380786f25e3 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 86 | opea-semantic-v1 | abb71172cdc9d8b8 | integration across popular user-facing frameworks. It leverages popular agent frameworks (aka orchestration frameworks or AI Construction Platforms) for developer productivity and availability of platform optimization.
Tuning of the solutions leverage platform optimizations via popular domain frameworks such as Hugging... | ai_ref_knowledge | OPEA Documentation | integration across popular user-facing frameworks. It leverages popular agent frameworks (aka orchestration frameworks or AI Construction Platforms) for developer productivity and availability of platform optimization.
Tuning of the solutions leverage platform optimizations via popular domain frameworks such as Hugging... | integration across popular user-facing frameworks. It leverages popular agent frameworks (aka orchestration frameworks or AI Construction Platforms) for developer productivity and availability of platform optimization.
Tuning of the solutions leverage platform optimizations via popular domain frameworks such as Hugging... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
cc489a13-3b04-4b5f-9474-e02315a7ea9a | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 28 | opea-semantic-v1 | cd6ee78ed9027fbe | consideration is to allow for OPEA Certification that will be determined by ensuring a minimum of Level 2 grading is achieved on all four domains.
 | ai_ref_knowledge | OPEA Documentation | consideration is to allow for OPEA Certification that will be determined by ensuring a minimum of Level 2 grading is achieved on all four domains.
 | consideration is to allow for OPEA Certification that will be determined by ensuring a minimum of Level 2 grading is achieved on all four domains.
 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
cf9ca10a-4bdc-471e-80f5-0f8ec7d84329 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 53 | opea-semantic-v1 | 273e54d3d3888ff3 | To ensure that compositional systems are addressing the range of care-abouts for enterprise deployment, the grading system has four categories:
* Performance – Focused on overall system performance and perf/TCO
* Features- Mandatory and optional capabilities of system components
* Trustworthiness – Ability to guarantee... | ai_ref_knowledge | OPEA Documentation | To ensure that compositional systems are addressing the range of care-abouts for enterprise deployment, the grading system has four categories:
* Performance – Focused on overall system performance and perf/TCO
* Features- Mandatory and optional capabilities of system components
* Trustworthiness – Ability to guarantee... | To ensure that compositional systems are addressing the range of care-abouts for enterprise deployment, the grading system has four categories:
* Performance – Focused on overall system performance and perf/TCO
* Features- Mandatory and optional capabilities of system components
* Trustworthiness – Ability to guarantee... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
cff5d395-6b2d-4eb1-a0d7-48739517cc37 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 152 | opea-semantic-v1 | 7443110d46bc1e5d | A demo user Interface looks like below, which also shows the difference with and without RAG.
 | ai_ref_knowledge | OPEA Documentation | A demo user Interface looks like below, which also shows the difference with and without RAG.
 | A demo user Interface looks like below, which also shows the difference with and without RAG.
 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d093bd4d-1ddb-4c6d-92fe-e4e930f2fa57 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 58 | opea-semantic-v1 | 3728a9eb7a3a3cfe | For each category, the assessments will be set with 3 levels:
* L1 – Entry Level – Limited capabilities. The solution might be seen as less
advanced or performant relative to other solutions assessed for similar tasks. It might encounter issues in deployment (if deficiencies in trustworthiness or
enterprise readiness... | ai_ref_knowledge | OPEA Documentation | For each category, the assessments will be set with 3 levels:
* L1 – Entry Level – Limited capabilities. The solution might be seen as less
advanced or performant relative to other solutions assessed for similar tasks. It might encounter issues in deployment (if deficiencies in trustworthiness or
enterprise readiness... | For each category, the assessments will be set with 3 levels:
* L1 – Entry Level – Limited capabilities. The solution might be seen as less
advanced or performant relative to other solutions assessed for similar tasks. It might encounter issues in deployment (if deficiencies in trustworthiness or
enterprise readiness... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d2dcd4ba-57bf-4b35-8316-0fc179e81d38 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 41 | opea-semantic-v1 | b933d3ccb82d4d7d | Components and entire end-to-end flows will be evaluated in four domains – performance, features, trustworthiness and enterprise-readiness.
Performance can be evaluated at the component level - e.g., Vector Database latency over a given large,
indexed dataset, or latency and throughput of an LLM model. Moreover, perfor... | ai_ref_knowledge | OPEA Documentation | Components and entire end-to-end flows will be evaluated in four domains – performance, features, trustworthiness and enterprise-readiness.
Performance can be evaluated at the component level - e.g., Vector Database latency over a given large,
indexed dataset, or latency and throughput of an LLM model. Moreover, perfor... | Components and entire end-to-end flows will be evaluated in four domains – performance, features, trustworthiness and enterprise-readiness.
Performance can be evaluated at the component level - e.g., Vector Database latency over a given large,
indexed dataset, or latency and throughput of an LLM model. Moreover, perfor... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d4cd9488-beea-411f-81d3-8b642fc02c6e | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 168 | opea-semantic-v1 | e5f4de98054173ac | is set up, next step is to deploy inference chat. The LLM and LMM models used for inference are Llama-2-7b-chat-hf, Llama-2-13b-chat-hf and LLaVa models respectively.
The below diagram shows the end-to-end flow for this optimized text and multimodal chat with RAG. | ai_ref_knowledge | OPEA Documentation | is set up, next step is to deploy inference chat. The LLM and LMM models used for inference are Llama-2-7b-chat-hf, Llama-2-13b-chat-hf and LLaVa models respectively.
The below diagram shows the end-to-end flow for this optimized text and multimodal chat with RAG. | is set up, next step is to deploy inference chat. The LLM and LMM models used for inference are Llama-2-7b-chat-hf, Llama-2-13b-chat-hf and LLaVa models respectively.
The below diagram shows the end-to-end flow for this optimized text and multimodal chat with RAG. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d6b98f23-3aab-4558-81f8-59082bed5d6c | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 81 | opea-semantic-v1 | 6832e3eef9658d11 | ### A1: System Components
| Components | Description | OSS Examples | Proprietary Examples |
| ---------- | ----------- | ------------ | -------------------- |
| Agent framework | Orchestration software for building and deploying workflows combining information retrieval components with LLMs for building AI agents with... | ai_ref_knowledge | OPEA Documentation | ### A1: System Components
| Components | Description | OSS Examples | Proprietary Examples |
| ---------- | ----------- | ------------ | -------------------- |
| Agent framework | Orchestration software for building and deploying workflows combining information retrieval components with LLMs for building AI agents with... | ### A1: System Components
| Components | Description | OSS Examples | Proprietary Examples |
| ---------- | ----------- | ------------ | -------------------- |
| Agent framework | Orchestration software for building and deploying workflows combining information retrieval components with LLMs for building AI agents with... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d8f65dc0-9eaf-4e1d-a4c8-c793946f3526 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 12 | opea-semantic-v1 | 89c0d58fb26303ef | hardware/software providers (e.g., NVIDIA). However, as of Q2 2024 these represent individual perspectives and offerings for the intricate task of building an end-to-end AI solution.
### 2.1 Key capabilities | ai_ref_knowledge | OPEA Documentation | hardware/software providers (e.g., NVIDIA). However, as of Q2 2024 these represent individual perspectives and offerings for the intricate task of building an end-to-end AI solution.
### 2.1 Key capabilities | hardware/software providers (e.g., NVIDIA). However, as of Q2 2024 these represent individual perspectives and offerings for the intricate task of building an end-to-end AI solution.
### 2.1 Key capabilities | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d8f91933-ba52-45c9-a613-951b4ad118c9 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 44 | opea-semantic-v1 | 46eb43bd6f1a289b | For evaluating trustworthiness/Hallucination safety the spec will leverage existing benchmarks such as RGB benchmark/Truthful QA where possible.
Some assessment of enterprise readiness would include aspects of scalability (how large of data set the
system can handle, size of vector store, size and type of models), infr... | ai_ref_knowledge | OPEA Documentation | For evaluating trustworthiness/Hallucination safety the spec will leverage existing benchmarks such as RGB benchmark/Truthful QA where possible.
Some assessment of enterprise readiness would include aspects of scalability (how large of data set the
system can handle, size of vector store, size and type of models), infr... | For evaluating trustworthiness/Hallucination safety the spec will leverage existing benchmarks such as RGB benchmark/Truthful QA where possible.
Some assessment of enterprise readiness would include aspects of scalability (how large of data set the
system can handle, size of vector store, size and type of models), infr... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d96c54c8-58fc-425c-a9d6-3e83b30c531f | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 146 | opea-semantic-v1 | 94b5d9377ee39431 | the query to create an enhanced prompt to the LLM. An LLM receives the enhanced prompt generates a grounded and correct response to the user.
The flow contains the following components:
* A data ingest flow that uses an embedding model serving platform (TEI) and an
embedding model (BGE-base) for encoding text and quer... | ai_ref_knowledge | OPEA Documentation | the query to create an enhanced prompt to the LLM. An LLM receives the enhanced prompt generates a grounded and correct response to the user.
The flow contains the following components:
* A data ingest flow that uses an embedding model serving platform (TEI) and an
embedding model (BGE-base) for encoding text and quer... | the query to create an enhanced prompt to the LLM. An LLM receives the enhanced prompt generates a grounded and correct response to the user.
The flow contains the following components:
* A data ingest flow that uses an embedding model serving platform (TEI) and an
embedding model (BGE-base) for encoding text and quer... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d999d79c-9c76-4f33-8820-3cad235a79ba | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 63 | opea-semantic-v1 | 88baa097e71c0093 | delivery and continuous improvement for broad enterprise deployment. It also serves to highlight outstanding solutions, providing them tailwinds as the present and differentiate their offering.
If and when certification becomes part of the framework (discussion and decisions to be made at a later
stage) it is assumed t... | ai_ref_knowledge | OPEA Documentation | delivery and continuous improvement for broad enterprise deployment. It also serves to highlight outstanding solutions, providing them tailwinds as the present and differentiate their offering.
If and when certification becomes part of the framework (discussion and decisions to be made at a later
stage) it is assumed t... | delivery and continuous improvement for broad enterprise deployment. It also serves to highlight outstanding solutions, providing them tailwinds as the present and differentiate their offering.
If and when certification becomes part of the framework (discussion and decisions to be made at a later
stage) it is assumed t... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
dc15b3e4-bd2d-464e-b5a8-828b1fb6e6d3 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 26 | opea-semantic-v1 | 05c205960f9f66e1 | offer tests for self-evaluation that can be done by the users. Furthermore, it will have the engineering setup and staffing to provide evaluations per request.
The OPEA evaluations can be viewed at the following levels: | ai_ref_knowledge | OPEA Documentation | offer tests for self-evaluation that can be done by the users. Furthermore, it will have the engineering setup and staffing to provide evaluations per request.
The OPEA evaluations can be viewed at the following levels: | offer tests for self-evaluation that can be done by the users. Furthermore, it will have the engineering setup and staffing to provide evaluations per request.
The OPEA evaluations can be viewed at the following levels: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
dd4b618c-9c13-46a4-8d70-ea8a5f83f11f | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 107 | opea-semantic-v1 | de24125385b8392e | system components * Trustworthiness – Ability to guarantee quality, security, and robustness. * Enterprise Ready – Ability to be used in production in enterprise environments.
For each category, the assessments will be set with 3 levels
* L1 – Entry Level – Limited capabilities. Solution acceptable for PoC, but not pro... | ai_ref_knowledge | OPEA Documentation | system components * Trustworthiness – Ability to guarantee quality, security, and robustness. * Enterprise Ready – Ability to be used in production in enterprise environments.
For each category, the assessments will be set with 3 levels
* L1 – Entry Level – Limited capabilities. Solution acceptable for PoC, but not pro... | system components * Trustworthiness – Ability to guarantee quality, security, and robustness. * Enterprise Ready – Ability to be used in production in enterprise environments.
For each category, the assessments will be set with 3 levels
* L1 – Entry Level – Limited capabilities. Solution acceptable for PoC, but not pro... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
de12d5c2-89a6-4821-a706-dbc862168287 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 125 | opea-semantic-v1 | 87a56e6aef23a36e | that information along with the response helps an end user in determining how confident they can be with a response. * Cites sources for responses.
Meta data can also be used to indicate how up-to-date the input information is. * With respect to diagnosis/classification tasks, such as cancer detection, the divergence o... | ai_ref_knowledge | OPEA Documentation | that information along with the response helps an end user in determining how confident they can be with a response. * Cites sources for responses.
Meta data can also be used to indicate how up-to-date the input information is. * With respect to diagnosis/classification tasks, such as cancer detection, the divergence o... | that information along with the response helps an end user in determining how confident they can be with a response. * Cites sources for responses.
Meta data can also be used to indicate how up-to-date the input information is. * With respect to diagnosis/classification tasks, such as cancer detection, the divergence o... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e457e300-1fa2-4540-953e-53e345c04a56 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 121 | opea-semantic-v1 | af5a5c6baf8fba7b | #### A5.3 Trustworthiness Grading
Trustworthiness and responsible AI are evolving in an operational sense. See NIST trustworthy and
responsible AI and the EU AI Act. While these efforts are evolving, for the interim, we propose grading
solution trustworthiness along the axes of security, reliability, transparency, and ... | ai_ref_knowledge | OPEA Documentation | #### A5.3 Trustworthiness Grading
Trustworthiness and responsible AI are evolving in an operational sense. See NIST trustworthy and
responsible AI and the EU AI Act. While these efforts are evolving, for the interim, we propose grading
solution trustworthiness along the axes of security, reliability, transparency, and ... | #### A5.3 Trustworthiness Grading
Trustworthiness and responsible AI are evolving in an operational sense. See NIST trustworthy and
responsible AI and the EU AI Act. While these efforts are evolving, for the interim, we propose grading
solution trustworthiness along the axes of security, reliability, transparency, and ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e4f83b6c-bdcc-4945-b1aa-02650cda1db7 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 92 | opea-semantic-v1 | 9226b02b9d628dad | best system performance. * Q&A evaluation (accuracy) * Task: Open Q&A * Databases: NQ, TriviaQA and HotpotQA * Metric: Average Accuracy * Indexing: KILT Wikipedia
##### Features / Functionality | ai_ref_knowledge | OPEA Documentation | best system performance. * Q&A evaluation (accuracy) * Task: Open Q&A * Databases: NQ, TriviaQA and HotpotQA * Metric: Average Accuracy * Indexing: KILT Wikipedia
##### Features / Functionality | best system performance. * Q&A evaluation (accuracy) * Task: Open Q&A * Databases: NQ, TriviaQA and HotpotQA * Metric: Average Accuracy * Indexing: KILT Wikipedia
##### Features / Functionality | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e8251062-6e32-42da-8945-f6ecf8f88924 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 155 | opea-semantic-v1 | 736a1278798fc8c7 | #### A6.2 - Multimodal Chat Over Images and Videos
This reference flow demonstrates a multimodal RAG pipeline which utilizes Intel Labs’ BridgeTower
vision-language model for indexing and LLaVA for inference, both running on Intel Gaudi AI accelerators. The use case for this reference flow is enabling an AI chat assist... | ai_ref_knowledge | OPEA Documentation | #### A6.2 - Multimodal Chat Over Images and Videos
This reference flow demonstrates a multimodal RAG pipeline which utilizes Intel Labs’ BridgeTower
vision-language model for indexing and LLaVA for inference, both running on Intel Gaudi AI accelerators. The use case for this reference flow is enabling an AI chat assist... | #### A6.2 - Multimodal Chat Over Images and Videos
This reference flow demonstrates a multimodal RAG pipeline which utilizes Intel Labs’ BridgeTower
vision-language model for indexing and LLaVA for inference, both running on Intel Gaudi AI accelerators. The use case for this reference flow is enabling an AI chat assist... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e8351c13-9653-4500-844f-f9f6ec5ab08d | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 128 | opea-semantic-v1 | 33a6a3c23c588af6 | (e.g., running Confidential Computing / Trusted execution Environment). Supports attestation of the models being run; full open- source transparency on pre-training dataset, weights, fine-tuning data/recipes
#### A5.4 Enterprise-Ready Grading | ai_ref_knowledge | OPEA Documentation | (e.g., running Confidential Computing / Trusted execution Environment). Supports attestation of the models being run; full open- source transparency on pre-training dataset, weights, fine-tuning data/recipes
#### A5.4 Enterprise-Ready Grading | (e.g., running Confidential Computing / Trusted execution Environment). Supports attestation of the models being run; full open- source transparency on pre-training dataset, weights, fine-tuning data/recipes
#### A5.4 Enterprise-Ready Grading | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e89bb941-e8a7-4134-a3b7-78164cbe6e4f | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 83 | opea-semantic-v1 | a58048bda584c44e | responses based on given prompts and contexts retrieved | vLLM, Ray, TensorRT-LLM | HF TGI, Deci Infery | LLM Models | Open-source and close-source models.
| LLama2-7B,13B, Falcon 40B, Mixtral-7b, Gemma etc. | LLama2-70B, OpenAI, Cohere, Gemini, etc. | Guardrails | A software component for enforcing compliance, filteri... | ai_ref_knowledge | OPEA Documentation | responses based on given prompts and contexts retrieved | vLLM, Ray, TensorRT-LLM | HF TGI, Deci Infery | LLM Models | Open-source and close-source models.
| LLama2-7B,13B, Falcon 40B, Mixtral-7b, Gemma etc. | LLama2-70B, OpenAI, Cohere, Gemini, etc. | Guardrails | A software component for enforcing compliance, filteri... | responses based on given prompts and contexts retrieved | vLLM, Ray, TensorRT-LLM | HF TGI, Deci Infery | LLM Models | Open-source and close-source models.
| LLama2-7B,13B, Falcon 40B, Mixtral-7b, Gemma etc. | LLama2-70B, OpenAI, Cohere, Gemini, etc. | Guardrails | A software component for enforcing compliance, filteri... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e993ed8d-51ed-4c00-b7d1-5e7b6a619903 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 46 | opea-semantic-v1 | b301f03a54cf040c | current (early 2024) benchmarks are focusing on performance and features, there will be an effort to complement those as needed for assessing trustworthiness and enterprise-readiness.
The development of assessments should use learnings from similar evaluations when available. For
example, referring to RAG evaluation as... | ai_ref_knowledge | OPEA Documentation | current (early 2024) benchmarks are focusing on performance and features, there will be an effort to complement those as needed for assessing trustworthiness and enterprise-readiness.
The development of assessments should use learnings from similar evaluations when available. For
example, referring to RAG evaluation as... | current (early 2024) benchmarks are focusing on performance and features, there will be an effort to complement those as needed for assessing trustworthiness and enterprise-readiness.
The development of assessments should use learnings from similar evaluations when available. For
example, referring to RAG evaluation as... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ecd5b2d3-dfbd-4d1f-8a01-d9a86a76c4ff | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 71 | opea-semantic-v1 | eb16b3abe10e94c7 | readiness, users can gain insight into what can be achieved. The experience serves as valuable learning tools towards achieving their AI deployment goals and planning.
* Facilitate easy deployment: Reference flows are designed to be accessible and
easy to instantiate with relatively low effort. It allows replicating a... | ai_ref_knowledge | OPEA Documentation | readiness, users can gain insight into what can be achieved. The experience serves as valuable learning tools towards achieving their AI deployment goals and planning.
* Facilitate easy deployment: Reference flows are designed to be accessible and
easy to instantiate with relatively low effort. It allows replicating a... | readiness, users can gain insight into what can be achieved. The experience serves as valuable learning tools towards achieving their AI deployment goals and planning.
* Facilitate easy deployment: Reference flows are designed to be accessible and
easy to instantiate with relatively low effort. It allows replicating a... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ee4dc535-1fa6-4da9-8fb2-59916f116893 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 118 | opea-semantic-v1 | 6109e47d1dee7435 | ##### Features Grade
Features evaluated for interoperability, platform capabilities, user experience (ease of use), AI
methods being applied, and specialized functionality. | ai_ref_knowledge | OPEA Documentation | ##### Features Grade
Features evaluated for interoperability, platform capabilities, user experience (ease of use), AI
methods being applied, and specialized functionality. | ##### Features Grade
Features evaluated for interoperability, platform capabilities, user experience (ease of use), AI
methods being applied, and specialized functionality. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
efc099a0-4c21-44be-aef2-dd0a57e6c58f | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 67 | opea-semantic-v1 | 25a58706c17a42c2 | ## 6. Reference flows
Reference flows are end-to-end instantiations of use cases within the OPEA framework. They represent
a specific selection of interoperable components to create an effective implementation of a GenAI
solution. Reference flows documentation and links need to include comprehensive information
necessa... | ai_ref_knowledge | OPEA Documentation | ## 6. Reference flows
Reference flows are end-to-end instantiations of use cases within the OPEA framework. They represent
a specific selection of interoperable components to create an effective implementation of a GenAI
solution. Reference flows documentation and links need to include comprehensive information
necessa... | ## 6. Reference flows
Reference flows are end-to-end instantiations of use cases within the OPEA framework. They represent
a specific selection of interoperable components to create an effective implementation of a GenAI
solution. Reference flows documentation and links need to include comprehensive information
necessa... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f0dd24a7-67d7-410f-b5ae-e9c0d15cefbb | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 70 | opea-semantic-v1 | f858c72c899cbe99 | Reference Flows serve several primary objectives:
* Demonstrate representative instantiations: Within OPEA framework, reference
flows showcase specific uses and tasks. Given the framework’s inherent
flexibility, various combinations of components are possible allowing for
maximum flexibility. Reference flows demonst... | ai_ref_knowledge | OPEA Documentation | Reference Flows serve several primary objectives:
* Demonstrate representative instantiations: Within OPEA framework, reference
flows showcase specific uses and tasks. Given the framework’s inherent
flexibility, various combinations of components are possible allowing for
maximum flexibility. Reference flows demonst... | Reference Flows serve several primary objectives:
* Demonstrate representative instantiations: Within OPEA framework, reference
flows showcase specific uses and tasks. Given the framework’s inherent
flexibility, various combinations of components are possible allowing for
maximum flexibility. Reference flows demonst... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f2d767f9-e1ef-4c59-b205-d8619d2ace99 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 27 | opea-semantic-v1 | 89e22ad2d693e695 | The OPEA evaluations can be viewed at the following levels:
* Assessment – Detailed tests or benchmarks done for particular modules or
attributes of the end-to-end flow. Assessments will be elaborate and specific,
checking for the functionality and characteristics specified for that module
or flow. * Grading - Aggre... | ai_ref_knowledge | OPEA Documentation | The OPEA evaluations can be viewed at the following levels:
* Assessment – Detailed tests or benchmarks done for particular modules or
attributes of the end-to-end flow. Assessments will be elaborate and specific,
checking for the functionality and characteristics specified for that module
or flow. * Grading - Aggre... | The OPEA evaluations can be viewed at the following levels:
* Assessment – Detailed tests or benchmarks done for particular modules or
attributes of the end-to-end flow. Assessments will be elaborate and specific,
checking for the functionality and characteristics specified for that module
or flow. * Grading - Aggre... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f2ead309-8d64-410b-b63b-a65b2a316215 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 144 | opea-semantic-v1 | 1dd3f0099df32bfb | enterprise RAG flow that runs on Xeon (GNR) with vector database and an embedding model, and with a Gaudi2 serving backend for LLM model inference.
The reference flow demonstrates a RAG application that provides an AI assistant experience with
capability of retrieving information from an external source to enhance the ... | ai_ref_knowledge | OPEA Documentation | enterprise RAG flow that runs on Xeon (GNR) with vector database and an embedding model, and with a Gaudi2 serving backend for LLM model inference.
The reference flow demonstrates a RAG application that provides an AI assistant experience with
capability of retrieving information from an external source to enhance the ... | enterprise RAG flow that runs on Xeon (GNR) with vector database and an embedding model, and with a Gaudi2 serving backend for LLM model inference.
The reference flow demonstrates a RAG application that provides an AI assistant experience with
capability of retrieving information from an external source to enhance the ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f6f1756a-892e-4105-8d5f-7385ab972671 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 113 | opea-semantic-v1 | 1139e948d569a361 | real use cases. Each solution submitted to the OpenRag alliance will be measured against it. Performance measurements will include latency, throughput, scalability, accuracy and consistency.
* Level 1 – Baseline benchmark complete
* Level 2 – Meets performance levels that are expected for the bulk of GenAI solutions pe... | ai_ref_knowledge | OPEA Documentation | real use cases. Each solution submitted to the OpenRag alliance will be measured against it. Performance measurements will include latency, throughput, scalability, accuracy and consistency.
* Level 1 – Baseline benchmark complete
* Level 2 – Meets performance levels that are expected for the bulk of GenAI solutions pe... | real use cases. Each solution submitted to the OpenRag alliance will be measured against it. Performance measurements will include latency, throughput, scalability, accuracy and consistency.
* Level 1 – Baseline benchmark complete
* Level 2 – Meets performance levels that are expected for the bulk of GenAI solutions pe... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f8e51191-a649-4d25-a228-3417842d22c1 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 124 | opea-semantic-v1 | 7860a29a97277d09 | that protect data in use – providing confidentiality and integrity from privileged and other processes running on the same infrastructure. Valuable particularly in the cloud.
* Attesting binaries in use, be it models or software. * Audit logs that indicate when and what updates were applied either to models or other so... | ai_ref_knowledge | OPEA Documentation | that protect data in use – providing confidentiality and integrity from privileged and other processes running on the same infrastructure. Valuable particularly in the cloud.
* Attesting binaries in use, be it models or software. * Audit logs that indicate when and what updates were applied either to models or other so... | that protect data in use – providing confidentiality and integrity from privileged and other processes running on the same infrastructure. Valuable particularly in the cloud.
* Attesting binaries in use, be it models or software. * Audit logs that indicate when and what updates were applied either to models or other so... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f8f443fd-3163-4097-8c92-f4757c000beb | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 19 | opea-semantic-v1 | bdfd5cb7b7d0e6b6 | choice (see following evaluation section). There will be multiple options offered from various providers for each module and model, to allow for choice and diversity.
This platform consists of a set of compositional capabilities that allow for building custom agents,
customizing AI assistants, and creating a full end-t... | ai_ref_knowledge | OPEA Documentation | choice (see following evaluation section). There will be multiple options offered from various providers for each module and model, to allow for choice and diversity.
This platform consists of a set of compositional capabilities that allow for building custom agents,
customizing AI assistants, and creating a full end-t... | choice (see following evaluation section). There will be multiple options offered from various providers for each module and model, to allow for choice and diversity.
This platform consists of a set of compositional capabilities that allow for building custom agents,
customizing AI assistants, and creating a full end-t... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
fa87349a-0ccc-486d-9aa5-162e80c54910 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 129 | opea-semantic-v1 | 40dfea3fd639d6ed | #### A5.4 Enterprise-Ready Grading
Grading enterprise-readiness consists of evaluating the ability of the overall solution to be deployed in
production in an enterprise environment. The following criteria will be taken into account: | ai_ref_knowledge | OPEA Documentation | #### A5.4 Enterprise-Ready Grading
Grading enterprise-readiness consists of evaluating the ability of the overall solution to be deployed in
production in an enterprise environment. The following criteria will be taken into account: | #### A5.4 Enterprise-Ready Grading
Grading enterprise-readiness consists of evaluating the ability of the overall solution to be deployed in
production in an enterprise environment. The following criteria will be taken into account: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ff078e81-0ed9-4347-9d37-a6184a8aab03 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 138 | opea-semantic-v1 | 99e0f5caf87ffe0c | readiness, users can gain insight into what can be achieved. The experience serves as valuable learning tools towards achieving their AI deployment goals and planning.
* Facilitate easy deployment: Reference flows are designed to be accessible and
easy to instantiate with relatively lower effort. It allows replicating... | ai_ref_knowledge | OPEA Documentation | readiness, users can gain insight into what can be achieved. The experience serves as valuable learning tools towards achieving their AI deployment goals and planning.
* Facilitate easy deployment: Reference flows are designed to be accessible and
easy to instantiate with relatively lower effort. It allows replicating... | readiness, users can gain insight into what can be achieved. The experience serves as valuable learning tools towards achieving their AI deployment goals and planning.
* Facilitate easy deployment: Reference flows are designed to be accessible and
easy to instantiate with relatively lower effort. It allows replicating... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ff2024ae-f72c-4236-a081-62bd1162b94e | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 64 | opea-semantic-v1 | f887746093cc6c6e | and users that the GenAI solution being evaluated is competitive and ready for broad deployment – stopping short of promising a guarantee of any sort.
The assessment test suites and associated grading will allow for ISVs and industry solution adopters to
self-test, evaluate and grade themselves on the various metrics. ... | ai_ref_knowledge | OPEA Documentation | and users that the GenAI solution being evaluated is competitive and ready for broad deployment – stopping short of promising a guarantee of any sort.
The assessment test suites and associated grading will allow for ISVs and industry solution adopters to
self-test, evaluate and grade themselves on the various metrics. ... | and users that the GenAI solution being evaluated is competitive and ready for broad deployment – stopping short of promising a guarantee of any sort.
The assessment test suites and associated grading will allow for ISVs and industry solution adopters to
self-test, evaluate and grade themselves on the various metrics. ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
003f1214-761b-4af5-8c5e-e8f6aa0155f3 | OPEA Documentation | file://datasets/opea-docs/getting-started/README.md | unknown | 1e67df9c-737e-4ea8-ad74-3f20368e33dc | 57 | opea-semantic-v1 | f65291b45e75b45c | Rules". Add the following information: - Source CIDR: **0.0.0.0/0** - Source Port Range: **All** - Destination Port Range: **80** - Click on "Add Ingress Rule"
11. Connect using ssh (`ssh -i <private_key> ubuntu@<public_ip_address>`). | ai_ref_knowledge | OPEA Documentation | Rules". Add the following information: - Source CIDR: **0.0.0.0/0** - Source Port Range: **All** - Destination Port Range: **80** - Click on "Add Ingress Rule"
11. Connect using ssh (`ssh -i <private_key> ubuntu@<public_ip_address>`). | Rules". Add the following information: - Source CIDR: **0.0.0.0/0** - Source Port Range: **All** - Destination Port Range: **80** - Click on "Add Ingress Rule"
11. Connect using ssh (`ssh -i <private_key> ubuntu@<public_ip_address>`). | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0152fd60-d2ae-44ee-a61e-adfec8e60074 | OPEA Documentation | file://datasets/opea-docs/getting-started/README.md | unknown | 1e67df9c-737e-4ea8-ad74-3f20368e33dc | 86 | opea-semantic-v1 | 4fa8a84ca4806865 | Interact with ChatQnA via a browser interface:
* To view the ChatQnA interface, open a browser and navigate to the UI by inserting the public facing IP address: `http://{public_ip}:80’. | ai_ref_knowledge | OPEA Documentation | Interact with ChatQnA via a browser interface:
* To view the ChatQnA interface, open a browser and navigate to the UI by inserting the public facing IP address: `http://{public_ip}:80’. | Interact with ChatQnA via a browser interface:
* To view the ChatQnA interface, open a browser and navigate to the UI by inserting the public facing IP address: `http://{public_ip}:80’. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation |
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