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7c29cabe-1754-47f3-bb9d-b963c2863c4b | OPEA Documentation | file://datasets/opea-docs/developer-guides/secondary_readme_genai_examples_template.md | unknown | ca0fbeba-d63a-43cb-b651-8873c4101fe4 | 5 | opea-semantic-v1 | 0a2d363f311a72ce | <How do you check the status of the deployment?>
*Additional Options for Deployment*
<Use this table to describe additional options that are available for this deployment, such as vector databases or LLM serving engine. List the YAML files to use for these options.> | ai_ref_knowledge | OPEA Documentation | <How do you check the status of the deployment?>
*Additional Options for Deployment*
<Use this table to describe additional options that are available for this deployment, such as vector databases or LLM serving engine. List the YAML files to use for these options.> | <How do you check the status of the deployment?>
*Additional Options for Deployment*
<Use this table to describe additional options that are available for this deployment, such as vector databases or LLM serving engine. List the YAML files to use for these options.> | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
aaa561db-4a88-4b60-b274-f92f0d166213 | OPEA Documentation | file://datasets/opea-docs/developer-guides/secondary_readme_genai_examples_template.md | unknown | ca0fbeba-d63a-43cb-b651-8873c4101fe4 | 9 | opea-semantic-v1 | 178e6b39cf489e61 | validation should display:> - <The name of the microservice> - <The test procedure used> - <Applicable CURL commands> - <An example of the expected output>
<Also include instructions to open the UI:>
- <What port should the developer use?>
- <Is port forwarding necessary?>
- <For Intel® Tiber™ AI Cloud (ITAC), is a loa... | ai_ref_knowledge | OPEA Documentation | validation should display:> - <The name of the microservice> - <The test procedure used> - <Applicable CURL commands> - <An example of the expected output>
<Also include instructions to open the UI:>
- <What port should the developer use?>
- <Is port forwarding necessary?>
- <For Intel® Tiber™ AI Cloud (ITAC), is a loa... | validation should display:> - <The name of the microservice> - <The test procedure used> - <Applicable CURL commands> - <An example of the expected output>
<Also include instructions to open the UI:>
- <What port should the developer use?>
- <Is port forwarding necessary?>
- <For Intel® Tiber™ AI Cloud (ITAC), is a loa... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c775132d-0213-4e6b-8ea8-5529db45ea2a | OPEA Documentation | file://datasets/opea-docs/developer-guides/secondary_readme_genai_examples_template.md | unknown | ca0fbeba-d63a-43cb-b651-8873c4101fe4 | 7 | opea-semantic-v1 | 4d9da6525752b70a | | File | Description | | ------------------------------------| ---------------------------------------------------------------------| | | |
## Validation
<How do you validate the health of the microservices that are used in this sample?> | ai_ref_knowledge | OPEA Documentation | | File | Description | | ------------------------------------| ---------------------------------------------------------------------| | | |
## Validation
<How do you validate the health of the microservices that are used in this sample?> | | File | Description | | ------------------------------------| ---------------------------------------------------------------------| | | |
## Validation
<How do you validate the health of the microservices that are used in this sample?> | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d9c2207e-aa39-4e98-85cb-a5005a213cd3 | OPEA Documentation | file://datasets/opea-docs/developer-guides/secondary_readme_genai_examples_template.md | unknown | ca0fbeba-d63a-43cb-b651-8873c4101fe4 | 4 | opea-semantic-v1 | 28a16eeb3b2a4482 | | Environment Variable | Description | Default Value | | ------------------------------------| ---------------------------------------------------------------------| | | | | | | | |
<How do you check the status of the deployment?> | ai_ref_knowledge | OPEA Documentation | | Environment Variable | Description | Default Value | | ------------------------------------| ---------------------------------------------------------------------| | | | | | | | |
<How do you check the status of the deployment?> | | Environment Variable | Description | Default Value | | ------------------------------------| ---------------------------------------------------------------------| | | | | | | | |
<How do you check the status of the deployment?> | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
dab045a4-5082-4aec-864f-16de84f4fd2f | OPEA Documentation | file://datasets/opea-docs/developer-guides/secondary_readme_genai_examples_template.md | unknown | ca0fbeba-d63a-43cb-b651-8873c4101fe4 | 11 | opea-semantic-v1 | a1c4b2c71206b34e | supported, how do you profile the microservices that are used in this sample?> <How do you prepare dashboards in Prometheus or Grafana for this purpose?>
## Termination
<How do you stop the microservices?> | ai_ref_knowledge | OPEA Documentation | supported, how do you profile the microservices that are used in this sample?> <How do you prepare dashboards in Prometheus or Grafana for this purpose?>
## Termination
<How do you stop the microservices?> | supported, how do you profile the microservices that are used in this sample?> <How do you prepare dashboards in Prometheus or Grafana for this purpose?>
## Termination
<How do you stop the microservices?> | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
dc428d80-870c-4b62-90cf-f65d1ebedb05 | OPEA Documentation | file://datasets/opea-docs/developer-guides/secondary_readme_genai_examples_template.md | unknown | ca0fbeba-d63a-43cb-b651-8873c4101fe4 | 6 | opea-semantic-v1 | 935a9ab632ee441e | additional options that are available for this deployment, such as vector databases or LLM serving engine. List the YAML files to use for these options.>
| File | Description |
| ------------------------------------| ---------------------------------------------------------------------|
| | | | ai_ref_knowledge | OPEA Documentation | additional options that are available for this deployment, such as vector databases or LLM serving engine. List the YAML files to use for these options.>
| File | Description |
| ------------------------------------| ---------------------------------------------------------------------|
| | | | additional options that are available for this deployment, such as vector databases or LLM serving engine. List the YAML files to use for these options.>
| File | Description |
| ------------------------------------| ---------------------------------------------------------------------|
| | | | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e0335888-93ac-4997-a3b1-4a3c37d3dd47 | OPEA Documentation | file://datasets/opea-docs/developer-guides/secondary_readme_genai_examples_template.md | unknown | ca0fbeba-d63a-43cb-b651-8873c4101fe4 | 2 | opea-semantic-v1 | 1acd911ebf3e3944 | ## Overview <What is the purpose of this README file?>
## Deployment
<What are the prerequisites before you deploy the sample on the target hardware?>
<What environment variables should be set before running Docker Compose?> | ai_ref_knowledge | OPEA Documentation | ## Overview <What is the purpose of this README file?>
## Deployment
<What are the prerequisites before you deploy the sample on the target hardware?>
<What environment variables should be set before running Docker Compose?> | ## Overview <What is the purpose of this README file?>
## Deployment
<What are the prerequisites before you deploy the sample on the target hardware?>
<What environment variables should be set before running Docker Compose?> | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
03c5b444-fc34-4575-9a60-2c0b8ff85728 | OPEA Documentation | file://datasets/opea-docs/faq.md | unknown | f72922d4-5e8c-4b60-a4d3-7d46549dc9e7 | 11 | opea-semantic-v1 | b92a4537a581b724 | add to that contribution, including a software infrastructure stack to enable fully containerized AI workload deployments, as well as potentially implementations of those containerized workloads.
## When you say Technical Conceptual Framework, what components are included? The models and modules can be part of an OPEA ... | ai_ref_knowledge | OPEA Documentation | add to that contribution, including a software infrastructure stack to enable fully containerized AI workload deployments, as well as potentially implementations of those containerized workloads.
## When you say Technical Conceptual Framework, what components are included? The models and modules can be part of an OPEA ... | add to that contribution, including a software infrastructure stack to enable fully containerized AI workload deployments, as well as potentially implementations of those containerized workloads.
## When you say Technical Conceptual Framework, what components are included? The models and modules can be part of an OPEA ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
03d99372-1545-47bf-969f-7c0c27ff541d | OPEA Documentation | file://datasets/opea-docs/faq.md | unknown | f72922d4-5e8c-4b60-a4d3-7d46549dc9e7 | 3 | opea-semantic-v1 | fabefc39d6d46beb | ensuring the solution is secure and trustworthy. The lack of a framework that encompasses both proprietary and open solutions impedes enterprises from charting their destiny.
This results in an enormous investment of time and money, impacting the time-to-market advantage. OPEA answers the need for a multi-provider, eco... | ai_ref_knowledge | OPEA Documentation | ensuring the solution is secure and trustworthy. The lack of a framework that encompasses both proprietary and open solutions impedes enterprises from charting their destiny.
This results in an enormous investment of time and money, impacting the time-to-market advantage. OPEA answers the need for a multi-provider, eco... | ensuring the solution is secure and trustworthy. The lack of a framework that encompasses both proprietary and open solutions impedes enterprises from charting their destiny.
This results in an enormous investment of time and money, impacting the time-to-market advantage. OPEA answers the need for a multi-provider, eco... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0f8429e3-d838-4609-b221-2f4471161de4 | OPEA Documentation | file://datasets/opea-docs/faq.md | unknown | f72922d4-5e8c-4b60-a4d3-7d46549dc9e7 | 6 | opea-semantic-v1 | 50cf3614ae2a1d08 | together in a vendor-neutral manner and delivers on the promise of openness, security, and scalability. This is our primary motivation for creating the OPEA project.
## Will OPEA reference implementations work with proprietary components? Like any other open-source project, the community will determine which components... | ai_ref_knowledge | OPEA Documentation | together in a vendor-neutral manner and delivers on the promise of openness, security, and scalability. This is our primary motivation for creating the OPEA project.
## Will OPEA reference implementations work with proprietary components? Like any other open-source project, the community will determine which components... | together in a vendor-neutral manner and delivers on the promise of openness, security, and scalability. This is our primary motivation for creating the OPEA project.
## Will OPEA reference implementations work with proprietary components? Like any other open-source project, the community will determine which components... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
189b7846-29df-4f89-8ca9-765f02649bad | OPEA Documentation | file://datasets/opea-docs/faq.md | unknown | f72922d4-5e8c-4b60-a4d3-7d46549dc9e7 | 0 | opea-semantic-v1 | 8d76f4d727a0440f | # OPEA Frequently Asked Questions
## What is OPEA's mission? OPEA’s mission is to offer a validated enterprise-grade GenAI (Generative Artificial Intelligence) RAG reference implementation. This will simplify GenAI development and deployment, thereby accelerating time-to-market. | ai_ref_knowledge | OPEA Documentation | # OPEA Frequently Asked Questions
## What is OPEA's mission? OPEA’s mission is to offer a validated enterprise-grade GenAI (Generative Artificial Intelligence) RAG reference implementation. This will simplify GenAI development and deployment, thereby accelerating time-to-market. | # OPEA Frequently Asked Questions
## What is OPEA's mission? OPEA’s mission is to offer a validated enterprise-grade GenAI (Generative Artificial Intelligence) RAG reference implementation. This will simplify GenAI development and deployment, thereby accelerating time-to-market. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
2bd19792-6693-4770-b05e-0ca8bd1c44f1 | OPEA Documentation | file://datasets/opea-docs/faq.md | unknown | f72922d4-5e8c-4b60-a4d3-7d46549dc9e7 | 1 | opea-semantic-v1 | 9f0d840f00b1ef48 | OPEA’s mission is to offer a validated enterprise-grade GenAI (Generative Artificial Intelligence) RAG reference implementation. This will simplify GenAI development and deployment, thereby accelerating time-to-market.
## What is OPEA? The project currently consists of a technical conceptual framework that enables GenA... | ai_ref_knowledge | OPEA Documentation | OPEA’s mission is to offer a validated enterprise-grade GenAI (Generative Artificial Intelligence) RAG reference implementation. This will simplify GenAI development and deployment, thereby accelerating time-to-market.
## What is OPEA? The project currently consists of a technical conceptual framework that enables GenA... | OPEA’s mission is to offer a validated enterprise-grade GenAI (Generative Artificial Intelligence) RAG reference implementation. This will simplify GenAI development and deployment, thereby accelerating time-to-market.
## What is OPEA? The project currently consists of a technical conceptual framework that enables GenA... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
400580f2-54ec-42fb-9b93-f00c3c07f8df | OPEA Documentation | file://datasets/opea-docs/faq.md | unknown | f72922d4-5e8c-4b60-a4d3-7d46549dc9e7 | 14 | opea-semantic-v1 | 65ab830852eb0104 | ## What are the different ways partners can contribute to OPEA? There are different ways partners can contribute to this project:
* Join the project and contribute assets in terms of use cases, code, test harness, etc. * Provide technical leadership
* Drive community engagement and evangelism
* Offer program manageme... | ai_ref_knowledge | OPEA Documentation | ## What are the different ways partners can contribute to OPEA? There are different ways partners can contribute to this project:
* Join the project and contribute assets in terms of use cases, code, test harness, etc. * Provide technical leadership
* Drive community engagement and evangelism
* Offer program manageme... | ## What are the different ways partners can contribute to OPEA? There are different ways partners can contribute to this project:
* Join the project and contribute assets in terms of use cases, code, test harness, etc. * Provide technical leadership
* Drive community engagement and evangelism
* Offer program manageme... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
430f40f8-6fbb-48e8-9c59-872fbf7060ea | OPEA Documentation | file://datasets/opea-docs/faq.md | unknown | f72922d4-5e8c-4b60-a4d3-7d46549dc9e7 | 12 | opea-semantic-v1 | e7bc87a7a5a5c3e4 | of an OPEA repository or be published in a stable, unobstructed repository (e.g., Hugging Face) and cleared for use by an OPEA assessment. These include:
* Ingest/Data Processing
* Embedding Models/Services
* Indexing/Vector/Graph data stores
* Retrieval/Ranking
* Prompt Engines
* Guardrails
* Memory systems | ai_ref_knowledge | OPEA Documentation | of an OPEA repository or be published in a stable, unobstructed repository (e.g., Hugging Face) and cleared for use by an OPEA assessment. These include:
* Ingest/Data Processing
* Embedding Models/Services
* Indexing/Vector/Graph data stores
* Retrieval/Ranking
* Prompt Engines
* Guardrails
* Memory systems | of an OPEA repository or be published in a stable, unobstructed repository (e.g., Hugging Face) and cleared for use by an OPEA assessment. These include:
* Ingest/Data Processing
* Embedding Models/Services
* Indexing/Vector/Graph data stores
* Retrieval/Ranking
* Prompt Engines
* Guardrails
* Memory systems | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4a7531a6-2d99-4cef-9141-887b9488e457 | OPEA Documentation | file://datasets/opea-docs/faq.md | unknown | f72922d4-5e8c-4b60-a4d3-7d46549dc9e7 | 7 | opea-semantic-v1 | e302e53ed44612cb | which components are needed by the broader ecosystem. Enterprises can always extend the OPEA project with other multi-vendor proprietary solutions to achieve their business goals.
## What does OPEA acronym stand for? Open Platform for Enterprise AI. | ai_ref_knowledge | OPEA Documentation | which components are needed by the broader ecosystem. Enterprises can always extend the OPEA project with other multi-vendor proprietary solutions to achieve their business goals.
## What does OPEA acronym stand for? Open Platform for Enterprise AI. | which components are needed by the broader ecosystem. Enterprises can always extend the OPEA project with other multi-vendor proprietary solutions to achieve their business goals.
## What does OPEA acronym stand for? Open Platform for Enterprise AI. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5c133ed4-4fd7-4110-8e3f-8b9091103df1 | OPEA Documentation | file://datasets/opea-docs/faq.md | unknown | f72922d4-5e8c-4b60-a4d3-7d46549dc9e7 | 5 | opea-semantic-v1 | 3ebe501b212b5728 | tenets – openness, security, and scalability. This will require the ecosystem to work together to build reference implementations that are performant, trustworthy, and enterprise-grade ready.
## How does it compare to other options for deploying Gen AI solutions within the enterprise? There is no alternative that bring... | ai_ref_knowledge | OPEA Documentation | tenets – openness, security, and scalability. This will require the ecosystem to work together to build reference implementations that are performant, trustworthy, and enterprise-grade ready.
## How does it compare to other options for deploying Gen AI solutions within the enterprise? There is no alternative that bring... | tenets – openness, security, and scalability. This will require the ecosystem to work together to build reference implementations that are performant, trustworthy, and enterprise-grade ready.
## How does it compare to other options for deploying Gen AI solutions within the enterprise? There is no alternative that bring... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
60df5182-792b-4afd-9b67-84fa45436f25 | OPEA Documentation | file://datasets/opea-docs/faq.md | unknown | f72922d4-5e8c-4b60-a4d3-7d46549dc9e7 | 16 | opea-semantic-v1 | 225e7e082338b3e7 | partners see the latest draft of the Conceptual Framework spec? A version of the spec is available in the documentation (["docs"](https://github.com/opea-project/docs)) repository within this project.
## Is there a cost for joining? There is no cost for anyone to join and contribute to the OPEA project. | ai_ref_knowledge | OPEA Documentation | partners see the latest draft of the Conceptual Framework spec? A version of the spec is available in the documentation (["docs"](https://github.com/opea-project/docs)) repository within this project.
## Is there a cost for joining? There is no cost for anyone to join and contribute to the OPEA project. | partners see the latest draft of the Conceptual Framework spec? A version of the spec is available in the documentation (["docs"](https://github.com/opea-project/docs)) repository within this project.
## Is there a cost for joining? There is no cost for anyone to join and contribute to the OPEA project. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
62c1443d-83da-4e47-a809-5ee312602083 | OPEA Documentation | file://datasets/opea-docs/faq.md | unknown | f72922d4-5e8c-4b60-a4d3-7d46549dc9e7 | 2 | opea-semantic-v1 | 7e1756c96447c803 | and validated in an open manner. Partnering with the LF AI & Data places it in the perfect spot for multi-partner development, evolution, and expansion.
## What problems are faced by GenAI deployments within the enterprise? Enterprises face a myriad of challenges in the development and deployment of GenAI. The developm... | ai_ref_knowledge | OPEA Documentation | and validated in an open manner. Partnering with the LF AI & Data places it in the perfect spot for multi-partner development, evolution, and expansion.
## What problems are faced by GenAI deployments within the enterprise? Enterprises face a myriad of challenges in the development and deployment of GenAI. The developm... | and validated in an open manner. Partnering with the LF AI & Data places it in the perfect spot for multi-partner development, evolution, and expansion.
## What problems are faced by GenAI deployments within the enterprise? Enterprises face a myriad of challenges in the development and deployment of GenAI. The developm... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7bf1d824-6e72-47cb-841c-a3eb43a00d6b | OPEA Documentation | file://datasets/opea-docs/faq.md | unknown | f72922d4-5e8c-4b60-a4d3-7d46549dc9e7 | 10 | opea-semantic-v1 | aa935b3f80c6ebdb | projects joined OPEA? AnyScale, Cloudera, DataStax, Domino Data Lab, HuggingFace, Intel, KX, MariaDB Foundation, MinIO, Qdrant, Red Hat, SAS, VMware by Broadcom, Yellowbrick Data, Zilliz.
## What is Intel contributing? OPEA is to be defined jointly by several community partners, with a call for broad ecosystem contribu... | ai_ref_knowledge | OPEA Documentation | projects joined OPEA? AnyScale, Cloudera, DataStax, Domino Data Lab, HuggingFace, Intel, KX, MariaDB Foundation, MinIO, Qdrant, Red Hat, SAS, VMware by Broadcom, Yellowbrick Data, Zilliz.
## What is Intel contributing? OPEA is to be defined jointly by several community partners, with a call for broad ecosystem contribu... | projects joined OPEA? AnyScale, Cloudera, DataStax, Domino Data Lab, HuggingFace, Intel, KX, MariaDB Foundation, MinIO, Qdrant, Red Hat, SAS, VMware by Broadcom, Yellowbrick Data, Zilliz.
## What is Intel contributing? OPEA is to be defined jointly by several community partners, with a call for broad ecosystem contribu... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
82573551-f366-4b75-b3d3-8c0efba1d692 | OPEA Documentation | file://datasets/opea-docs/faq.md | unknown | f72922d4-5e8c-4b60-a4d3-7d46549dc9e7 | 17 | opea-semantic-v1 | 9b2ae2a751b2d666 | ## Is there a cost for joining? There is no cost for anyone to join and contribute to the OPEA project.
## Do I need to be a Linux Foundation member to join? Anyone can join and contribute. You don’t need to be a Linux Foundation member. | ai_ref_knowledge | OPEA Documentation | ## Is there a cost for joining? There is no cost for anyone to join and contribute to the OPEA project.
## Do I need to be a Linux Foundation member to join? Anyone can join and contribute. You don’t need to be a Linux Foundation member. | ## Is there a cost for joining? There is no cost for anyone to join and contribute to the OPEA project.
## Do I need to be a Linux Foundation member to join? Anyone can join and contribute. You don’t need to be a Linux Foundation member. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
828f6046-5d49-4669-a155-b3771efb3278 | OPEA Documentation | file://datasets/opea-docs/faq.md | unknown | f72922d4-5e8c-4b60-a4d3-7d46549dc9e7 | 4 | opea-semantic-v1 | 74b971ead244000f | OPEA answers the need for a multi-provider, ecosystem-supported framework that enables the evaluation, selection, customization, and trusted deployment of solutions that businesses can rely on.
## Why now? The major adoption and deployment cycle of robust, secure, enterprise-grade GenAI solutions across all industries ... | ai_ref_knowledge | OPEA Documentation | OPEA answers the need for a multi-provider, ecosystem-supported framework that enables the evaluation, selection, customization, and trusted deployment of solutions that businesses can rely on.
## Why now? The major adoption and deployment cycle of robust, secure, enterprise-grade GenAI solutions across all industries ... | OPEA answers the need for a multi-provider, ecosystem-supported framework that enables the evaluation, selection, customization, and trusted deployment of solutions that businesses can rely on.
## Why now? The major adoption and deployment cycle of robust, secure, enterprise-grade GenAI solutions across all industries ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a7514877-23c9-4191-9304-f42b2adc5c15 | OPEA Documentation | file://datasets/opea-docs/faq.md | unknown | f72922d4-5e8c-4b60-a4d3-7d46549dc9e7 | 15 | opea-semantic-v1 | 344dc9d4e657a42b | committer, and adopter * Define and offer use cases for various industry verticals that shape OPEA project * Build the infrastructure to support OPEA projects
## Where can partners see the latest draft of the Conceptual Framework spec? A version of the spec is available in the documentation (["docs"](https://github.com... | ai_ref_knowledge | OPEA Documentation | committer, and adopter * Define and offer use cases for various industry verticals that shape OPEA project * Build the infrastructure to support OPEA projects
## Where can partners see the latest draft of the Conceptual Framework spec? A version of the spec is available in the documentation (["docs"](https://github.com... | committer, and adopter * Define and offer use cases for various industry verticals that shape OPEA project * Build the infrastructure to support OPEA projects
## Where can partners see the latest draft of the Conceptual Framework spec? A version of the spec is available in the documentation (["docs"](https://github.com... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b017ef27-8f35-42c2-b885-46da20098132 | OPEA Documentation | file://datasets/opea-docs/faq.md | unknown | f72922d4-5e8c-4b60-a4d3-7d46549dc9e7 | 8 | opea-semantic-v1 | ebd1453ff4678ba0 | ## What does OPEA acronym stand for? Open Platform for Enterprise AI.
## How do I pronounce OPEA? It is pronounced ‘OH-PEA-AY.’ | ai_ref_knowledge | OPEA Documentation | ## What does OPEA acronym stand for? Open Platform for Enterprise AI.
## How do I pronounce OPEA? It is pronounced ‘OH-PEA-AY.’ | ## What does OPEA acronym stand for? Open Platform for Enterprise AI.
## How do I pronounce OPEA? It is pronounced ‘OH-PEA-AY.’ | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c52856ce-eef3-4ff1-aa03-ab4ba3af4256 | OPEA Documentation | file://datasets/opea-docs/faq.md | unknown | f72922d4-5e8c-4b60-a4d3-7d46549dc9e7 | 18 | opea-semantic-v1 | bea3e0408073f09a | Do I need to be a Linux Foundation member to join? Anyone can join and contribute. You don’t need to be a Linux Foundation member.
## Where can I report a bug or vulnerability? Vulnerability reports and bug submissions can be sent to [info@opea.dev](mailto:info@opea.dev). | ai_ref_knowledge | OPEA Documentation | Do I need to be a Linux Foundation member to join? Anyone can join and contribute. You don’t need to be a Linux Foundation member.
## Where can I report a bug or vulnerability? Vulnerability reports and bug submissions can be sent to [info@opea.dev](mailto:info@opea.dev). | Do I need to be a Linux Foundation member to join? Anyone can join and contribute. You don’t need to be a Linux Foundation member.
## Where can I report a bug or vulnerability? Vulnerability reports and bug submissions can be sent to [info@opea.dev](mailto:info@opea.dev). | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d70e9211-4906-409e-a710-4374b0695da3 | OPEA Documentation | file://datasets/opea-docs/faq.md | unknown | f72922d4-5e8c-4b60-a4d3-7d46549dc9e7 | 13 | opea-semantic-v1 | c764808b0e08eed6 | * Ingest/Data Processing * Embedding Models/Services * Indexing/Vector/Graph data stores * Retrieval/Ranking * Prompt Engines * Guardrails * Memory systems
## What are the different ways partners can contribute to OPEA? There are different ways partners can contribute to this project: | ai_ref_knowledge | OPEA Documentation | * Ingest/Data Processing * Embedding Models/Services * Indexing/Vector/Graph data stores * Retrieval/Ranking * Prompt Engines * Guardrails * Memory systems
## What are the different ways partners can contribute to OPEA? There are different ways partners can contribute to this project: | * Ingest/Data Processing * Embedding Models/Services * Indexing/Vector/Graph data stores * Retrieval/Ranking * Prompt Engines * Guardrails * Memory systems
## What are the different ways partners can contribute to OPEA? There are different ways partners can contribute to this project: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f3f970e7-d2b5-4474-8cab-ff46608a8929 | OPEA Documentation | file://datasets/opea-docs/faq.md | unknown | f72922d4-5e8c-4b60-a4d3-7d46549dc9e7 | 9 | opea-semantic-v1 | af78c733db6dca93 | ## How do I pronounce OPEA? It is pronounced ‘OH-PEA-AY.’
## What initial companies and open-source projects joined OPEA? AnyScale, Cloudera, DataStax, Domino Data Lab, HuggingFace, Intel, KX, MariaDB Foundation, MinIO, Qdrant, Red Hat, SAS, VMware by Broadcom, Yellowbrick Data, Zilliz. | ai_ref_knowledge | OPEA Documentation | ## How do I pronounce OPEA? It is pronounced ‘OH-PEA-AY.’
## What initial companies and open-source projects joined OPEA? AnyScale, Cloudera, DataStax, Domino Data Lab, HuggingFace, Intel, KX, MariaDB Foundation, MinIO, Qdrant, Red Hat, SAS, VMware by Broadcom, Yellowbrick Data, Zilliz. | ## How do I pronounce OPEA? It is pronounced ‘OH-PEA-AY.’
## What initial companies and open-source projects joined OPEA? AnyScale, Cloudera, DataStax, Domino Data Lab, HuggingFace, Intel, KX, MariaDB Foundation, MinIO, Qdrant, Red Hat, SAS, VMware by Broadcom, Yellowbrick Data, Zilliz. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0003befb-bd13-4300-8fea-a3e27e7318cb | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 93 | opea-semantic-v1 | a53967471a8b332f | ##### Features / Functionality
* Functional
* Features – multimodal, Multi-LLM, Multiple embedding model choices, multiple Embedding DBs, context length
* Context Relevance (context precision/recall)
* Groundedness/faithfulness
* Answer Relevance
* Multi-step reasoning
* Task: 3-shot multi-hop REACT agents
* Data... | ai_ref_knowledge | OPEA Documentation | ##### Features / Functionality
* Functional
* Features – multimodal, Multi-LLM, Multiple embedding model choices, multiple Embedding DBs, context length
* Context Relevance (context precision/recall)
* Groundedness/faithfulness
* Answer Relevance
* Multi-step reasoning
* Task: 3-shot multi-hop REACT agents
* Data... | ##### Features / Functionality
* Functional
* Features – multimodal, Multi-LLM, Multiple embedding model choices, multiple Embedding DBs, context length
* Context Relevance (context precision/recall)
* Groundedness/faithfulness
* Answer Relevance
* Multi-step reasoning
* Task: 3-shot multi-hop REACT agents
* Data... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
027b1bbe-db40-4c86-9fac-dcd676af8574 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 106 | opea-semantic-v1 | fe1c8cc73b28bece | 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 | |
04b8bb2b-ed70-4545-83fb-667bd3f05e91 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 48 | opea-semantic-v1 | d828f1eaf94dcf5b | * Human preference * Average accuracy of an E2E * Multi-lingual * Long-context “Needles in Haystack” * Domain specific
The assessments development will be starting with focus on primary use-cases for RAG flow, such as
Open Q&A. It will allow for comparison with common industrial evaluations (see Cohere, GPT-4) | ai_ref_knowledge | OPEA Documentation | * Human preference * Average accuracy of an E2E * Multi-lingual * Long-context “Needles in Haystack” * Domain specific
The assessments development will be starting with focus on primary use-cases for RAG flow, such as
Open Q&A. It will allow for comparison with common industrial evaluations (see Cohere, GPT-4) | * Human preference * Average accuracy of an E2E * Multi-lingual * Long-context “Needles in Haystack” * Domain specific
The assessments development will be starting with focus on primary use-cases for RAG flow, such as
Open Q&A. It will allow for comparison with common industrial evaluations (see Cohere, GPT-4) | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
04f964d5-39d0-4473-a357-7d86c987ae9a | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 4 | opea-semantic-v1 | dd4e835ce533ee9e | transparency), and readiness for enterprise-grade applications. The specifications will also include a set of reference flows and demos that can be easily reproduced and adopted.
 | ai_ref_knowledge | OPEA Documentation | transparency), and readiness for enterprise-grade applications. The specifications will also include a set of reference flows and demos that can be easily reproduced and adopted.
 | transparency), and readiness for enterprise-grade applications. The specifications will also include a set of reference flows and demos that can be easily reproduced and adopted.
 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
07132109-b143-48f8-9f7a-e843490004bd | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 66 | opea-semantic-v1 | da9988cf96cb9b1c | that indicate higher level capabilities of the RAG pipeline. The next level and the highest level of assessments are indicated by text with no color.
 | ai_ref_knowledge | OPEA Documentation | that indicate higher level capabilities of the RAG pipeline. The next level and the highest level of assessments are indicated by text with no color.
 | that indicate higher level capabilities of the RAG pipeline. The next level and the highest level of assessments are indicated by text with no color.
 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0a096fbe-92a4-4b36-8bca-de61dca85740 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 31 | opea-semantic-v1 | e8386e6b492eac12 | ## 3. Framework Components, Architecture and Flow
The 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. | ai_ref_knowledge | OPEA Documentation | ## 3. Framework Components, Architecture and Flow
The 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. | ## 3. Framework Components, Architecture and Flow
The 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. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0a7b150c-dfe5-4552-a196-a8164dde119c | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 85 | opea-semantic-v1 | 18e4f22369698b2c | ### A2: SW Architecture
Support model selection and data 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. | ai_ref_knowledge | OPEA Documentation | ### A2: SW Architecture
Support model selection and data 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. | ### A2: SW Architecture
Support model selection and data 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. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0aec06c3-966a-491c-a85d-47f0317b580e | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 102 | opea-semantic-v1 | 7d35ddcb2ddc8521 | Early example of next level articulation of metrics expected per each major component.
Component Name: Retriever
* 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 | ai_ref_knowledge | OPEA Documentation | Early example of next level articulation of metrics expected per each major component.
Component Name: Retriever
* 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 | Early example of next level articulation of metrics expected per each major component.
Component Name: Retriever
* 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 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0c407d1d-7026-4d5a-bd04-d68414b6a1d9 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 110 | opea-semantic-v1 | a7d15b866b754851 | #### A5.1 Performance Grading
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. | ai_ref_knowledge | OPEA Documentation | #### A5.1 Performance Grading
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. | #### A5.1 Performance Grading
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. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0f40bbde-372a-4405-ad65-0dd1fa119d2f | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 108 | opea-semantic-v1 | 532e8c90c97cb6a2 | PoC, but not production. * L2 – Market – Meets market needs. Can be deployed in production. * L3 – Advanced – Exceeds market needs.
Part of the recommendation is to have a certification (if and when it becomes part of the framework)
process. It is assumed that a system needs to be at least at Level 2 for every aspect t... | ai_ref_knowledge | OPEA Documentation | PoC, but not production. * L2 – Market – Meets market needs. Can be deployed in production. * L3 – Advanced – Exceeds market needs.
Part of the recommendation is to have a certification (if and when it becomes part of the framework)
process. It is assumed that a system needs to be at least at Level 2 for every aspect t... | PoC, but not production. * L2 – Market – Meets market needs. Can be deployed in production. * L3 – Advanced – Exceeds market needs.
Part of the recommendation is to have a certification (if and when it becomes part of the framework)
process. It is assumed that a system needs to be at least at Level 2 for every aspect t... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0f4c8935-4fda-4b48-ad3a-2ab093a7060e | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 60 | opea-semantic-v1 | 80970857d64a5a46 | solution. Naturally, the goal posts of what is defined as L1/L2/L3 need to be updated on regular basis as the industry pushes GenAI State-of-the-Art forward.
 | ai_ref_knowledge | OPEA Documentation | solution. Naturally, the goal posts of what is defined as L1/L2/L3 need to be updated on regular basis as the industry pushes GenAI State-of-the-Art forward.
 | solution. Naturally, the goal posts of what is defined as L1/L2/L3 need to be updated on regular basis as the industry pushes GenAI State-of-the-Art forward.
 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0f8eb555-4136-443d-9d68-7464c118c265 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 89 | opea-semantic-v1 | ed77f544f232ca64 | Evaluating a composite generative AI system requires a view of end-to-end capabilities as well as assessment of individual components.
#### A4.1 End-to-end assessment | ai_ref_knowledge | OPEA Documentation | Evaluating a composite generative AI system requires a view of end-to-end capabilities as well as assessment of individual components.
#### A4.1 End-to-end assessment | Evaluating a composite generative AI system requires a view of end-to-end capabilities as well as assessment of individual components.
#### A4.1 End-to-end assessment | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
113abb19-631c-4a4a-861b-21424e61c54c | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 36 | opea-semantic-v1 | ffbaa1e64318ea28 | and allows easy pluggable and replaceable models and other components. Ability to exchange components is an important factor in the fast progression of the field.
* Providing an environment to experiment with solution variations - e.g. What is
the impact (E2E system performance) when replacing a generic re-ranking
co... | ai_ref_knowledge | OPEA Documentation | and allows easy pluggable and replaceable models and other components. Ability to exchange components is an important factor in the fast progression of the field.
* Providing an environment to experiment with solution variations - e.g. What is
the impact (E2E system performance) when replacing a generic re-ranking
co... | and allows easy pluggable and replaceable models and other components. Ability to exchange components is an important factor in the fast progression of the field.
* Providing an environment to experiment with solution variations - e.g. What is
the impact (E2E system performance) when replacing a generic re-ranking
co... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1465b082-ac1d-4208-8281-0f5e4117a4f6 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 150 | opea-semantic-v1 | 3b95c4519c7888d5 | 
Figure A6-1.2 Xeon + Gaudi2 LLM RAG flow for Chat QnA | ai_ref_knowledge | OPEA Documentation | 
Figure A6-1.2 Xeon + Gaudi2 LLM RAG flow for Chat QnA | 
Figure A6-1.2 Xeon + Gaudi2 LLM RAG flow for Chat QnA | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
174e5c1d-e8c9-40ee-8821-560639cf55e7 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 75 | opea-semantic-v1 | 294aabd6110a90e5 | extended, there will be diverse set of solution providers and variations of HW (Intel, NVIDIA and others) as well as AI models, modules and construction.
## Appendix A – Draft OPEA Specifications | ai_ref_knowledge | OPEA Documentation | extended, there will be diverse set of solution providers and variations of HW (Intel, NVIDIA and others) as well as AI models, modules and construction.
## Appendix A – Draft OPEA Specifications | extended, there will be diverse set of solution providers and variations of HW (Intel, NVIDIA and others) as well as AI models, modules and construction.
## Appendix A – Draft OPEA Specifications | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1835225e-09dd-4774-8e8d-f90a2f5e716b | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 141 | opea-semantic-v1 | 67cf5f47d2741ed1 | being instantiated and how they are connected in the flow. The graphics legend described in Figure 6.1 will be used for all reference flow depictions.
 | ai_ref_knowledge | OPEA Documentation | being instantiated and how they are connected in the flow. The graphics legend described in Figure 6.1 will be used for all reference flow depictions.
 | being instantiated and how they are connected in the flow. The graphics legend described in Figure 6.1 will be used for all reference flow depictions.
 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
187e9483-7a54-438b-a6e8-44519b035050 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 56 | opea-semantic-v1 | d555d4dc11a2c47f | at that time, as well as evaluate some necessary requirements (such as for security and enterprise readiness) for robust deployment of GenAI solutions at scale.
A
grading system establishes a mechanism to evaluate different constructed AI solutions (such as
particular RAG flows) in the context of the OPEA framework. | ai_ref_knowledge | OPEA Documentation | at that time, as well as evaluate some necessary requirements (such as for security and enterprise readiness) for robust deployment of GenAI solutions at scale.
A
grading system establishes a mechanism to evaluate different constructed AI solutions (such as
particular RAG flows) in the context of the OPEA framework. | at that time, as well as evaluate some necessary requirements (such as for security and enterprise readiness) for robust deployment of GenAI solutions at scale.
A
grading system establishes a mechanism to evaluate different constructed AI solutions (such as
particular RAG flows) in the context of the OPEA framework. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
18b868f5-0c98-47b6-9376-f0623a441306 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 10 | opea-semantic-v1 | 0001fdf6bc4c970c | ## 2. Introduction
Recently, the practices for developing AI solutions have undergone significant transformation. Instead of
considering AI model (e.g., a GenAI LLM) as the complete solution, these models are now being
integrated into more comprehensive end-to-end AI solutions. These solutions consist of multiple
compo... | ai_ref_knowledge | OPEA Documentation | ## 2. Introduction
Recently, the practices for developing AI solutions have undergone significant transformation. Instead of
considering AI model (e.g., a GenAI LLM) as the complete solution, these models are now being
integrated into more comprehensive end-to-end AI solutions. These solutions consist of multiple
compo... | ## 2. Introduction
Recently, the practices for developing AI solutions have undergone significant transformation. Instead of
considering AI model (e.g., a GenAI LLM) as the complete solution, these models are now being
integrated into more comprehensive end-to-end AI solutions. These solutions consist of multiple
compo... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1ed7bc86-a5a0-4123-bd3a-50a4078cd389 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 76 | opea-semantic-v1 | b31ca3c9098cca7e | **Rev 0.1 April 15, 2024**
The draft specifications are intended for illustration and discussion purposes. The appendix has six
sections: | ai_ref_knowledge | OPEA Documentation | **Rev 0.1 April 15, 2024**
The draft specifications are intended for illustration and discussion purposes. The appendix has six
sections: | **Rev 0.1 April 15, 2024**
The draft specifications are intended for illustration and discussion purposes. The appendix has six
sections: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1f3896c9-f8a1-4ac0-b6f5-519a435772c0 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 147 | opea-semantic-v1 | d0fd3880d7f88caa | serving on Gaudi2 platform, which is used generating answers by inputting prompts that combine retrieved relevant documents from Redis vector database and the user query.
* An orchestration framework based on LangChain that initializes a pipeline with
the components above and orchestrates the data processing from the ... | ai_ref_knowledge | OPEA Documentation | serving on Gaudi2 platform, which is used generating answers by inputting prompts that combine retrieved relevant documents from Redis vector database and the user query.
* An orchestration framework based on LangChain that initializes a pipeline with
the components above and orchestrates the data processing from the ... | serving on Gaudi2 platform, which is used generating answers by inputting prompts that combine retrieved relevant documents from Redis vector database and the user query.
* An orchestration framework based on LangChain that initializes a pipeline with
the components above and orchestrates the data processing from the ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
20007947-1325-478f-8aa6-a281ca7337e4 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 115 | opea-semantic-v1 | ac7eba4ee21f4c5b | #### A5.2 Features Grading
Feature grading consists of running functional tests to test system capabilities in a number of different
domains. Each domain will have its own score. | ai_ref_knowledge | OPEA Documentation | #### A5.2 Features Grading
Feature grading consists of running functional tests to test system capabilities in a number of different
domains. Each domain will have its own score. | #### A5.2 Features Grading
Feature grading consists of running functional tests to test system capabilities in a number of different
domains. Each domain will have its own score. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
207e2f81-40b4-4bee-8f19-bb465eec51ff | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 2 | opea-semantic-v1 | fee94c110b394706 | 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 is an ecosystem-wide program within the Linux Foundation Data & AI framework that aims to
accelerate enterprise adoption of GenAI end-to-end ... | ai_ref_knowledge | OPEA Documentation | 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 is an ecosystem-wide program within the Linux Foundation Data & AI framework that aims to
accelerate enterprise adoption of GenAI end-to-end ... | 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 is an ecosystem-wide program within the Linux Foundation Data & AI framework that aims to
accelerate enterprise adoption of GenAI end-to-end ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
22840307-3ce2-48cb-ba75-08da575595da | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 9 | opea-semantic-v1 | 4550539ef5993972 | to leverage decades of insights into enterprise-scale computing, security, trustworthiness, and datacenter integration, among other areas, to accelerate AI adoption and unlock its potential value.
## 2. Introduction | ai_ref_knowledge | OPEA Documentation | to leverage decades of insights into enterprise-scale computing, security, trustworthiness, and datacenter integration, among other areas, to accelerate AI adoption and unlock its potential value.
## 2. Introduction | to leverage decades of insights into enterprise-scale computing, security, trustworthiness, and datacenter integration, among other areas, to accelerate AI adoption and unlock its potential value.
## 2. Introduction | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
22d12249-1e7d-4854-888d-9009338222ec | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 112 | opea-semantic-v1 | 43729c98da7a1421 | different input magnitude size * Metrics * First-token latency, overall latency, throughput, cost, consistency * Formula to aggregate metrics for final score * Vertical-specific metrics
##### Performance Grade
Performance grade is based on a set of ‘black box’ end-to-end RAG benchmarks, based on real use
cases. Each so... | ai_ref_knowledge | OPEA Documentation | different input magnitude size * Metrics * First-token latency, overall latency, throughput, cost, consistency * Formula to aggregate metrics for final score * Vertical-specific metrics
##### Performance Grade
Performance grade is based on a set of ‘black box’ end-to-end RAG benchmarks, based on real use
cases. Each so... | different input magnitude size * Metrics * First-token latency, overall latency, throughput, cost, consistency * Formula to aggregate metrics for final score * Vertical-specific metrics
##### Performance Grade
Performance grade is based on a set of ‘black box’ end-to-end RAG benchmarks, based on real use
cases. Each so... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
23c9a7a7-7ce3-46c6-8e6c-34c129203f4c | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 133 | opea-semantic-v1 | 1f24a158168275e5 | ##### Enterprise Readiness Grade
Must first meet mins across performance, features, and trustworthiness
* Level 1 – Reference Design and deployment guide
* Level 2 - Output ready for enterprise deployment (no post-OPEA steps needed);
containerized, K8 support; generally robust (but not guaranteed) for production
depl... | ai_ref_knowledge | OPEA Documentation | ##### Enterprise Readiness Grade
Must first meet mins across performance, features, and trustworthiness
* Level 1 – Reference Design and deployment guide
* Level 2 - Output ready for enterprise deployment (no post-OPEA steps needed);
containerized, K8 support; generally robust (but not guaranteed) for production
depl... | ##### Enterprise Readiness Grade
Must first meet mins across performance, features, and trustworthiness
* Level 1 – Reference Design and deployment guide
* Level 2 - Output ready for enterprise deployment (no post-OPEA steps needed);
containerized, K8 support; generally robust (but not guaranteed) for production
depl... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
25a4d3b5-1b6b-45a4-998c-cf6055c3d5d6 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 153 | opea-semantic-v1 | e0d08ab0b1310dfc | 
Figure A6-1.3 Xeon + Gaudi2 LLM RAG flow for Chat QnA – demo screen | ai_ref_knowledge | OPEA Documentation | 
Figure A6-1.3 Xeon + Gaudi2 LLM RAG flow for Chat QnA – demo screen | 
Figure A6-1.3 Xeon + Gaudi2 LLM RAG flow for Chat QnA – demo screen | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
25e32d56-5337-4521-8227-ae916122b840 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 43 | opea-semantic-v1 | c002114a8a341f07 | MMLU). As for functionality, there are benchmarks and datasets available to evaluate particular target functionality such as multi-lingual (like FLORES) or code generations (e.g., Human-Eval).
For evaluating trustworthiness/Hallucination safety the spec will leverage existing benchmarks such
as RGB benchmark/Truthful Q... | ai_ref_knowledge | OPEA Documentation | MMLU). As for functionality, there are benchmarks and datasets available to evaluate particular target functionality such as multi-lingual (like FLORES) or code generations (e.g., Human-Eval).
For evaluating trustworthiness/Hallucination safety the spec will leverage existing benchmarks such
as RGB benchmark/Truthful Q... | MMLU). As for functionality, there are benchmarks and datasets available to evaluate particular target functionality such as multi-lingual (like FLORES) or code generations (e.g., Human-Eval).
For evaluating trustworthiness/Hallucination safety the spec will leverage existing benchmarks such
as RGB benchmark/Truthful Q... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
268871ca-2fd1-4791-8814-6ad47a4e61a1 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 94 | opea-semantic-v1 | 481aa5a16138c89b | * Human reference on enterprise RAG use cases * Domains: Customer support, Workplace support (Tech), Workplace Assistant (Media), Tech FAQ * Metric: Win ratio vs.
Mixtral | ai_ref_knowledge | OPEA Documentation | * Human reference on enterprise RAG use cases * Domains: Customer support, Workplace support (Tech), Workplace Assistant (Media), Tech FAQ * Metric: Win ratio vs.
Mixtral | * Human reference on enterprise RAG use cases * Domains: Customer support, Workplace support (Tech), Workplace Assistant (Media), Tech FAQ * Metric: Win ratio vs.
Mixtral | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
2900c993-9dcd-4a5b-9007-c69794893dc1 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 0 | opea-semantic-v1 | 297db331d46c0edf | Rev 0.5 April 15, 2024
Initial draft by Intel. Contacts for content – Ke Ding (ke.ding@intel.com ), Gadi Singer
(gadi.singer@intel.com) | ai_ref_knowledge | OPEA Documentation | Rev 0.5 April 15, 2024
Initial draft by Intel. Contacts for content – Ke Ding (ke.ding@intel.com ), Gadi Singer
(gadi.singer@intel.com) | Rev 0.5 April 15, 2024
Initial draft by Intel. Contacts for content – Ke Ding (ke.ding@intel.com ), Gadi Singer
(gadi.singer@intel.com) | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
291ff9f8-65cf-4a9a-a19e-2c8171fc3bd8 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 68 | opea-semantic-v1 | c98b19d3334aca7d | documentation will provide links to the required components (which may come from multiple providers) and the necessary script and other software required to run them.
Several flows will exclusively focus on open models and other components, providing full transparency
when necessary. Other flows may include proprietary... | ai_ref_knowledge | OPEA Documentation | documentation will provide links to the required components (which may come from multiple providers) and the necessary script and other software required to run them.
Several flows will exclusively focus on open models and other components, providing full transparency
when necessary. Other flows may include proprietary... | documentation will provide links to the required components (which may come from multiple providers) and the necessary script and other software required to run them.
Several flows will exclusively focus on open models and other components, providing full transparency
when necessary. Other flows may include proprietary... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
295a3d08-c028-4c6a-97df-4f1ab26a46ef | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 72 | opea-semantic-v1 | 01572e0ee81958bc | Allow users in the ecosystem to experiment with and innovate with a broad set of flows and maximize the value for their end-to-end use cases.
OPEA will deploy and evolve a visualization language to capture the blueprint flows (e.g., a base flow for
RAG chat/Q&A) as well as to document the choices made for every referen... | ai_ref_knowledge | OPEA Documentation | Allow users in the ecosystem to experiment with and innovate with a broad set of flows and maximize the value for their end-to-end use cases.
OPEA will deploy and evolve a visualization language to capture the blueprint flows (e.g., a base flow for
RAG chat/Q&A) as well as to document the choices made for every referen... | Allow users in the ecosystem to experiment with and innovate with a broad set of flows and maximize the value for their end-to-end use cases.
OPEA will deploy and evolve a visualization language to capture the blueprint flows (e.g., a base flow for
RAG chat/Q&A) as well as to document the choices made for every referen... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
2961baab-b4fb-48e8-8864-57c1df33f0df | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 5 | opea-semantic-v1 | ee079d3fc6daa4a2 | Figure 1-1: OPEA’s Core Values
\*Disclaimer – The term ‘specification’ is used throughout this draft whitepaper and appendix as a broad
working term, referring generally to a detailed description of systems and their components. However, it
is important to note that this term might be replaced or updated based on more ... | ai_ref_knowledge | OPEA Documentation | Figure 1-1: OPEA’s Core Values
\*Disclaimer – The term ‘specification’ is used throughout this draft whitepaper and appendix as a broad
working term, referring generally to a detailed description of systems and their components. However, it
is important to note that this term might be replaced or updated based on more ... | Figure 1-1: OPEA’s Core Values
\*Disclaimer – The term ‘specification’ is used throughout this draft whitepaper and appendix as a broad
working term, referring generally to a detailed description of systems and their components. However, it
is important to note that this term might be replaced or updated based on more ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
30e11caf-d754-424f-b1de-9c0225231be7 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 164 | opea-semantic-v1 | e6aeec1651bee044 | #### A6.3 – Optimized Text and Multimodal RAG pipeline
The reference flow below demonstrates an optimized Text and Multimodal RAG pipeline which can be
leveraged by Enterprise customers on Intel Xeon processor. | ai_ref_knowledge | OPEA Documentation | #### A6.3 – Optimized Text and Multimodal RAG pipeline
The reference flow below demonstrates an optimized Text and Multimodal RAG pipeline which can be
leveraged by Enterprise customers on Intel Xeon processor. | #### A6.3 – Optimized Text and Multimodal RAG pipeline
The reference flow below demonstrates an optimized Text and Multimodal RAG pipeline which can be
leveraged by Enterprise customers on Intel Xeon processor. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
30f074f1-b8ec-4f0d-ab7b-4bbbe3f0f82d | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 45 | opea-semantic-v1 | 97075b8ef67dd510 | steps required for broad deployment). One of the measures that could be assessed in this category is overall Cost/TCO of a full end-to-end GenAI flow.
When aspects of composite GenAI solutions are not freely available, reliable benchmarks or tests,
efforts will be made to ensure creation of such. As many of the current... | ai_ref_knowledge | OPEA Documentation | steps required for broad deployment). One of the measures that could be assessed in this category is overall Cost/TCO of a full end-to-end GenAI flow.
When aspects of composite GenAI solutions are not freely available, reliable benchmarks or tests,
efforts will be made to ensure creation of such. As many of the current... | steps required for broad deployment). One of the measures that could be assessed in this category is overall Cost/TCO of a full end-to-end GenAI flow.
When aspects of composite GenAI solutions are not freely available, reliable benchmarks or tests,
efforts will be made to ensure creation of such. As many of the current... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
328d91f7-7a4c-4638-a861-99a754522ac7 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 47 | opea-semantic-v1 | e369314ba1a1a58e | assessments should use learnings from similar evaluations when available. For example, referring to RAG evaluation as reported by Cohere’s Nils Reimers. See more details here:
* Human preference
* Average accuracy of an E2E
* Multi-lingual
* Long-context “Needles in Haystack”
* Domain specific | ai_ref_knowledge | OPEA Documentation | assessments should use learnings from similar evaluations when available. For example, referring to RAG evaluation as reported by Cohere’s Nils Reimers. See more details here:
* Human preference
* Average accuracy of an E2E
* Multi-lingual
* Long-context “Needles in Haystack”
* Domain specific | assessments should use learnings from similar evaluations when available. For example, referring to RAG evaluation as reported by Cohere’s Nils Reimers. See more details here:
* Human preference
* Average accuracy of an E2E
* Multi-lingual
* Long-context “Needles in Haystack”
* Domain specific | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
35460e5a-3e4d-4fca-838a-027745e8ae07 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 88 | opea-semantic-v1 | 1b29365a77abdc01 | ### A4: Select Specifications
Evaluating a composite generative AI system requires a view of end-to-end capabilities as well as assessment of individual components. | ai_ref_knowledge | OPEA Documentation | ### A4: Select Specifications
Evaluating a composite generative AI system requires a view of end-to-end capabilities as well as assessment of individual components. | ### A4: Select Specifications
Evaluating a composite generative AI system requires a view of end-to-end capabilities as well as assessment of individual components. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
388bb377-03a3-42e8-8e1b-aa1f1341ed01 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 101 | opea-semantic-v1 | 9a3f189092c24a0c | – quality/latency/context length/reasoning ability/function calling/tool usage * Auto evaluation vs Manul evaluation * Observability * Guardrails - * Prompting * Output Generation – structured/grammar/output types(json/text)
Early example of next level articulation of metrics expected per each major component. | ai_ref_knowledge | OPEA Documentation | – quality/latency/context length/reasoning ability/function calling/tool usage * Auto evaluation vs Manul evaluation * Observability * Guardrails - * Prompting * Output Generation – structured/grammar/output types(json/text)
Early example of next level articulation of metrics expected per each major component. | – quality/latency/context length/reasoning ability/function calling/tool usage * Auto evaluation vs Manul evaluation * Observability * Guardrails - * Prompting * Output Generation – structured/grammar/output types(json/text)
Early example of next level articulation of metrics expected per each major component. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
38c3fea4-a82d-4d19-bcf1-16bd0138a218 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 73 | opea-semantic-v1 | 414370e0c20b0760 | (e.g., sequence of functions or containerization) (see Figure 6-2) as well as the implementation choices for particular model and modules (See Appendix A section A6).
 | ai_ref_knowledge | OPEA Documentation | (e.g., sequence of functions or containerization) (see Figure 6-2) as well as the implementation choices for particular model and modules (See Appendix A section A6).
 | (e.g., sequence of functions or containerization) (see Figure 6-2) as well as the implementation choices for particular model and modules (See Appendix A section A6).
 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
3c5ba462-d711-4294-bd97-f61dff4b7ce4 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 17 | opea-semantic-v1 | 7ba24bfd551ef450 | of OPEA repository, or published in stable open repository (e.g., Hugging Face), or proprietary / closed source and cleared for use by an OPEA assessment.
* GenAI models – Large Language Models (LLMs), Large Vision Models (LVMs), multimodal models, etc. * Other modules - AI system components (other than LLM/LVM models)... | ai_ref_knowledge | OPEA Documentation | of OPEA repository, or published in stable open repository (e.g., Hugging Face), or proprietary / closed source and cleared for use by an OPEA assessment.
* GenAI models – Large Language Models (LLMs), Large Vision Models (LVMs), multimodal models, etc. * Other modules - AI system components (other than LLM/LVM models)... | of OPEA repository, or published in stable open repository (e.g., Hugging Face), or proprietary / closed source and cleared for use by an OPEA assessment.
* GenAI models – Large Language Models (LLMs), Large Vision Models (LVMs), multimodal models, etc. * Other modules - AI system components (other than LLM/LVM models)... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
3dc3c895-2104-4031-a916-c07d37afd4dd | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 62 | opea-semantic-v1 | a3693150f2054073 | Figure 5-1 Overall view of the grading system across four domains
The grading system can play a different role for the providers of models, building blocks (modules), and
complete end-to-end GenAI solutions. Providers can get structured and impartial feedback on the
strengths and weaknesses of their offering compared w... | ai_ref_knowledge | OPEA Documentation | Figure 5-1 Overall view of the grading system across four domains
The grading system can play a different role for the providers of models, building blocks (modules), and
complete end-to-end GenAI solutions. Providers can get structured and impartial feedback on the
strengths and weaknesses of their offering compared w... | Figure 5-1 Overall view of the grading system across four domains
The grading system can play a different role for the providers of models, building blocks (modules), and
complete end-to-end GenAI solutions. Providers can get structured and impartial feedback on the
strengths and weaknesses of their offering compared w... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
3fc5c74a-eacd-42db-a698-6b0bfabcae4f | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 24 | opea-semantic-v1 | 86a40ebacd1ac955 | trustworthiness and enterprise-readiness. The evaluation can be done on a flow created within OPEA, or created elsewhere but requesting to be assessed through the platform.
Some of the evaluation tools will be part of the OPEA repository, while others will be references to
selected benchmarks offered by the ecosystem. | ai_ref_knowledge | OPEA Documentation | trustworthiness and enterprise-readiness. The evaluation can be done on a flow created within OPEA, or created elsewhere but requesting to be assessed through the platform.
Some of the evaluation tools will be part of the OPEA repository, while others will be references to
selected benchmarks offered by the ecosystem. | trustworthiness and enterprise-readiness. The evaluation can be done on a flow created within OPEA, or created elsewhere but requesting to be assessed through the platform.
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, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
40446a43-7c97-4718-9053-3bbc09441d20 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 109 | opea-semantic-v1 | a50c288d826af4d6 | of the framework) process. It is assumed that a system needs to be at least at Level 2 for every aspect to be “OPEA Certified”.
#### A5.1 Performance Grading | ai_ref_knowledge | OPEA Documentation | of the framework) process. It is assumed that a system needs to be at least at Level 2 for every aspect to be “OPEA Certified”.
#### A5.1 Performance Grading | of the framework) process. It is assumed that a system needs to be at least at Level 2 for every aspect to be “OPEA Certified”.
#### A5.1 Performance Grading | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
41d2f280-4a43-4052-9285-93f10bf26ec8 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 114 | opea-semantic-v1 | a00dcffdc3fe254f | solutions performing similar benchmarks/tasks. * Level 3 – Exceeds the performance of most solutions being evaluated at that time. Top-tier solutions per the tasks evaluated.
#### A5.2 Features Grading | ai_ref_knowledge | OPEA Documentation | solutions performing similar benchmarks/tasks. * Level 3 – Exceeds the performance of most solutions being evaluated at that time. Top-tier solutions per the tasks evaluated.
#### A5.2 Features Grading | solutions performing similar benchmarks/tasks. * Level 3 – Exceeds the performance of most solutions being evaluated at that time. Top-tier solutions per the tasks evaluated.
#### A5.2 Features Grading | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4405e0d5-7677-4694-9b26-fdbcba7b2df4 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 90 | opea-semantic-v1 | 05a30c5c5c8dadfd | #### A4.1 End-to-end assessment
Following are some examples of assessments addressing the four domains - performance, features, trustworthiness and enterprise readiness. | ai_ref_knowledge | OPEA Documentation | #### A4.1 End-to-end assessment
Following are some examples of assessments addressing the four domains - performance, features, trustworthiness and enterprise readiness. | #### A4.1 End-to-end assessment
Following are some examples of assessments addressing the four domains - performance, features, trustworthiness and enterprise readiness. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
44a55d29-53a3-4af7-89b7-32296af108e1 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 40 | opea-semantic-v1 | 9d27edca30cac4e9 | accuracy per defined suite of tests. Assessments are covered in this section. Grading is an aggregation of assessments and is covered in the next section.
Components and entire end-to-end flows will be evaluated in four domains – performance, features,
trustworthiness and enterprise-readiness. | ai_ref_knowledge | OPEA Documentation | accuracy per defined suite of tests. Assessments are covered in this section. Grading is an aggregation of assessments and is covered in the next section.
Components and entire end-to-end flows will be evaluated in four domains – performance, features,
trustworthiness and enterprise-readiness. | accuracy per defined suite of tests. Assessments are covered in this section. Grading is an aggregation of assessments and is covered in the next section.
Components and entire end-to-end flows will be evaluated in four domains – performance, features,
trustworthiness and enterprise-readiness. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
470e638b-bc4a-46d5-bad8-315bc566c5e1 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 91 | opea-semantic-v1 | 4f42665547619242 | Following are some examples of assessments addressing the four domains - performance, features, trustworthiness and enterprise readiness.
##### Performance
* Overall System Performance
* Latency (first token latency, average token latency, streaming vs non-streaming output)
* Throughput
* Given a fixed combination o... | ai_ref_knowledge | OPEA Documentation | Following are some examples of assessments addressing the four domains - performance, features, trustworthiness and enterprise readiness.
##### Performance
* Overall System Performance
* Latency (first token latency, average token latency, streaming vs non-streaming output)
* Throughput
* Given a fixed combination o... | Following are some examples of assessments addressing the four domains - performance, features, trustworthiness and enterprise readiness.
##### Performance
* Overall System Performance
* Latency (first token latency, average token latency, streaming vs non-streaming output)
* Throughput
* Given a fixed combination o... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4793a8e0-474f-495a-8af6-938d9b837ea0 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 160 | opea-semantic-v1 | b121ecbadca81f59 | Figure A6-2.1 Multimodal Chat Over Images and Videos Reference Flow
Below is an illustration of a user interface constructed for this reference flow, which was showcased at
Intel Vision: | ai_ref_knowledge | OPEA Documentation | Figure A6-2.1 Multimodal Chat Over Images and Videos Reference Flow
Below is an illustration of a user interface constructed for this reference flow, which was showcased at
Intel Vision: | Figure A6-2.1 Multimodal Chat Over Images and Videos Reference Flow
Below is an illustration of a user interface constructed for this reference flow, which was showcased at
Intel Vision: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4817016c-65cf-4ab3-ad4a-2b2b3a56231c | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 39 | opea-semantic-v1 | c1d2fb7f689900e3 | ## 4. Assessing GenAI components and flows
One of the important benefits to the ecosystem from the development and broad use of OPEA is a
structured set of evaluation that can provide trusted feedback on GenAI flows – whether composed
within OPEA, or composed elsewhere but has the visibility and access that allows for ... | ai_ref_knowledge | OPEA Documentation | ## 4. Assessing GenAI components and flows
One of the important benefits to the ecosystem from the development and broad use of OPEA is a
structured set of evaluation that can provide trusted feedback on GenAI flows – whether composed
within OPEA, or composed elsewhere but has the visibility and access that allows for ... | ## 4. Assessing GenAI components and flows
One of the important benefits to the ecosystem from the development and broad use of OPEA is a
structured set of evaluation that can provide trusted feedback on GenAI flows – whether composed
within OPEA, or composed elsewhere but has the visibility and access that allows for ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
48d40519-6ce4-4fff-a559-f1fbd771462e | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 123 | opea-semantic-v1 | 967d82bb08370f52 | in generating responses, protecting from hallucinations. One of the chief benefits of RAG. * Meeting regulatory requirements such as ISO27001, HIPAA, and FedRAMP as appropriate.
* Security:
* Role-based access control, segmented access per user-role regardless of same model use. This could be a pre or post processing ... | ai_ref_knowledge | OPEA Documentation | in generating responses, protecting from hallucinations. One of the chief benefits of RAG. * Meeting regulatory requirements such as ISO27001, HIPAA, and FedRAMP as appropriate.
* Security:
* Role-based access control, segmented access per user-role regardless of same model use. This could be a pre or post processing ... | in generating responses, protecting from hallucinations. One of the chief benefits of RAG. * Meeting regulatory requirements such as ISO27001, HIPAA, and FedRAMP as appropriate.
* Security:
* Role-based access control, segmented access per user-role regardless of same model use. This could be a pre or post processing ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
49def824-aa06-462e-a103-ca527b251a30 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 136 | opea-semantic-v1 | 6b9f12ba26b87b6c | This section includes descriptions of reference flows that will be available for loading and reproducing with minimal effort.
Reference flows serve four primary objectives: | ai_ref_knowledge | OPEA Documentation | This section includes descriptions of reference flows that will be available for loading and reproducing with minimal effort.
Reference flows serve four primary objectives: | This section includes descriptions of reference flows that will be available for loading and reproducing with minimal effort.
Reference flows serve four primary objectives: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4b11e69c-5c77-4279-9fcc-644ef515a3c7 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 77 | opea-semantic-v1 | 09d39d4543edffc7 | The draft specifications are intended for illustration and discussion purposes. The appendix has six sections:
* A1: System Components - List of ingredients that comprise a composed system,
along with their key characteristics. * A2: SW architecture - Diagram providing the layering of components in a SW stack
* A3: Sy... | ai_ref_knowledge | OPEA Documentation | The draft specifications are intended for illustration and discussion purposes. The appendix has six sections:
* A1: System Components - List of ingredients that comprise a composed system,
along with their key characteristics. * A2: SW architecture - Diagram providing the layering of components in a SW stack
* A3: Sy... | The draft specifications are intended for illustration and discussion purposes. The appendix has six sections:
* A1: System Components - List of ingredients that comprise a composed system,
along with their key characteristics. * A2: SW architecture - Diagram providing the layering of components in a SW stack
* A3: Sy... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4c3bd976-8d69-4323-8bf1-083fe40e315a | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 54 | opea-semantic-v1 | ab8d10e076d94846 | and robustness. This will take into account relevant government or other policies. * Enterprise Readiness – Ability to be used in production in enterprise environments.
The Performance and Features capabilities are well understood by the communities and industry today,
while Trustworthiness and Enterprise Readiness are... | ai_ref_knowledge | OPEA Documentation | and robustness. This will take into account relevant government or other policies. * Enterprise Readiness – Ability to be used in production in enterprise environments.
The Performance and Features capabilities are well understood by the communities and industry today,
while Trustworthiness and Enterprise Readiness are... | and robustness. This will take into account relevant government or other policies. * Enterprise Readiness – Ability to be used in production in enterprise environments.
The Performance and Features capabilities are well understood by the communities and industry today,
while Trustworthiness and Enterprise Readiness are... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4dcde18f-d092-42e4-8fce-fb0d3ad25ac7 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 49 | opea-semantic-v1 | a6968c94eebfdec1 | starting with focus on primary use-cases for RAG flow, such as Open Q&A. It will allow for comparison with common industrial evaluations (see Cohere, GPT-4)
## 5. Grading Structure | ai_ref_knowledge | OPEA Documentation | starting with focus on primary use-cases for RAG flow, such as Open Q&A. It will allow for comparison with common industrial evaluations (see Cohere, GPT-4)
## 5. Grading Structure | starting with focus on primary use-cases for RAG flow, such as Open Q&A. It will allow for comparison with common industrial evaluations (see Cohere, GPT-4)
## 5. Grading Structure | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4f3535e5-991b-43e8-baa3-522e6510ecaf | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 161 | opea-semantic-v1 | e532aa7703b63b36 | Below is an illustration of a user interface constructed for this reference flow, which was showcased at Intel Vision:
 | ai_ref_knowledge | OPEA Documentation | Below is an illustration of a user interface constructed for this reference flow, which was showcased at Intel Vision:
 | Below is an illustration of a user interface constructed for this reference flow, which was showcased at Intel Vision:
 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
50f7b803-e40e-40cc-98b5-306c7c3ff810 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 35 | opea-semantic-v1 | 74c80958a9779184 | Assumptions for the development of OPEA sections include:
* OPEA is a blueprint for composition frameworks and is not set to compete with
the popular frameworks. It is set to help assess the pros and cons of various
solutions and allow for improved interoperability of components. * In production, it is likely that ma... | ai_ref_knowledge | OPEA Documentation | Assumptions for the development of OPEA sections include:
* OPEA is a blueprint for composition frameworks and is not set to compete with
the popular frameworks. It is set to help assess the pros and cons of various
solutions and allow for improved interoperability of components. * In production, it is likely that ma... | Assumptions for the development of OPEA sections include:
* OPEA is a blueprint for composition frameworks and is not set to compete with
the popular frameworks. It is set to help assess the pros and cons of various
solutions and allow for improved interoperability of components. * In production, it is likely that ma... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
51f6e57e-799d-4e30-8a03-f0c5b546e7b7 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 148 | opea-semantic-v1 | 264977d36e1dee2a | that initializes a pipeline with the components above and orchestrates the data processing from the user (query), text encoding, retrieval, prompt generation and LLM inference.
A complete reference implementation of this flow is available in the ChatQnA example in Intel’s GenAI
examples repository. | ai_ref_knowledge | OPEA Documentation | that initializes a pipeline with the components above and orchestrates the data processing from the user (query), text encoding, retrieval, prompt generation and LLM inference.
A complete reference implementation of this flow is available in the ChatQnA example in Intel’s GenAI
examples repository. | that initializes a pipeline with the components above and orchestrates the data processing from the user (query), text encoding, retrieval, prompt generation and LLM inference.
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 | |
5346bc01-90d3-47b5-985e-72e9ccf1b07d | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 167 | opea-semantic-v1 | 8de40d5d68f5f2f6 | for the vector database (i.e. Qdrant in this case). For unstructured text data, sentence-transformers is used. For images, BridgeTower is used to encode the inputs.
Once the vector database 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-ch... | ai_ref_knowledge | OPEA Documentation | for the vector database (i.e. Qdrant in this case). For unstructured text data, sentence-transformers is used. For images, BridgeTower is used to encode the inputs.
Once the vector database 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-ch... | for the vector database (i.e. Qdrant in this case). For unstructured text data, sentence-transformers is used. For images, BridgeTower is used to encode the inputs.
Once the vector database 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-ch... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5496236b-0eaf-45b6-941d-48a0e9d390c9 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 140 | opea-semantic-v1 | c80d60c71bdab4ce | provided for illustration purposes. The set of reference flows is expected to grow and cover various combinations of HW and SW/AI components from multiple providers.
The reference flow descriptions need to provide high clarity as to what and how they can be recreated
and results reproduced at an OPEA user setting. All ... | ai_ref_knowledge | OPEA Documentation | provided for illustration purposes. The set of reference flows is expected to grow and cover various combinations of HW and SW/AI components from multiple providers.
The reference flow descriptions need to provide high clarity as to what and how they can be recreated
and results reproduced at an OPEA user setting. All ... | provided for illustration purposes. The set of reference flows is expected to grow and cover various combinations of HW and SW/AI components from multiple providers.
The reference flow descriptions need to provide high clarity as to what and how they can be recreated
and results reproduced at an OPEA user setting. All ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
556453c7-303d-4368-baaa-ad77ed74007f | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 143 | opea-semantic-v1 | 0c696dd65d52e068 | #### A6.1 – Xeon + Gaudi2 LLM RAG flow for Chat QnA
A reference flow that illustrates an LLM 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. | ai_ref_knowledge | OPEA Documentation | #### A6.1 – Xeon + Gaudi2 LLM RAG flow for Chat QnA
A reference flow that illustrates an LLM 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. | #### A6.1 – Xeon + Gaudi2 LLM RAG flow for Chat QnA
A reference flow that illustrates an LLM 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. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
573464f5-e649-484b-a15f-8aa51653afcc | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 3 | opea-semantic-v1 | fe63c15f47950a76 | Generative AI (RAG). The platform is designed to facilitate efficient integration of secure, performant, and cost-effective GenAI workflows into business systems and manage its deployments.
This platform’s definition will include an architectural blueprint, a comprehensive set of components for
GenAI systems, and a sui... | ai_ref_knowledge | OPEA Documentation | Generative AI (RAG). The platform is designed to facilitate efficient integration of secure, performant, and cost-effective GenAI workflows into business systems and manage its deployments.
This platform’s definition will include an architectural blueprint, a comprehensive set of components for
GenAI systems, and a sui... | Generative AI (RAG). The platform is designed to facilitate efficient integration of secure, performant, and cost-effective GenAI workflows into business systems and manage its deployments.
This platform’s definition will include an architectural blueprint, a comprehensive set of components for
GenAI systems, and a sui... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
58146bdf-10a9-4c88-8a27-5c63f16af68d | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 74 | opea-semantic-v1 | 3469d7e26c608c69 | Figure 6-2 Example of blueprint RAG flow
The Reference flows section of the specification (Section A6 in Appendix A) provides an initial catalog of
reference flows, demonstrating common tasks and diverse combinations of hardware and AI
components. As this collection of reference flows is extended, there will be diverse... | ai_ref_knowledge | OPEA Documentation | Figure 6-2 Example of blueprint RAG flow
The Reference flows section of the specification (Section A6 in Appendix A) provides an initial catalog of
reference flows, demonstrating common tasks and diverse combinations of hardware and AI
components. As this collection of reference flows is extended, there will be diverse... | Figure 6-2 Example of blueprint RAG flow
The Reference flows section of the specification (Section A6 in Appendix A) provides an initial catalog of
reference flows, demonstrating common tasks and diverse combinations of hardware and AI
components. As this collection of reference flows is extended, there will be diverse... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
59d15e23-0254-4c66-aa8d-80e44d4b1d2a | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 11 | opea-semantic-v1 | 7fdb371e98dda77b | LangChain or Haystack), which are used to assemble these components into end-to-end GenAI flows, like RAG solutions, for the development and deployment of AI solutions.
The ecosystem offers a range of composition frameworks, some are open-source (e.g., LangChain and
LlamaIndex), while others are closed-sourced and come... | ai_ref_knowledge | OPEA Documentation | LangChain or Haystack), which are used to assemble these components into end-to-end GenAI flows, like RAG solutions, for the development and deployment of AI solutions.
The ecosystem offers a range of composition frameworks, some are open-source (e.g., LangChain and
LlamaIndex), while others are closed-sourced and come... | LangChain or Haystack), which are used to assemble these components into end-to-end GenAI flows, like RAG solutions, for the development and deployment of AI solutions.
The ecosystem offers a range of composition frameworks, some are open-source (e.g., LangChain and
LlamaIndex), while others are closed-sourced and come... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5a6487ee-a3ed-49ce-b9a1-fef633fedcb5 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 171 | opea-semantic-v1 | 4552aa481589a4cb | 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 | 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.
 | 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 | |
5b385b24-dcf9-43e4-85ca-01cc44dd335a | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 157 | opea-semantic-v1 | ca0fd33e4e7266a4 | Detailed instructions and documentation for this model are available via Hugging Face. The multimodal embeddings are then indexed and stored in a Redis vector database.
At inference time, a user’s query is embedded by BridgeTower and used to retrieve the most relevant
images & videos from the vector database. The retri... | ai_ref_knowledge | OPEA Documentation | Detailed instructions and documentation for this model are available via Hugging Face. The multimodal embeddings are then indexed and stored in a Redis vector database.
At inference time, a user’s query is embedded by BridgeTower and used to retrieve the most relevant
images & videos from the vector database. The retri... | Detailed instructions and documentation for this model are available via Hugging Face. The multimodal embeddings are then indexed and stored in a Redis vector database.
At inference time, a user’s query is embedded by BridgeTower and used to retrieve the most relevant
images & videos from the vector database. The retri... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6030fda9-b430-4f76-9ed5-f0c8398b01bb | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 78 | opea-semantic-v1 | 77bca41844afaf33 | 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.
This is an early draft of OPEA framework specification. It provides an initial view of the content and is
expected to be substantially expanded in future revisions. | 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.
This is an early draft of OPEA framework specification. It provides an initial view of the content and is
expected to be substantially expanded in future revisions. | 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.
This is an early draft of OPEA framework specification. It provides an initial view of the content and is
expected to be substantially expanded in future revisions. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
60de8449-0d8a-4c93-957c-6098346bdca2 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 55 | opea-semantic-v1 | 354c2d6342ddd7a7 | assessment and evaluation when it comes to GenAI solutions. Nevertheless, all domains are essential to ensure performant, secure, privacy-aware, robust solutions ready for broad deployment.
The grading system is not intended to add any particular tests or benchmarks. All individual tests are to
be part of the assessmen... | ai_ref_knowledge | OPEA Documentation | assessment and evaluation when it comes to GenAI solutions. Nevertheless, all domains are essential to ensure performant, secure, privacy-aware, robust solutions ready for broad deployment.
The grading system is not intended to add any particular tests or benchmarks. All individual tests are to
be part of the assessmen... | assessment and evaluation when it comes to GenAI solutions. Nevertheless, all domains are essential to ensure performant, secure, privacy-aware, robust solutions ready for broad deployment.
The grading system is not intended to add any particular tests or benchmarks. All individual tests are to
be part of the assessmen... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
61be7b2f-d7dc-45f8-b905-8bdefe5cf182 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 23 | opea-semantic-v1 | 7d65eb1281866bfa | #### 2.1.2 Evaluation of GenAI solutions, including retrieval augmentation:
OPEA will provide means and services to fully evaluate and grade components and end-to-end GenAI
solutions across four domains – performance, functionality, trustworthiness and enterprise-readiness. The evaluation can be done on a flow created ... | ai_ref_knowledge | OPEA Documentation | #### 2.1.2 Evaluation of GenAI solutions, including retrieval augmentation:
OPEA will provide means and services to fully evaluate and grade components and end-to-end GenAI
solutions across four domains – performance, functionality, trustworthiness and enterprise-readiness. The evaluation can be done on a flow created ... | #### 2.1.2 Evaluation of GenAI solutions, including retrieval augmentation:
OPEA will provide means and services to fully evaluate and grade components and end-to-end GenAI
solutions across four domains – performance, functionality, trustworthiness and enterprise-readiness. The evaluation can be done on a flow created ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6220df02-b380-495e-ade9-67608be0d98d | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 158 | opea-semantic-v1 | c26fe30ac419a2d9 | to the user’s query and passed to LLaVA to generate an answer. Detailed instructions and documentation for the LLaVA model are available via Hugging Face.
This reference flow requires Intel Gaudi AI Accelerators for the embedding model and for generating
responses with the LVLM. All other components of the reference fl... | ai_ref_knowledge | OPEA Documentation | to the user’s query and passed to LLaVA to generate an answer. Detailed instructions and documentation for the LLaVA model are available via Hugging Face.
This reference flow requires Intel Gaudi AI Accelerators for the embedding model and for generating
responses with the LVLM. All other components of the reference fl... | to the user’s query and passed to LLaVA to generate an answer. Detailed instructions and documentation for the LLaVA model are available via Hugging Face.
This reference flow requires Intel Gaudi AI Accelerators for the embedding model and for generating
responses with the LVLM. All other components of the reference fl... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
629167f7-6769-457d-af77-6462cf9ba0a1 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 132 | opea-semantic-v1 | 61fa2f14d4bcfb17 | for enterprise 24/7 support * Licensing model and SW Distribution * Scalable from small to large customers * Ability to customize for specific enterprise needs
##### Enterprise Readiness Grade | ai_ref_knowledge | OPEA Documentation | for enterprise 24/7 support * Licensing model and SW Distribution * Scalable from small to large customers * Ability to customize for specific enterprise needs
##### Enterprise Readiness Grade | for enterprise 24/7 support * Licensing model and SW Distribution * Scalable from small to large customers * Ability to customize for specific enterprise needs
##### Enterprise Readiness Grade | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6333d4cf-2064-4945-a232-fe3adf345f56 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 96 | opea-semantic-v1 | a567a751d3225dd5 | value of K in top-K documents) * Maximum context length supported by the generator * Parameter size of the generator models * Embedding dimension size
Production deploy-ability Readiness includes various capabilities such as
* Efficient inference serving
* Integrations with different enterprise systems such as Slack/wo... | ai_ref_knowledge | OPEA Documentation | value of K in top-K documents) * Maximum context length supported by the generator * Parameter size of the generator models * Embedding dimension size
Production deploy-ability Readiness includes various capabilities such as
* Efficient inference serving
* Integrations with different enterprise systems such as Slack/wo... | value of K in top-K documents) * Maximum context length supported by the generator * Parameter size of the generator models * Embedding dimension size
Production deploy-ability Readiness includes various capabilities such as
* Efficient inference serving
* Integrations with different enterprise systems such as Slack/wo... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6a402a96-21d8-4480-b912-94ad90dd375c | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 104 | opea-semantic-v1 | 83ce272039332327 | between 0 and 1) * Metric: Answer Relevance – how relevant generated answer to the query (computed as a ragas metrics between 0 and 1)
### A5: Grading | ai_ref_knowledge | OPEA Documentation | between 0 and 1) * Metric: Answer Relevance – how relevant generated answer to the query (computed as a ragas metrics between 0 and 1)
### A5: Grading | between 0 and 1) * Metric: Answer Relevance – how relevant generated answer to the query (computed as a ragas metrics between 0 and 1)
### A5: Grading | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6b51e8d5-d475-44ee-855e-8b52929f0d4a | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 69 | opea-semantic-v1 | 62b15b20ce0f3605 | referred to in a reference flow must be accessible to OPEA users – whether they are open source or proprietary, free to use or fee-based.
Reference Flows serve several primary objectives: | ai_ref_knowledge | OPEA Documentation | referred to in a reference flow must be accessible to OPEA users – whether they are open source or proprietary, free to use or fee-based.
Reference Flows serve several primary objectives: | referred to in a reference flow must be accessible to OPEA users – whether they are open source or proprietary, free to use or fee-based.
Reference Flows serve several primary objectives: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6c9e3af9-3fa3-468c-9c63-45ecef1b780b | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 98 | opea-semantic-v1 | c4e74bc64fd0bd33 | Updatability includes capability for * Rolling upgrade * Online upgrade * Component level upgrade
Observability/Debuggability includes capability for
* Error detection and attribution to component
* Early detection of component degradation
* Trace generation to debug failures (functional and performance)
* Traceability... | ai_ref_knowledge | OPEA Documentation | Updatability includes capability for * Rolling upgrade * Online upgrade * Component level upgrade
Observability/Debuggability includes capability for
* Error detection and attribution to component
* Early detection of component degradation
* Trace generation to debug failures (functional and performance)
* Traceability... | Updatability includes capability for * Rolling upgrade * Online upgrade * Component level upgrade
Observability/Debuggability includes capability for
* Error detection and attribution to component
* Early detection of component degradation
* Trace generation to debug failures (functional and performance)
* Traceability... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6d4935da-4496-4ebe-a4f8-87ae79ee4076 | OPEA Documentation | file://datasets/opea-docs/framework/framework.md | unknown | 75243589-c7f7-4e1f-850d-b2559f987b0f | 16 | opea-semantic-v1 | 58a8d5fb5571f12f | Composing an end-to-end AI solution (including retrieval augmentation) can be done by combining models and modules from multiple providers.
OPEA will offer or refer to a set of building blocks – models and modules – that can be called in a flow to
achieve an AI task or service. The models and modules can be part of OPE... | ai_ref_knowledge | OPEA Documentation | Composing an end-to-end AI solution (including retrieval augmentation) can be done by combining models and modules from multiple providers.
OPEA will offer or refer to a set of building blocks – models and modules – that can be called in a flow to
achieve an AI task or service. The models and modules can be part of OPE... | Composing an end-to-end AI solution (including retrieval augmentation) can be done by combining models and modules from multiple providers.
OPEA will offer or refer to a set of building blocks – models and modules – that can be called in a flow to
achieve an AI task or service. The models and modules can be part of OPE... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation |
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