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5df74402-30c0-440f-9a6e-f784e425ec7b | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/DocIndexRetriever_Guide.rst | unknown | bde3c268-bdd8-402f-9dbc-f02ca1bd0cbb | 4 | opea-semantic-v1 | cbe717b684064676 | Key Implementation Details **************************
User Interface:
The interface that interactivates with users, gets inputs from users and
serves responses to users. DocIndexRetriever GateWay:
The agent that maintains the connections between user-end and service-end,
forwards requests and responses to appropr... | ai_ref_knowledge | OPEA Documentation | Key Implementation Details **************************
User Interface:
The interface that interactivates with users, gets inputs from users and
serves responses to users. DocIndexRetriever GateWay:
The agent that maintains the connections between user-end and service-end,
forwards requests and responses to appropr... | Key Implementation Details **************************
User Interface:
The interface that interactivates with users, gets inputs from users and
serves responses to users. DocIndexRetriever GateWay:
The agent that maintains the connections between user-end and service-end,
forwards requests and responses to appropr... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6f077288-7c89-424c-8e4e-54e1c240bd38 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/DocIndexRetriever_Guide.rst | unknown | bde3c268-bdd8-402f-9dbc-f02ca1bd0cbb | 14 | opea-semantic-v1 | 96d480b40e0114bf | %% Embedding service flow direction LR EM <-.-> TEI_EM RET <-.-> R_RET RER <-.-> TEI_RER
direction TB
%% Vector DB interaction
R_RET <-.-> VDB
DP <-.-> VDB | ai_ref_knowledge | OPEA Documentation | %% Embedding service flow direction LR EM <-.-> TEI_EM RET <-.-> R_RET RER <-.-> TEI_RER
direction TB
%% Vector DB interaction
R_RET <-.-> VDB
DP <-.-> VDB | %% Embedding service flow direction LR EM <-.-> TEI_EM RET <-.-> R_RET RER <-.-> TEI_RER
direction TB
%% Vector DB interaction
R_RET <-.-> VDB
DP <-.-> VDB | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
703e64de-e00e-4fbd-aca6-760a5c1d0ac7 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/DocIndexRetriever_Guide.rst | unknown | bde3c268-bdd8-402f-9dbc-f02ca1bd0cbb | 15 | opea-semantic-v1 | 7112206a48b271a0 | direction TB %% Vector DB interaction R_RET <-.-> VDB DP <-.-> VDB
This diagram illustrates the flow of information in the DocIndexRetriever system. Firstly, the user provides docments to the system, which are ingested by the
Data Preparation MicroService. The Data Preparation MicroService prepares the data
for the vec... | ai_ref_knowledge | OPEA Documentation | direction TB %% Vector DB interaction R_RET <-.-> VDB DP <-.-> VDB
This diagram illustrates the flow of information in the DocIndexRetriever system. Firstly, the user provides docments to the system, which are ingested by the
Data Preparation MicroService. The Data Preparation MicroService prepares the data
for the vec... | direction TB %% Vector DB interaction R_RET <-.-> VDB DP <-.-> VDB
This diagram illustrates the flow of information in the DocIndexRetriever system. Firstly, the user provides docments to the system, which are ingested by the
Data Preparation MicroService. The Data Preparation MicroService prepares the data
for the vec... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
773ea62b-1ef7-4269-8e68-ee9ed74a1f99 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/DocIndexRetriever_Guide.rst | unknown | bde3c268-bdd8-402f-9dbc-f02ca1bd0cbb | 11 | opea-semantic-v1 | 07c55008fec8bf49 | DP([Data Preparation MicroService]):::blue TEI_RER{{Reranking service<br>}} TEI_EM{{Embedding service <br>}} VDB{{Vector DB<br><br>}} R_RET{{Retriever service <br>}} GW([DocIndexRetriever GateWay<br>]):::orange
%% Data Preparation flow
%% Ingest data flow
direction LR
Ingest[Ingest data] --> DP
DP <-.-> TEI_EM | ai_ref_knowledge | OPEA Documentation | DP([Data Preparation MicroService]):::blue TEI_RER{{Reranking service<br>}} TEI_EM{{Embedding service <br>}} VDB{{Vector DB<br><br>}} R_RET{{Retriever service <br>}} GW([DocIndexRetriever GateWay<br>]):::orange
%% Data Preparation flow
%% Ingest data flow
direction LR
Ingest[Ingest data] --> DP
DP <-.-> TEI_EM | DP([Data Preparation MicroService]):::blue TEI_RER{{Reranking service<br>}} TEI_EM{{Embedding service <br>}} VDB{{Vector DB<br><br>}} R_RET{{Retriever service <br>}} GW([DocIndexRetriever GateWay<br>]):::orange
%% Data Preparation flow
%% Ingest data flow
direction LR
Ingest[Ingest data] --> DP
DP <-.-> TEI_EM | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a6386a15-736d-484b-8a50-08dc47acfdf6 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/DocIndexRetriever_Guide.rst | unknown | bde3c268-bdd8-402f-9dbc-f02ca1bd0cbb | 5 | opea-semantic-v1 | 6fd3cc4f83776728 | vector database and reranks relevant documents to select the most related documents. Data Preparation MicroService: The component that prepares the data for the vector database.
How It Works
************ | ai_ref_knowledge | OPEA Documentation | vector database and reranks relevant documents to select the most related documents. Data Preparation MicroService: The component that prepares the data for the vector database.
How It Works
************ | vector database and reranks relevant documents to select the most related documents. Data Preparation MicroService: The component that prepares the data for the vector database.
How It Works
************ | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a9390e93-2458-4e97-bc7d-463434d65fb7 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/DocIndexRetriever_Guide.rst | unknown | bde3c268-bdd8-402f-9dbc-f02ca1bd0cbb | 1 | opea-semantic-v1 | bb85285328b58907 | retrieval of information. These databases store data as vectors, allowing RAG to swiftly access the most pertinent documents or data points based on semantic similarity.
Purpose
******* | ai_ref_knowledge | OPEA Documentation | retrieval of information. These databases store data as vectors, allowing RAG to swiftly access the most pertinent documents or data points based on semantic similarity.
Purpose
******* | retrieval of information. These databases store data as vectors, allowing RAG to swiftly access the most pertinent documents or data points based on semantic similarity.
Purpose
******* | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b7a98c00-a57a-489e-ad8a-f66062a09c81 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/DocIndexRetriever_Guide.rst | unknown | bde3c268-bdd8-402f-9dbc-f02ca1bd0cbb | 8 | opea-semantic-v1 | 5f7149495fa77183 | config: flowchart: nodeSpacing: 400 rankSpacing: 100 curve: linear themeVariables: fontSize: 50px
flowchart LR
%% Colors %%
classDef blue fill:#ADD8E6,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5
classDef orange fill:#FBAA60,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5
classDef orchid fill:#C26DBC,stroke:#A... | ai_ref_knowledge | OPEA Documentation | config: flowchart: nodeSpacing: 400 rankSpacing: 100 curve: linear themeVariables: fontSize: 50px
flowchart LR
%% Colors %%
classDef blue fill:#ADD8E6,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5
classDef orange fill:#FBAA60,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5
classDef orchid fill:#C26DBC,stroke:#A... | config: flowchart: nodeSpacing: 400 rankSpacing: 100 curve: linear themeVariables: fontSize: 50px
flowchart LR
%% Colors %%
classDef blue fill:#ADD8E6,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5
classDef orange fill:#FBAA60,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5
classDef orchid fill:#C26DBC,stroke:#A... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b8f8a534-5e25-42a4-9d71-fa6b3d7f14aa | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/DocIndexRetriever_Guide.rst | unknown | bde3c268-bdd8-402f-9dbc-f02ca1bd0cbb | 16 | opea-semantic-v1 | acc289eac6569737 | representation. The Retrieval MicroService retrieves relevant documents from the vector database, and the Rerank MicroService reranks the relevant documents to select the most related documents.
The reranked documents are then sent back to
the DocIndexRetriever Gateway, which forwards the documents to the user. | ai_ref_knowledge | OPEA Documentation | representation. The Retrieval MicroService retrieves relevant documents from the vector database, and the Rerank MicroService reranks the relevant documents to select the most related documents.
The reranked documents are then sent back to
the DocIndexRetriever Gateway, which forwards the documents to the user. | representation. The Retrieval MicroService retrieves relevant documents from the vector database, and the Rerank MicroService reranks the relevant documents to select the most related documents.
The reranked documents are then sent back to
the DocIndexRetriever Gateway, which forwards the documents to the user. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b9816580-7d69-49db-8ad4-8b635c1abaa2 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/DocIndexRetriever_Guide.rst | unknown | bde3c268-bdd8-402f-9dbc-f02ca1bd0cbb | 7 | opea-semantic-v1 | c170c7f9fff642c2 | DocIndexRetriever example is implemented using the component-level microservices defined in [GenAIComps](https://github.com/opea-project/GenAIComps). The flow chart below shows the information flow between different microservices for this example.
.. mermaid:: | ai_ref_knowledge | OPEA Documentation | DocIndexRetriever example is implemented using the component-level microservices defined in [GenAIComps](https://github.com/opea-project/GenAIComps). The flow chart below shows the information flow between different microservices for this example.
.. mermaid:: | DocIndexRetriever example is implemented using the component-level microservices defined in [GenAIComps](https://github.com/opea-project/GenAIComps). The flow chart below shows the information flow between different microservices for this example.
.. mermaid:: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d5c8ab3a-caab-4b64-99d6-000740290b2f | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/DocIndexRetriever_Guide.rst | unknown | bde3c268-bdd8-402f-9dbc-f02ca1bd0cbb | 6 | opea-semantic-v1 | a3eea4a58251db3e | How It Works ************
The DocIndexRetriever example is implemented using the component-level microservices
defined in [GenAIComps](https://github.com/opea-project/GenAIComps). The flow chart
below shows the information flow between different microservices for this example. | ai_ref_knowledge | OPEA Documentation | How It Works ************
The DocIndexRetriever example is implemented using the component-level microservices
defined in [GenAIComps](https://github.com/opea-project/GenAIComps). The flow chart
below shows the information flow between different microservices for this example. | How It Works ************
The DocIndexRetriever example is implemented using the component-level microservices
defined in [GenAIComps](https://github.com/opea-project/GenAIComps). The flow chart
below shows the information flow between different microservices for this example. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e0f72d30-ef2b-47c9-9df4-4abe8c69b61d | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/DocIndexRetriever_Guide.rst | unknown | bde3c268-bdd8-402f-9dbc-f02ca1bd0cbb | 2 | opea-semantic-v1 | 6131eb69053e3c73 | Purpose *******
* **Enable document retrieval with LLMs**: DocIndexRetriever is designed to
facilitate the retrieval of documents or information from a large corpus of
text data using Large Language Models (LLMs). | ai_ref_knowledge | OPEA Documentation | Purpose *******
* **Enable document retrieval with LLMs**: DocIndexRetriever is designed to
facilitate the retrieval of documents or information from a large corpus of
text data using Large Language Models (LLMs). | Purpose *******
* **Enable document retrieval with LLMs**: DocIndexRetriever is designed to
facilitate the retrieval of documents or information from a large corpus of
text data using Large Language Models (LLMs). | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ebb607ca-93a1-4c65-959e-7066a2c68fd4 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/DocIndexRetriever_Guide.rst | unknown | bde3c268-bdd8-402f-9dbc-f02ca1bd0cbb | 3 | opea-semantic-v1 | af1b978e9b6cd180 | with LLMs**: DocIndexRetriever is designed to facilitate the retrieval of documents or information from a large corpus of text data using Large Language Models (LLMs).
Key Implementation Details
************************** | ai_ref_knowledge | OPEA Documentation | with LLMs**: DocIndexRetriever is designed to facilitate the retrieval of documents or information from a large corpus of text data using Large Language Models (LLMs).
Key Implementation Details
************************** | with LLMs**: DocIndexRetriever is designed to facilitate the retrieval of documents or information from a large corpus of text data using Large Language Models (LLMs).
Key Implementation Details
************************** | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
fd9fd263-ef24-4414-a441-0cbc4c9ced1e | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/DocIndexRetriever_Guide.rst | unknown | bde3c268-bdd8-402f-9dbc-f02ca1bd0cbb | 17 | opea-semantic-v1 | 9f593aa270a84d6e | The reranked documents are then sent back to the DocIndexRetriever Gateway, which forwards the documents to the user.
The architecture follows a series of steps to process user queries and generate
responses: | ai_ref_knowledge | OPEA Documentation | The reranked documents are then sent back to the DocIndexRetriever Gateway, which forwards the documents to the user.
The architecture follows a series of steps to process user queries and generate
responses: | The reranked documents are then sent back to the DocIndexRetriever Gateway, which forwards the documents to the user.
The architecture follows a series of steps to process user queries and generate
responses: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ffd41b4e-0206-4962-b612-362619a7ed61 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/DocIndexRetriever_Guide.rst | unknown | bde3c268-bdd8-402f-9dbc-f02ca1bd0cbb | 12 | opea-semantic-v1 | ee291da01167a683 | %% Data Preparation flow %% Ingest data flow direction LR Ingest[Ingest data] --> DP DP <-.-> TEI_EM
%% Questions interaction
direction LR
a[User Input Query] --> GW
GW <==> DocIndexRetriever-MegaService
EM ==> RET
RET ==> RER | ai_ref_knowledge | OPEA Documentation | %% Data Preparation flow %% Ingest data flow direction LR Ingest[Ingest data] --> DP DP <-.-> TEI_EM
%% Questions interaction
direction LR
a[User Input Query] --> GW
GW <==> DocIndexRetriever-MegaService
EM ==> RET
RET ==> RER | %% Data Preparation flow %% Ingest data flow direction LR Ingest[Ingest data] --> DP DP <-.-> TEI_EM
%% Questions interaction
direction LR
a[User Input Query] --> GW
GW <==> DocIndexRetriever-MegaService
EM ==> RET
RET ==> RER | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1785ea97-3f9c-4dbb-8762-f0cb40f3ce97 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/DocSum_Guide.rst | unknown | 3c5cdbb2-6eb5-4b80-973e-de7fd9062c70 | 0 | opea-semantic-v1 | baa32cd8d5e7667c | Overview ********
The DocSum example is designed to process diverse content types including text documents, spoken language (audio), and visual media (video) to generate concise summaries that capture the essence of the original material. This pipeline integrates ASR (automatic speech recognition) with an LLM to summar... | ai_ref_knowledge | OPEA Documentation | Overview ********
The DocSum example is designed to process diverse content types including text documents, spoken language (audio), and visual media (video) to generate concise summaries that capture the essence of the original material. This pipeline integrates ASR (automatic speech recognition) with an LLM to summar... | Overview ********
The DocSum example is designed to process diverse content types including text documents, spoken language (audio), and visual media (video) to generate concise summaries that capture the essence of the original material. This pipeline integrates ASR (automatic speech recognition) with an LLM to summar... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
313f5a42-3ca6-4f63-b785-1f22ebaef5b3 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/DocSum_Guide.rst | unknown | 3c5cdbb2-6eb5-4b80-973e-de7fd9062c70 | 2 | opea-semantic-v1 | f8f4c16327a34777 | or automated reports, streamlining content workflows. * Legal and Compliance: Extracts key clauses and obligations from contracts or guidelines, ensuring compliance while reducing review effort.
How It Works
************ | ai_ref_knowledge | OPEA Documentation | or automated reports, streamlining content workflows. * Legal and Compliance: Extracts key clauses and obligations from contracts or guidelines, ensuring compliance while reducing review effort.
How It Works
************ | or automated reports, streamlining content workflows. * Legal and Compliance: Extracts key clauses and obligations from contracts or guidelines, ensuring compliance while reducing review effort.
How It Works
************ | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
430bf7ff-79c5-45e0-8165-ddc0cf2c864f | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/DocSum_Guide.rst | unknown | 3c5cdbb2-6eb5-4b80-973e-de7fd9062c70 | 1 | opea-semantic-v1 | 5400a147c034ab17 | content. This example can be used to create summaries of news articles, research papers, technical documents, legal documents, multimedia documents, and other types of documents.
Purpose
*******
* Quick Content Understanding: Saves time by providing concise overviews of lengthy documents, enabling users to focus on ess... | ai_ref_knowledge | OPEA Documentation | content. This example can be used to create summaries of news articles, research papers, technical documents, legal documents, multimedia documents, and other types of documents.
Purpose
*******
* Quick Content Understanding: Saves time by providing concise overviews of lengthy documents, enabling users to focus on ess... | content. This example can be used to create summaries of news articles, research papers, technical documents, legal documents, multimedia documents, and other types of documents.
Purpose
*******
* Quick Content Understanding: Saves time by providing concise overviews of lengthy documents, enabling users to focus on ess... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
595f0450-f7e2-48d8-8a6c-f9c18e28581d | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/DocSum_Guide.rst | unknown | 3c5cdbb2-6eb5-4b80-973e-de7fd9062c70 | 3 | opea-semantic-v1 | e6e870e316360bee | How It Works ************
The Docsum example uses an open-source model served using a framework such as Text Generation Inference (TGI) or vLLM to construct a summary
of the input provided. It can process textual, audio, and video input from a variety of sources as shown in the diagram below. | ai_ref_knowledge | OPEA Documentation | How It Works ************
The Docsum example uses an open-source model served using a framework such as Text Generation Inference (TGI) or vLLM to construct a summary
of the input provided. It can process textual, audio, and video input from a variety of sources as shown in the diagram below. | How It Works ************
The Docsum example uses an open-source model served using a framework such as Text Generation Inference (TGI) or vLLM to construct a summary
of the input provided. It can process textual, audio, and video input from a variety of sources as shown in the diagram below. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
bfbebfd2-67e2-49d2-909f-54b41e4ea825 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/DocSum_Guide.rst | unknown | 3c5cdbb2-6eb5-4b80-973e-de7fd9062c70 | 4 | opea-semantic-v1 | 62bbd6a93db7d79c | a summary of the input provided. It can process textual, audio, and video input from a variety of sources as shown in the diagram below.
.. figure:: /GenAIExamples/DocSum/assets/img/docsum_architecture.png | ai_ref_knowledge | OPEA Documentation | a summary of the input provided. It can process textual, audio, and video input from a variety of sources as shown in the diagram below.
.. figure:: /GenAIExamples/DocSum/assets/img/docsum_architecture.png | a summary of the input provided. It can process textual, audio, and video input from a variety of sources as shown in the diagram below.
.. figure:: /GenAIExamples/DocSum/assets/img/docsum_architecture.png | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0400041c-3bb7-4e2b-ad4d-efa058c5c1eb | OPEA Documentation | file://datasets/opea-docs/tutorial/EnterpriseInference/EnterpriseInference_Guide.rst | unknown | f3e567f4-f6b0-40e0-8447-a17ccfc91dfb | 14 | opea-semantic-v1 | caf4564268299f80 | This can be an on-premises machine or from a cloud service provider. All options for deployment can be found `here <https://www.intel.com/content/www/us/en/products/docs/accelerator-engines/enterprise-ai.html>`_.
Note down the HTTPS endpoint and generate an access token or API key. The HTTPS endpoint may look something... | ai_ref_knowledge | OPEA Documentation | This can be an on-premises machine or from a cloud service provider. All options for deployment can be found `here <https://www.intel.com/content/www/us/en/products/docs/accelerator-engines/enterprise-ai.html>`_.
Note down the HTTPS endpoint and generate an access token or API key. The HTTPS endpoint may look something... | This can be an on-premises machine or from a cloud service provider. All options for deployment can be found `here <https://www.intel.com/content/www/us/en/products/docs/accelerator-engines/enterprise-ai.html>`_.
Note down the HTTPS endpoint and generate an access token or API key. The HTTPS endpoint may look something... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
272d0f23-496f-4187-b95f-3aca57455244 | OPEA Documentation | file://datasets/opea-docs/tutorial/EnterpriseInference/EnterpriseInference_Guide.rst | unknown | f3e567f4-f6b0-40e0-8447-a17ccfc91dfb | 27 | opea-semantic-v1 | 3eedb44933940475 | docker compose -f compose_openai.yaml -f compose_remote.yaml up -d
Next Steps
**********
Go back to the GenAIExample's tutorial or README to access the UI and interact with it. The LLM text generation is now handled on a remote server, reducing the burden and resources used on the hardware platform hosting the applicat... | ai_ref_knowledge | OPEA Documentation | docker compose -f compose_openai.yaml -f compose_remote.yaml up -d
Next Steps
**********
Go back to the GenAIExample's tutorial or README to access the UI and interact with it. The LLM text generation is now handled on a remote server, reducing the burden and resources used on the hardware platform hosting the applicat... | docker compose -f compose_openai.yaml -f compose_remote.yaml up -d
Next Steps
**********
Go back to the GenAIExample's tutorial or README to access the UI and interact with it. The LLM text generation is now handled on a remote server, reducing the burden and resources used on the hardware platform hosting the applicat... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
29706e6f-c0b8-4b25-8fce-d257cdc843b3 | OPEA Documentation | file://datasets/opea-docs/tutorial/EnterpriseInference/EnterpriseInference_Guide.rst | unknown | f3e567f4-f6b0-40e0-8447-a17ccfc91dfb | 17 | opea-semantic-v1 | 352b371c459bb50d | need to be specified. LiteLLM is not used by default. - `API_KEY` is the access token or key to access the model(s) on the server.
export REMOTE_ENDPOINT=<your-http-endpoint>
export API_KEY=<your-api-key> | ai_ref_knowledge | OPEA Documentation | need to be specified. LiteLLM is not used by default. - `API_KEY` is the access token or key to access the model(s) on the server.
export REMOTE_ENDPOINT=<your-http-endpoint>
export API_KEY=<your-api-key> | need to be specified. LiteLLM is not used by default. - `API_KEY` is the access token or key to access the model(s) on the server.
export REMOTE_ENDPOINT=<your-http-endpoint>
export API_KEY=<your-api-key> | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
34483876-4d55-4041-b254-725d709599e2 | OPEA Documentation | file://datasets/opea-docs/tutorial/EnterpriseInference/EnterpriseInference_Guide.rst | unknown | f3e567f4-f6b0-40e0-8447-a17ccfc91dfb | 15 | opea-semantic-v1 | 69c483d7b7877a55 | The HTTPS endpoint may look something like this: https://api.example.com. This access token will be used as the API key to securely access the deployed models.
Using Remote Endpoints on OPEA GenAIExamples
********************************************
OPEA GenAIExamples by default will download and deploy the models on t... | ai_ref_knowledge | OPEA Documentation | The HTTPS endpoint may look something like this: https://api.example.com. This access token will be used as the API key to securely access the deployed models.
Using Remote Endpoints on OPEA GenAIExamples
********************************************
OPEA GenAIExamples by default will download and deploy the models on t... | The HTTPS endpoint may look something like this: https://api.example.com. This access token will be used as the API key to securely access the deployed models.
Using Remote Endpoints on OPEA GenAIExamples
********************************************
OPEA GenAIExamples by default will download and deploy the models on t... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
361f49b5-35a2-4151-8774-e53baa337dae | OPEA Documentation | file://datasets/opea-docs/tutorial/EnterpriseInference/EnterpriseInference_Guide.rst | unknown | f3e567f4-f6b0-40e0-8447-a17ccfc91dfb | 3 | opea-semantic-v1 | bd0c4ab10a23734f | deploying AI models with ease. With this suite, enterprises can efficiently configure and evolve their AI infrastructure, adapting to new models and growing demands effortlessly.
How It Works
************
With a single click, Intel® AI for Enterprise Inference leverages several key components to streamline AI inference... | ai_ref_knowledge | OPEA Documentation | deploying AI models with ease. With this suite, enterprises can efficiently configure and evolve their AI infrastructure, adapting to new models and growing demands effortlessly.
How It Works
************
With a single click, Intel® AI for Enterprise Inference leverages several key components to streamline AI inference... | deploying AI models with ease. With this suite, enterprises can efficiently configure and evolve their AI infrastructure, adapting to new models and growing demands effortlessly.
How It Works
************
With a single click, Intel® AI for Enterprise Inference leverages several key components to streamline AI inference... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
3e307657-e2be-41d6-823a-942f9cdc28c1 | OPEA Documentation | file://datasets/opea-docs/tutorial/EnterpriseInference/EnterpriseInference_Guide.rst | unknown | f3e567f4-f6b0-40e0-8447-a17ccfc91dfb | 18 | opea-semantic-v1 | 1646e91ff7a12932 | export REMOTE_ENDPOINT=<your-http-endpoint> export API_KEY=<your-api-key>
Depending on the GenAIExample, the next steps may be different. For each section, see the list for the GenAIExample of interest. | ai_ref_knowledge | OPEA Documentation | export REMOTE_ENDPOINT=<your-http-endpoint> export API_KEY=<your-api-key>
Depending on the GenAIExample, the next steps may be different. For each section, see the list for the GenAIExample of interest. | export REMOTE_ENDPOINT=<your-http-endpoint> export API_KEY=<your-api-key>
Depending on the GenAIExample, the next steps may be different. For each section, see the list for the GenAIExample of interest. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4257aad0-3279-4f79-9587-5def4777fe47 | OPEA Documentation | file://datasets/opea-docs/tutorial/EnterpriseInference/EnterpriseInference_Guide.rst | unknown | f3e567f4-f6b0-40e0-8447-a17ccfc91dfb | 26 | opea-semantic-v1 | 7b993ff0daffa7ed | Run *docker compose* for the example by **appending** the *compose_remote.yaml* file to the original command:
docker compose -f compose_openai.yaml -f compose_remote.yaml up -d | ai_ref_knowledge | OPEA Documentation | Run *docker compose* for the example by **appending** the *compose_remote.yaml* file to the original command:
docker compose -f compose_openai.yaml -f compose_remote.yaml up -d | Run *docker compose* for the example by **appending** the *compose_remote.yaml* file to the original command:
docker compose -f compose_openai.yaml -f compose_remote.yaml up -d | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4a528569-d59c-4a72-a47f-fa322a819086 | OPEA Documentation | file://datasets/opea-docs/tutorial/EnterpriseInference/EnterpriseInference_Guide.rst | unknown | f3e567f4-f6b0-40e0-8447-a17ccfc91dfb | 0 | opea-semantic-v1 | c2c913f0099e9798 | Enterprise Inference Guide ##########################
Overview
********
`Intel® AI for Enterprise Inference <https://www.intel.com/content/www/us/en/products/docs/accelerator-engines/enterprise-ai.html>`_ is aimed to streamline and enhance the deployment and management of AI inference services on Intel hardware. Utiliz... | ai_ref_knowledge | OPEA Documentation | Enterprise Inference Guide ##########################
Overview
********
`Intel® AI for Enterprise Inference <https://www.intel.com/content/www/us/en/products/docs/accelerator-engines/enterprise-ai.html>`_ is aimed to streamline and enhance the deployment and management of AI inference services on Intel hardware. Utiliz... | Enterprise Inference Guide ##########################
Overview
********
`Intel® AI for Enterprise Inference <https://www.intel.com/content/www/us/en/products/docs/accelerator-engines/enterprise-ai.html>`_ is aimed to streamline and enhance the deployment and management of AI inference services on Intel hardware. Utiliz... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4b752765-9d8e-4552-9256-81eed2874901 | OPEA Documentation | file://datasets/opea-docs/tutorial/EnterpriseInference/EnterpriseInference_Guide.rst | unknown | f3e567f4-f6b0-40e0-8447-a17ccfc91dfb | 19 | opea-semantic-v1 | 59ec39f7315634ed | Depending on the GenAIExample, the next steps may be different. For each section, see the list for the GenAIExample of interest.
1. Endpoints with Megaservices
++++++++++++++++++++++++++++++
This section applies to the following GenAIExamples:
- AudioQnA
- ChatQnA
- CodeGen
- CodeTrans
- DocSum
- Productivity Sui... | ai_ref_knowledge | OPEA Documentation | Depending on the GenAIExample, the next steps may be different. For each section, see the list for the GenAIExample of interest.
1. Endpoints with Megaservices
++++++++++++++++++++++++++++++
This section applies to the following GenAIExamples:
- AudioQnA
- ChatQnA
- CodeGen
- CodeTrans
- DocSum
- Productivity Sui... | Depending on the GenAIExample, the next steps may be different. For each section, see the list for the GenAIExample of interest.
1. Endpoints with Megaservices
++++++++++++++++++++++++++++++
This section applies to the following GenAIExamples:
- AudioQnA
- ChatQnA
- CodeGen
- CodeTrans
- DocSum
- Productivity Sui... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4bae6c8d-4224-43ea-a38b-c30c8eed1eec | OPEA Documentation | file://datasets/opea-docs/tutorial/EnterpriseInference/EnterpriseInference_Guide.rst | unknown | f3e567f4-f6b0-40e0-8447-a17ccfc91dfb | 1 | opea-semantic-v1 | 4f2feee518ba72db | the deployment of LLM models to run faster inference, provision compute resources, and configure the optimal settings to minimize the complexities and reduce manual efforts.
It supports a broad range of Intel hardware platforms, including Intel® Xeon® Scalable processors and Intel® Gaudi® AI Accelerators, ensuring flex... | ai_ref_knowledge | OPEA Documentation | the deployment of LLM models to run faster inference, provision compute resources, and configure the optimal settings to minimize the complexities and reduce manual efforts.
It supports a broad range of Intel hardware platforms, including Intel® Xeon® Scalable processors and Intel® Gaudi® AI Accelerators, ensuring flex... | the deployment of LLM models to run faster inference, provision compute resources, and configure the optimal settings to minimize the complexities and reduce manual efforts.
It supports a broad range of Intel hardware platforms, including Intel® Xeon® Scalable processors and Intel® Gaudi® AI Accelerators, ensuring flex... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5b085d90-a6a4-4bf9-843c-0f6375981114 | OPEA Documentation | file://datasets/opea-docs/tutorial/EnterpriseInference/EnterpriseInference_Guide.rst | unknown | f3e567f4-f6b0-40e0-8447-a17ccfc91dfb | 12 | opea-semantic-v1 | 2f387d0d9e9249a3 | using an OpenAI API with an https endpoint. This endpoint and API key are all that is needed to do inference with these deployed models.
Setting Up a Remote Server or Cluster
*************************************
The first step is to get access to the hardware platform of choice:
- Intel® Gaudi® AI Accelerators
- Int... | ai_ref_knowledge | OPEA Documentation | using an OpenAI API with an https endpoint. This endpoint and API key are all that is needed to do inference with these deployed models.
Setting Up a Remote Server or Cluster
*************************************
The first step is to get access to the hardware platform of choice:
- Intel® Gaudi® AI Accelerators
- Int... | using an OpenAI API with an https endpoint. This endpoint and API key are all that is needed to do inference with these deployed models.
Setting Up a Remote Server or Cluster
*************************************
The first step is to get access to the hardware platform of choice:
- Intel® Gaudi® AI Accelerators
- Int... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5d594aef-9588-4033-9545-a73bf3e54cd7 | OPEA Documentation | file://datasets/opea-docs/tutorial/EnterpriseInference/EnterpriseInference_Guide.rst | unknown | f3e567f4-f6b0-40e0-8447-a17ccfc91dfb | 5 | opea-semantic-v1 | f05544bad853c4e1 | providing container orchestration to automate the deployment, scaling, and management of AI inference services. Kubernetes ensures high availability and efficient resource utilization across the cluster.
- **Intel Gaudi Base Operator**: For deployments utilizing Intel® Gaudi® AI Accelerators, this operator manages the ... | ai_ref_knowledge | OPEA Documentation | providing container orchestration to automate the deployment, scaling, and management of AI inference services. Kubernetes ensures high availability and efficient resource utilization across the cluster.
- **Intel Gaudi Base Operator**: For deployments utilizing Intel® Gaudi® AI Accelerators, this operator manages the ... | providing container orchestration to automate the deployment, scaling, and management of AI inference services. Kubernetes ensures high availability and efficient resource utilization across the cluster.
- **Intel Gaudi Base Operator**: For deployments utilizing Intel® Gaudi® AI Accelerators, this operator manages the ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
647fc676-f176-4413-8f02-3b1a22ed4476 | OPEA Documentation | file://datasets/opea-docs/tutorial/EnterpriseInference/EnterpriseInference_Guide.rst | unknown | f3e567f4-f6b0-40e0-8447-a17ccfc91dfb | 22 | opea-semantic-v1 | 43c72ae96b04b204 | docker compose -f compose_remote.yaml up -d
2. Endpoints with Microservices
+++++++++++++++++++++++++++++++
This section applies to the following GenAIExamples:
- AgentQnA
- FinanceAgent
- WorkflowExecAgent | ai_ref_knowledge | OPEA Documentation | docker compose -f compose_remote.yaml up -d
2. Endpoints with Microservices
+++++++++++++++++++++++++++++++
This section applies to the following GenAIExamples:
- AgentQnA
- FinanceAgent
- WorkflowExecAgent | docker compose -f compose_remote.yaml up -d
2. Endpoints with Microservices
+++++++++++++++++++++++++++++++
This section applies to the following GenAIExamples:
- AgentQnA
- FinanceAgent
- WorkflowExecAgent | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6fe12d92-35ed-481e-8069-7e8cd351bb83 | OPEA Documentation | file://datasets/opea-docs/tutorial/EnterpriseInference/EnterpriseInference_Guide.rst | unknown | f3e567f4-f6b0-40e0-8447-a17ccfc91dfb | 9 | opea-semantic-v1 | 0bf198eb5cffbc21 | a cloud-native API gateway, handling API traffic with advanced features such as caching and authentication. It ensures efficient and secure access to deployed AI models.
- **Observability**: Offers comprehensive monitoring and visibility into the performance, health, and resource utilization of deployed applications an... | ai_ref_knowledge | OPEA Documentation | a cloud-native API gateway, handling API traffic with advanced features such as caching and authentication. It ensures efficient and secure access to deployed AI models.
- **Observability**: Offers comprehensive monitoring and visibility into the performance, health, and resource utilization of deployed applications an... | a cloud-native API gateway, handling API traffic with advanced features such as caching and authentication. It ensures efficient and secure access to deployed AI models.
- **Observability**: Offers comprehensive monitoring and visibility into the performance, health, and resource utilization of deployed applications an... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
72b043c0-8d93-4c07-93bf-e74b9ff6417b | OPEA Documentation | file://datasets/opea-docs/tutorial/EnterpriseInference/EnterpriseInference_Guide.rst | unknown | f3e567f4-f6b0-40e0-8447-a17ccfc91dfb | 20 | opea-semantic-v1 | 7d754d42f90b7849 | 1. Endpoints with Megaservices ++++++++++++++++++++++++++++++ This section applies to the following GenAIExamples: - AudioQnA - ChatQnA - CodeGen - CodeTrans - DocSum - Productivity Suite
Set additional environment variable(s):
- `LLM_MODEL_ID` is the model card which may need to be overwritten depending on what it is... | ai_ref_knowledge | OPEA Documentation | 1. Endpoints with Megaservices ++++++++++++++++++++++++++++++ This section applies to the following GenAIExamples: - AudioQnA - ChatQnA - CodeGen - CodeTrans - DocSum - Productivity Suite
Set additional environment variable(s):
- `LLM_MODEL_ID` is the model card which may need to be overwritten depending on what it is... | 1. Endpoints with Megaservices ++++++++++++++++++++++++++++++ This section applies to the following GenAIExamples: - AudioQnA - ChatQnA - CodeGen - CodeTrans - DocSum - Productivity Suite
Set additional environment variable(s):
- `LLM_MODEL_ID` is the model card which may need to be overwritten depending on what it is... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7970cc29-3e49-461d-9b1d-6a72f5383f27 | OPEA Documentation | file://datasets/opea-docs/tutorial/EnterpriseInference/EnterpriseInference_Guide.rst | unknown | f3e567f4-f6b0-40e0-8447-a17ccfc91dfb | 25 | opea-semantic-v1 | fd7e79a133c10d69 | * - GenAIExample - Environment Variable(s) * - AgentQnA - export model=<model-card> * - FinanceAgent - export OPENAI_LLM_MODEL_ID=<model-card>
Run *docker compose* for the example by **appending** the *compose_remote.yaml* file to the original command: | ai_ref_knowledge | OPEA Documentation | * - GenAIExample - Environment Variable(s) * - AgentQnA - export model=<model-card> * - FinanceAgent - export OPENAI_LLM_MODEL_ID=<model-card>
Run *docker compose* for the example by **appending** the *compose_remote.yaml* file to the original command: | * - GenAIExample - Environment Variable(s) * - AgentQnA - export model=<model-card> * - FinanceAgent - export OPENAI_LLM_MODEL_ID=<model-card>
Run *docker compose* for the example by **appending** the *compose_remote.yaml* file to the original command: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7f1e1394-24c8-4aec-a59e-a16fdb9240a4 | OPEA Documentation | file://datasets/opea-docs/tutorial/EnterpriseInference/EnterpriseInference_Guide.rst | unknown | f3e567f4-f6b0-40e0-8447-a17ccfc91dfb | 24 | opea-semantic-v1 | 8ed8fc9cf88a8ef8 | .. list-table:: Environment Variables for GenAIExamples
* - GenAIExample
- Environment Variable(s)
* - AgentQnA
- export model=<model-card>
* - FinanceAgent
- export OPENAI_LLM_MODEL_ID=<model-card> | ai_ref_knowledge | OPEA Documentation | .. list-table:: Environment Variables for GenAIExamples
* - GenAIExample
- Environment Variable(s)
* - AgentQnA
- export model=<model-card>
* - FinanceAgent
- export OPENAI_LLM_MODEL_ID=<model-card> | .. list-table:: Environment Variables for GenAIExamples
* - GenAIExample
- Environment Variable(s)
* - AgentQnA
- export model=<model-card>
* - FinanceAgent
- export OPENAI_LLM_MODEL_ID=<model-card> | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
863d810a-2e7d-4420-8cc4-e28251d1f1c0 | OPEA Documentation | file://datasets/opea-docs/tutorial/EnterpriseInference/EnterpriseInference_Guide.rst | unknown | f3e567f4-f6b0-40e0-8447-a17ccfc91dfb | 16 | opea-semantic-v1 | abfa8caea8239eb4 | configure the application to instead interact with models deployed on a remote server or cluster by specifying the HTTPS endpoint and providing the API key.
For all GenAIExamples, set the following environment variables:
- `REMOTE_ENDPOINT` is the HTTPS endpoint of the remote server with the model of choice (i.e. http... | ai_ref_knowledge | OPEA Documentation | configure the application to instead interact with models deployed on a remote server or cluster by specifying the HTTPS endpoint and providing the API key.
For all GenAIExamples, set the following environment variables:
- `REMOTE_ENDPOINT` is the HTTPS endpoint of the remote server with the model of choice (i.e. http... | configure the application to instead interact with models deployed on a remote server or cluster by specifying the HTTPS endpoint and providing the API key.
For all GenAIExamples, set the following environment variables:
- `REMOTE_ENDPOINT` is the HTTPS endpoint of the remote server with the model of choice (i.e. http... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
9328d299-432b-474a-bddd-cbb53704658f | OPEA Documentation | file://datasets/opea-docs/tutorial/EnterpriseInference/EnterpriseInference_Guide.rst | unknown | f3e567f4-f6b0-40e0-8447-a17ccfc91dfb | 23 | opea-semantic-v1 | c8928edf7b504f28 | 2. Endpoints with Microservices +++++++++++++++++++++++++++++++ This section applies to the following GenAIExamples: - AgentQnA - FinanceAgent - WorkflowExecAgent
Set additional environment variable(s) depending on the example: | ai_ref_knowledge | OPEA Documentation | 2. Endpoints with Microservices +++++++++++++++++++++++++++++++ This section applies to the following GenAIExamples: - AgentQnA - FinanceAgent - WorkflowExecAgent
Set additional environment variable(s) depending on the example: | 2. Endpoints with Microservices +++++++++++++++++++++++++++++++ This section applies to the following GenAIExamples: - AgentQnA - FinanceAgent - WorkflowExecAgent
Set additional environment variable(s) depending on the example: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a8f3a7a7-d8f1-4627-9d8e-57117c9dc7d3 | OPEA Documentation | file://datasets/opea-docs/tutorial/EnterpriseInference/EnterpriseInference_Guide.rst | unknown | f3e567f4-f6b0-40e0-8447-a17ccfc91dfb | 4 | opea-semantic-v1 | 6b91978fcc9d2b85 | How It Works ************ With a single click, Intel® AI for Enterprise Inference leverages several key components to streamline AI inference deployment and management:
- **Kubernetes**: Acts as the backbone of the solution, providing container orchestration to automate the deployment, scaling, and management of AI inf... | ai_ref_knowledge | OPEA Documentation | How It Works ************ With a single click, Intel® AI for Enterprise Inference leverages several key components to streamline AI inference deployment and management:
- **Kubernetes**: Acts as the backbone of the solution, providing container orchestration to automate the deployment, scaling, and management of AI inf... | How It Works ************ With a single click, Intel® AI for Enterprise Inference leverages several key components to streamline AI inference deployment and management:
- **Kubernetes**: Acts as the backbone of the solution, providing container orchestration to automate the deployment, scaling, and management of AI inf... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b1b1929e-c0de-429a-bed7-e020b6f6c86b | OPEA Documentation | file://datasets/opea-docs/tutorial/EnterpriseInference/EnterpriseInference_Guide.rst | unknown | f3e567f4-f6b0-40e0-8447-a17ccfc91dfb | 2 | opea-semantic-v1 | 846a677819836be3 | broad range of Intel hardware platforms, including Intel® Xeon® Scalable processors and Intel® Gaudi® AI Accelerators, ensuring flexibility and scalability to meet diverse enterprise needs.
Intel® AI for Enterprise Inference, powered by OPEA, is compatible with OpenAI standard APIs, enabling seamless integration to ent... | ai_ref_knowledge | OPEA Documentation | broad range of Intel hardware platforms, including Intel® Xeon® Scalable processors and Intel® Gaudi® AI Accelerators, ensuring flexibility and scalability to meet diverse enterprise needs.
Intel® AI for Enterprise Inference, powered by OPEA, is compatible with OpenAI standard APIs, enabling seamless integration to ent... | broad range of Intel hardware platforms, including Intel® Xeon® Scalable processors and Intel® Gaudi® AI Accelerators, ensuring flexibility and scalability to meet diverse enterprise needs.
Intel® AI for Enterprise Inference, powered by OPEA, is compatible with OpenAI standard APIs, enabling seamless integration to ent... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b4b77279-8e11-432b-a545-6614f661ba56 | OPEA Documentation | file://datasets/opea-docs/tutorial/EnterpriseInference/EnterpriseInference_Guide.rst | unknown | f3e567f4-f6b0-40e0-8447-a17ccfc91dfb | 13 | opea-semantic-v1 | 74484566bf936494 | Cluster ************************************* The first step is to get access to the hardware platform of choice: - Intel® Gaudi® AI Accelerators - Intel® Xeon® Scalable processors
This can be an on-premises machine or from a cloud service provider. All options for deployment can be found `here <https://www.intel.com/c... | ai_ref_knowledge | OPEA Documentation | Cluster ************************************* The first step is to get access to the hardware platform of choice: - Intel® Gaudi® AI Accelerators - Intel® Xeon® Scalable processors
This can be an on-premises machine or from a cloud service provider. All options for deployment can be found `here <https://www.intel.com/c... | Cluster ************************************* The first step is to get access to the hardware platform of choice: - Intel® Gaudi® AI Accelerators - Intel® Xeon® Scalable processors
This can be an on-premises machine or from a cloud service provider. All options for deployment can be found `here <https://www.intel.com/c... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b532ba30-195d-46f8-a4b5-d494f1c770e0 | OPEA Documentation | file://datasets/opea-docs/tutorial/EnterpriseInference/EnterpriseInference_Guide.rst | unknown | f3e567f4-f6b0-40e0-8447-a17ccfc91dfb | 6 | opea-semantic-v1 | f0b5008015ebb70f | Habana AI resources within the Kubernetes cluster. It optimizes the utilization of Gaudi hardware for AI workloads, ensuring peak performance. (Applicable only to Gaudi-based deployments.)
- **Ingress NGINX Controller**: Serves as a high-performance reverse proxy and load balancer, routing incoming requests to the appr... | ai_ref_knowledge | OPEA Documentation | Habana AI resources within the Kubernetes cluster. It optimizes the utilization of Gaudi hardware for AI workloads, ensuring peak performance. (Applicable only to Gaudi-based deployments.)
- **Ingress NGINX Controller**: Serves as a high-performance reverse proxy and load balancer, routing incoming requests to the appr... | Habana AI resources within the Kubernetes cluster. It optimizes the utilization of Gaudi hardware for AI workloads, ensuring peak performance. (Applicable only to Gaudi-based deployments.)
- **Ingress NGINX Controller**: Serves as a high-performance reverse proxy and load balancer, routing incoming requests to the appr... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
bac87f06-ea62-4b2d-aab1-d61a0741a77d | OPEA Documentation | file://datasets/opea-docs/tutorial/EnterpriseInference/EnterpriseInference_Guide.rst | unknown | f3e567f4-f6b0-40e0-8447-a17ccfc91dfb | 7 | opea-semantic-v1 | 0e9fc0d79a470339 | and load balancer, routing incoming requests to the appropriate services within the cluster. This ensures seamless access to deployed AI models and efficient traffic management.
- **Keycloak**: Provides robust identity and access management capabilities, enabling secure authentication and authorization for accessing AI... | ai_ref_knowledge | OPEA Documentation | and load balancer, routing incoming requests to the appropriate services within the cluster. This ensures seamless access to deployed AI models and efficient traffic management.
- **Keycloak**: Provides robust identity and access management capabilities, enabling secure authentication and authorization for accessing AI... | and load balancer, routing incoming requests to the appropriate services within the cluster. This ensures seamless access to deployed AI models and efficient traffic management.
- **Keycloak**: Provides robust identity and access management capabilities, enabling secure authentication and authorization for accessing AI... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c7915f7a-499d-4020-9b65-99bbf8b998dc | OPEA Documentation | file://datasets/opea-docs/tutorial/EnterpriseInference/EnterpriseInference_Guide.rst | unknown | f3e567f4-f6b0-40e0-8447-a17ccfc91dfb | 8 | opea-semantic-v1 | ba4f5ea98417f603 | - **Keycloak**: Provides robust identity and access management capabilities, enabling secure authentication and authorization for accessing AI services and resources within the cluster.
- **APISIX**: Functions as a cloud-native API gateway, handling API traffic with advanced features such as caching and authentication.... | ai_ref_knowledge | OPEA Documentation | - **Keycloak**: Provides robust identity and access management capabilities, enabling secure authentication and authorization for accessing AI services and resources within the cluster.
- **APISIX**: Functions as a cloud-native API gateway, handling API traffic with advanced features such as caching and authentication.... | - **Keycloak**: Provides robust identity and access management capabilities, enabling secure authentication and authorization for accessing AI services and resources within the cluster.
- **APISIX**: Functions as a cloud-native API gateway, handling API traffic with advanced features such as caching and authentication.... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
dcf10b34-832b-4b8f-81ca-08d682533bf4 | OPEA Documentation | file://datasets/opea-docs/tutorial/EnterpriseInference/EnterpriseInference_Guide.rst | unknown | f3e567f4-f6b0-40e0-8447-a17ccfc91dfb | 21 | opea-semantic-v1 | 39325793c367bfa3 | Set additional environment variable(s): - `LLM_MODEL_ID` is the model card which may need to be overwritten depending on what it is set to `set_env.sh`.
export LLM_MODEL_ID=<model-id-deployed-on-remote-server> | ai_ref_knowledge | OPEA Documentation | Set additional environment variable(s): - `LLM_MODEL_ID` is the model card which may need to be overwritten depending on what it is set to `set_env.sh`.
export LLM_MODEL_ID=<model-id-deployed-on-remote-server> | Set additional environment variable(s): - `LLM_MODEL_ID` is the model card which may need to be overwritten depending on what it is set to `set_env.sh`.
export LLM_MODEL_ID=<model-id-deployed-on-remote-server> | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e235fbf4-6840-4313-8441-6b219f20fe89 | OPEA Documentation | file://datasets/opea-docs/tutorial/EnterpriseInference/EnterpriseInference_Guide.rst | unknown | f3e567f4-f6b0-40e0-8447-a17ccfc91dfb | 11 | opea-semantic-v1 | b021ef4d189803e6 | models within the Kubernetes inference cluster. This enables scalable and reliable AI inference capabilities, allowing enterprises to adapt to growing demands and new models effortlessly.
After deployment, the models can be accessed using an OpenAI API with an https endpoint. This endpoint and API key are all that is n... | ai_ref_knowledge | OPEA Documentation | models within the Kubernetes inference cluster. This enables scalable and reliable AI inference capabilities, allowing enterprises to adapt to growing demands and new models effortlessly.
After deployment, the models can be accessed using an OpenAI API with an https endpoint. This endpoint and API key are all that is n... | models within the Kubernetes inference cluster. This enables scalable and reliable AI inference capabilities, allowing enterprises to adapt to growing demands and new models effortlessly.
After deployment, the models can be accessed using an OpenAI API with an https endpoint. This endpoint and API key are all that is n... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f68363af-430b-42f3-915b-903f25cfcd8d | OPEA Documentation | file://datasets/opea-docs/tutorial/EnterpriseInference/EnterpriseInference_Guide.rst | unknown | f3e567f4-f6b0-40e0-8447-a17ccfc91dfb | 10 | opea-semantic-v1 | 5ba7cf4f645c575a | visibility into the performance, health, and resource utilization of deployed applications and cluster components. It provides metrics, visualization, and alerting capabilities to maintain operational excellence.
- **Model Deployments**: Automates the deployment and management of AI LLM models within the Kubernetes inf... | ai_ref_knowledge | OPEA Documentation | visibility into the performance, health, and resource utilization of deployed applications and cluster components. It provides metrics, visualization, and alerting capabilities to maintain operational excellence.
- **Model Deployments**: Automates the deployment and management of AI LLM models within the Kubernetes inf... | visibility into the performance, health, and resource utilization of deployed applications and cluster components. It provides metrics, visualization, and alerting capabilities to maintain operational excellence.
- **Model Deployments**: Automates the deployment and management of AI LLM models within the Kubernetes inf... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1e4a2444-01a4-4ccc-ba93-b05c2a230f82 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/OpenTelemetry_OPEA_Guide.rst | unknown | 75234309-f49e-4ee5-8ec0-bc9c13e2af19 | 4 | opea-semantic-v1 | 60dfefb0fecd1286 | and TGI. Once Jaeger endpoints are configured in OPEA microservices, TEI, and TGI, trace data will automatically be reported and visualized in the Jaeger UI.
How to Monitor
**************** | ai_ref_knowledge | OPEA Documentation | and TGI. Once Jaeger endpoints are configured in OPEA microservices, TEI, and TGI, trace data will automatically be reported and visualized in the Jaeger UI.
How to Monitor
**************** | and TGI. Once Jaeger endpoints are configured in OPEA microservices, TEI, and TGI, trace data will automatically be reported and visualized in the Jaeger UI.
How to Monitor
**************** | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
221079f1-5f2a-46ab-90ad-5211cc102391 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/OpenTelemetry_OPEA_Guide.rst | unknown | 75234309-f49e-4ee5-8ec0-bc9c13e2af19 | 0 | opea-semantic-v1 | 0701fc2e7b06aab3 | OpenTelemetry on OPEA Guide #############################
Overview
********
OpenTelemetry (also referred to as OTel) is an open source observability framework made up of a collection of tools, APIs, and SDKs. OTel enables developers to instrument, generate, collect, and export telemetry data for analysis and to underst... | ai_ref_knowledge | OPEA Documentation | OpenTelemetry on OPEA Guide #############################
Overview
********
OpenTelemetry (also referred to as OTel) is an open source observability framework made up of a collection of tools, APIs, and SDKs. OTel enables developers to instrument, generate, collect, and export telemetry data for analysis and to underst... | OpenTelemetry on OPEA Guide #############################
Overview
********
OpenTelemetry (also referred to as OTel) is an open source observability framework made up of a collection of tools, APIs, and SDKs. OTel enables developers to instrument, generate, collect, and export telemetry data for analysis and to underst... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
285841f6-bb0e-4ed4-8d24-fa7e444c0540 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/OpenTelemetry_OPEA_Guide.rst | unknown | 75234309-f49e-4ee5-8ec0-bc9c13e2af19 | 21 | opea-semantic-v1 | 4c01bedbec142bf8 | Select "opea" as the service, then click the "Find Traces" button to view the trace data associated with the service's execution.
All traces will be displayed on the UI. The diagram in the upper right corner provides a visual representation of all requests along the timeline. Meanwhile,
the diagrams in the lower right ... | ai_ref_knowledge | OPEA Documentation | Select "opea" as the service, then click the "Find Traces" button to view the trace data associated with the service's execution.
All traces will be displayed on the UI. The diagram in the upper right corner provides a visual representation of all requests along the timeline. Meanwhile,
the diagrams in the lower right ... | Select "opea" as the service, then click the "Find Traces" button to view the trace data associated with the service's execution.
All traces will be displayed on the UI. The diagram in the upper right corner provides a visual representation of all requests along the timeline. Meanwhile,
the diagrams in the lower right ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
37313898-5b48-472b-9eae-0bf0b446e995 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/OpenTelemetry_OPEA_Guide.rst | unknown | 75234309-f49e-4ee5-8ec0-bc9c13e2af19 | 9 | opea-semantic-v1 | e6e9f63f13652370 | http://${host_ip}:9090/targets
Services with accessible metrics endpoints will be marked as "up" in Prometheus. If a service is marked as "down," Grafana Dashboards will be unable to display the associated metrics information. | ai_ref_knowledge | OPEA Documentation | http://${host_ip}:9090/targets
Services with accessible metrics endpoints will be marked as "up" in Prometheus. If a service is marked as "down," Grafana Dashboards will be unable to display the associated metrics information. | http://${host_ip}:9090/targets
Services with accessible metrics endpoints will be marked as "up" in Prometheus. If a service is marked as "down," Grafana Dashboards will be unable to display the associated metrics information. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
3f7daaf6-ec06-46c6-90ac-26426b77dbc8 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/OpenTelemetry_OPEA_Guide.rst | unknown | 75234309-f49e-4ee5-8ec0-bc9c13e2af19 | 11 | opea-semantic-v1 | e8e5a7ca77018143 | 2. Grafana +++++++++++++++
The Grafana UI displays telemetry metrics through pre-defined dashboards, providing a clear visualization of data. For OPEA examples, Grafana is configured by default to use Prometheus as its data source, eliminating the need for manual setup. The Grafana UI web page can be accessed using the... | ai_ref_knowledge | OPEA Documentation | 2. Grafana +++++++++++++++
The Grafana UI displays telemetry metrics through pre-defined dashboards, providing a clear visualization of data. For OPEA examples, Grafana is configured by default to use Prometheus as its data source, eliminating the need for manual setup. The Grafana UI web page can be accessed using the... | 2. Grafana +++++++++++++++
The Grafana UI displays telemetry metrics through pre-defined dashboards, providing a clear visualization of data. For OPEA examples, Grafana is configured by default to use Prometheus as its data source, eliminating the need for manual setup. The Grafana UI web page can be accessed using the... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
44f23edb-c108-46ff-8d20-44d28f30c116 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/OpenTelemetry_OPEA_Guide.rst | unknown | 75234309-f49e-4ee5-8ec0-bc9c13e2af19 | 17 | opea-semantic-v1 | c5d8ec5c3b1fad71 | and monitors key functions within them. By default, Jaeger operates on port 16686. The Jaeger UI web page could be accessed using the following URL.
http://${host_ip}:16686 | ai_ref_knowledge | OPEA Documentation | and monitors key functions within them. By default, Jaeger operates on port 16686. The Jaeger UI web page could be accessed using the following URL.
http://${host_ip}:16686 | and monitors key functions within them. By default, Jaeger operates on port 16686. The Jaeger UI web page could be accessed using the following URL.
http://${host_ip}:16686 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
45528c20-aae8-43aa-8d8c-29a6c103d4b5 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/OpenTelemetry_OPEA_Guide.rst | unknown | 75234309-f49e-4ee5-8ec0-bc9c13e2af19 | 12 | opea-semantic-v1 | ea9e40f2efc86f2e | to use Prometheus as its data source, eliminating the need for manual setup. The Grafana UI web page can be accessed using the following URL.
http://${host_ip}:3000 | ai_ref_knowledge | OPEA Documentation | to use Prometheus as its data source, eliminating the need for manual setup. The Grafana UI web page can be accessed using the following URL.
http://${host_ip}:3000 | to use Prometheus as its data source, eliminating the need for manual setup. The Grafana UI web page can be accessed using the following URL.
http://${host_ip}:3000 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
48384fef-5e84-404b-bc27-7edce251d324 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/OpenTelemetry_OPEA_Guide.rst | unknown | 75234309-f49e-4ee5-8ec0-bc9c13e2af19 | 27 | opea-semantic-v1 | c25eea501783e1a9 | source ./set_env.sh docker compose -f compose.yaml -f compose.telemetry.yaml up -d
Below are the GenAIExamples that include support for Grafana, Prometheus, and Jaeger services. | ai_ref_knowledge | OPEA Documentation | source ./set_env.sh docker compose -f compose.yaml -f compose.telemetry.yaml up -d
Below are the GenAIExamples that include support for Grafana, Prometheus, and Jaeger services. | source ./set_env.sh docker compose -f compose.yaml -f compose.telemetry.yaml up -d
Below are the GenAIExamples that include support for Grafana, Prometheus, and Jaeger services. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4b250ce9-82c4-4eab-9e57-478d42251c4c | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/OpenTelemetry_OPEA_Guide.rst | unknown | 75234309-f49e-4ee5-8ec0-bc9c13e2af19 | 19 | opea-semantic-v1 | 5619f0c914c6c4e4 | appear in the Jaeger UI if the relevant functions have been executed. Therefore, without running the example, the UI will not display any trace data.
Once the example is run, refresh the Jaeger UI webpage, and the OPEA service should appear under the "Services" tab,
indicating that trace data is being captured and disp... | ai_ref_knowledge | OPEA Documentation | appear in the Jaeger UI if the relevant functions have been executed. Therefore, without running the example, the UI will not display any trace data.
Once the example is run, refresh the Jaeger UI webpage, and the OPEA service should appear under the "Services" tab,
indicating that trace data is being captured and disp... | appear in the Jaeger UI if the relevant functions have been executed. Therefore, without running the example, the UI will not display any trace data.
Once the example is run, refresh the Jaeger UI webpage, and the OPEA service should appear under the "Services" tab,
indicating that trace data is being captured and disp... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5a9a0e08-6cd9-4709-bd03-4cfe79ea3c02 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/OpenTelemetry_OPEA_Guide.rst | unknown | 75234309-f49e-4ee5-8ec0-bc9c13e2af19 | 15 | opea-semantic-v1 | 93239bab23b65084 | telemetry sections of the respective GenAIExamples. These sections offer insights into how to interpret the data and utilize the dashboards effectively for monitoring and analysis.
ChatQnA <deploy/ChatQnA>
AgentQnA <deploy/AgentQnA> | ai_ref_knowledge | OPEA Documentation | telemetry sections of the respective GenAIExamples. These sections offer insights into how to interpret the data and utilize the dashboards effectively for monitoring and analysis.
ChatQnA <deploy/ChatQnA>
AgentQnA <deploy/AgentQnA> | telemetry sections of the respective GenAIExamples. These sections offer insights into how to interpret the data and utilize the dashboards effectively for monitoring and analysis.
ChatQnA <deploy/ChatQnA>
AgentQnA <deploy/AgentQnA> | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
69a20cb1-6319-44f2-95d9-d15422c7a0a6 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/OpenTelemetry_OPEA_Guide.rst | unknown | 75234309-f49e-4ee5-8ec0-bc9c13e2af19 | 1 | opea-semantic-v1 | 56d8cc594ff14919 | of traces, metrics, and logs. OPEA integrates OpenTelemetry's metrics and tracing capabilities to enhance its telemetry support, providing users with valuable insights into system performance.
How It Works
************
OPEA Comps offers telemetry functionalities for metrics and tracing by integrating with tools such as... | ai_ref_knowledge | OPEA Documentation | of traces, metrics, and logs. OPEA integrates OpenTelemetry's metrics and tracing capabilities to enhance its telemetry support, providing users with valuable insights into system performance.
How It Works
************
OPEA Comps offers telemetry functionalities for metrics and tracing by integrating with tools such as... | of traces, metrics, and logs. OPEA integrates OpenTelemetry's metrics and tracing capabilities to enhance its telemetry support, providing users with valuable insights into system performance.
How It Works
************
OPEA Comps offers telemetry functionalities for metrics and tracing by integrating with tools such as... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6d2cfd69-7a66-431c-94d4-a7aaa45e8f3f | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/OpenTelemetry_OPEA_Guide.rst | unknown | 75234309-f49e-4ee5-8ec0-bc9c13e2af19 | 6 | opea-semantic-v1 | 34e264120ee2a68e | OpenTelemetry metrics and tracing currently can be visualized through one of three primary monitoring UI web pages.
1. Prometheus
+++++++++++++++ | ai_ref_knowledge | OPEA Documentation | OpenTelemetry metrics and tracing currently can be visualized through one of three primary monitoring UI web pages.
1. Prometheus
+++++++++++++++ | OpenTelemetry metrics and tracing currently can be visualized through one of three primary monitoring UI web pages.
1. Prometheus
+++++++++++++++ | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
759f107a-0906-4e30-9260-16c3e21eb700 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/OpenTelemetry_OPEA_Guide.rst | unknown | 75234309-f49e-4ee5-8ec0-bc9c13e2af19 | 5 | opea-semantic-v1 | 9b16c6e2487b0ab9 | How to Monitor ****************
OpenTelemetry metrics and tracing currently can be visualized through one of three primary monitoring UI web pages. | ai_ref_knowledge | OPEA Documentation | How to Monitor ****************
OpenTelemetry metrics and tracing currently can be visualized through one of three primary monitoring UI web pages. | How to Monitor ****************
OpenTelemetry metrics and tracing currently can be visualized through one of three primary monitoring UI web pages. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7ef2b875-961e-4d99-925d-e3f03b809d3b | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/OpenTelemetry_OPEA_Guide.rst | unknown | 75234309-f49e-4ee5-8ec0-bc9c13e2af19 | 7 | opea-semantic-v1 | 60affd231f6ccd15 | 1. Prometheus +++++++++++++++
The Prometheus UI provides insights into which services have active metrics endpoints. By default, Prometheus operates on port 9090. You can access the Prometheus UI web page using the following URL. | ai_ref_knowledge | OPEA Documentation | 1. Prometheus +++++++++++++++
The Prometheus UI provides insights into which services have active metrics endpoints. By default, Prometheus operates on port 9090. You can access the Prometheus UI web page using the following URL. | 1. Prometheus +++++++++++++++
The Prometheus UI provides insights into which services have active metrics endpoints. By default, Prometheus operates on port 9090. You can access the Prometheus UI web page using the following URL. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8c5fe4d0-9b43-467f-a0d1-2c39c07a4154 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/OpenTelemetry_OPEA_Guide.rst | unknown | 75234309-f49e-4ee5-8ec0-bc9c13e2af19 | 16 | opea-semantic-v1 | ff304ad2cca4d054 | 3. Jaeger +++++++++++++++
The Jaeger UI is instrumental in understanding function tracing for each request, providing visibility into the execution flow and timing of microservices. OPEA traces the execution time for each microservice and monitors key functions within them. By default, Jaeger operates on port 16686. Th... | ai_ref_knowledge | OPEA Documentation | 3. Jaeger +++++++++++++++
The Jaeger UI is instrumental in understanding function tracing for each request, providing visibility into the execution flow and timing of microservices. OPEA traces the execution time for each microservice and monitors key functions within them. By default, Jaeger operates on port 16686. Th... | 3. Jaeger +++++++++++++++
The Jaeger UI is instrumental in understanding function tracing for each request, providing visibility into the execution flow and timing of microservices. OPEA traces the execution time for each microservice and monitors key functions within them. By default, Jaeger operates on port 16686. Th... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8c7f0584-8891-4a2e-ac65-1c4ecd30d024 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/OpenTelemetry_OPEA_Guide.rst | unknown | 75234309-f49e-4ee5-8ec0-bc9c13e2af19 | 8 | opea-semantic-v1 | 2cff9745f8e61fe5 | which services have active metrics endpoints. By default, Prometheus operates on port 9090. You can access the Prometheus UI web page using the following URL.
http://${host_ip}:9090/targets | ai_ref_knowledge | OPEA Documentation | which services have active metrics endpoints. By default, Prometheus operates on port 9090. You can access the Prometheus UI web page using the following URL.
http://${host_ip}:9090/targets | which services have active metrics endpoints. By default, Prometheus operates on port 9090. You can access the Prometheus UI web page using the following URL.
http://${host_ip}:9090/targets | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
98ddfd17-117e-4c95-8152-812936e69204 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/OpenTelemetry_OPEA_Guide.rst | unknown | 75234309-f49e-4ee5-8ec0-bc9c13e2af19 | 18 | opea-semantic-v1 | 1db3c9f1c7d07d5c | http://${host_ip}:16686
Traces will only appear in the Jaeger UI if the relevant functions have been executed. Therefore, without running the example, the UI will not display any trace data. | ai_ref_knowledge | OPEA Documentation | http://${host_ip}:16686
Traces will only appear in the Jaeger UI if the relevant functions have been executed. Therefore, without running the example, the UI will not display any trace data. | http://${host_ip}:16686
Traces will only appear in the Jaeger UI if the relevant functions have been executed. Therefore, without running the example, the UI will not display any trace data. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
9b4f8495-78b9-4805-8c03-d24193dc0fca | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/OpenTelemetry_OPEA_Guide.rst | unknown | 75234309-f49e-4ee5-8ec0-bc9c13e2af19 | 24 | opea-semantic-v1 | d2f8806d2612abb6 | then apply the Python decorator @opea_telemetry to the function you wish to trace. Below is an example of how to trace your_func using OPEA tracing:
from comps import opea_telemetry | ai_ref_knowledge | OPEA Documentation | then apply the Python decorator @opea_telemetry to the function you wish to trace. Below is an example of how to trace your_func using OPEA tracing:
from comps import opea_telemetry | then apply the Python decorator @opea_telemetry to the function you wish to trace. Below is an example of how to trace your_func using OPEA tracing:
from comps import opea_telemetry | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a3bc9881-e6ef-4159-8246-2b2ee04e90f8 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/OpenTelemetry_OPEA_Guide.rst | unknown | 75234309-f49e-4ee5-8ec0-bc9c13e2af19 | 2 | opea-semantic-v1 | 6903cb31715694f0 | for metrics and tracing by integrating with tools such as Prometheus, Grafana, and Jaeger. Below is a brief introduction to the workflows of those tools:
The majority of OPEA's micro and mega services are equipped to support OpenTelemetry metrics, which are exported in Prometheus format via the /metrics endpoint. For f... | ai_ref_knowledge | OPEA Documentation | for metrics and tracing by integrating with tools such as Prometheus, Grafana, and Jaeger. Below is a brief introduction to the workflows of those tools:
The majority of OPEA's micro and mega services are equipped to support OpenTelemetry metrics, which are exported in Prometheus format via the /metrics endpoint. For f... | for metrics and tracing by integrating with tools such as Prometheus, Grafana, and Jaeger. Below is a brief introduction to the workflows of those tools:
The majority of OPEA's micro and mega services are equipped to support OpenTelemetry metrics, which are exported in Prometheus format via the /metrics endpoint. For f... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a3c37804-1321-4841-96c8-b6c83e13c12c | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/OpenTelemetry_OPEA_Guide.rst | unknown | 75234309-f49e-4ee5-8ec0-bc9c13e2af19 | 23 | opea-semantic-v1 | 75b647fe31a5a516 | Code Instrumentations for OPEA Tracing ****************************************
Enabling OPEA OpenTelemetry tracing for a function is straightforward. First, import opea_telemetry, and then apply the Python decorator @opea_telemetry to the function you wish to trace. Below is an example of how to trace your_func using ... | ai_ref_knowledge | OPEA Documentation | Code Instrumentations for OPEA Tracing ****************************************
Enabling OPEA OpenTelemetry tracing for a function is straightforward. First, import opea_telemetry, and then apply the Python decorator @opea_telemetry to the function you wish to trace. Below is an example of how to trace your_func using ... | Code Instrumentations for OPEA Tracing ****************************************
Enabling OPEA OpenTelemetry tracing for a function is straightforward. First, import opea_telemetry, and then apply the Python decorator @opea_telemetry to the function you wish to trace. Below is an example of how to trace your_func using ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
aaa93e87-de67-4816-87e7-099602cdcd41 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/OpenTelemetry_OPEA_Guide.rst | unknown | 75234309-f49e-4ee5-8ec0-bc9c13e2af19 | 20 | opea-semantic-v1 | 6e652bb335df9e8a | run, refresh the Jaeger UI webpage, and the OPEA service should appear under the "Services" tab, indicating that trace data is being captured and displayed.
Select "opea" as the service, then click the "Find Traces" button to view the trace data associated with the service's execution. | ai_ref_knowledge | OPEA Documentation | run, refresh the Jaeger UI webpage, and the OPEA service should appear under the "Services" tab, indicating that trace data is being captured and displayed.
Select "opea" as the service, then click the "Find Traces" button to view the trace data associated with the service's execution. | run, refresh the Jaeger UI webpage, and the OPEA service should appear under the "Services" tab, indicating that trace data is being captured and displayed.
Select "opea" as the service, then click the "Find Traces" button to view the trace data associated with the service's execution. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b8d1c2be-cac3-4100-8e47-6ffedecec7be | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/OpenTelemetry_OPEA_Guide.rst | unknown | 75234309-f49e-4ee5-8ec0-bc9c13e2af19 | 14 | opea-semantic-v1 | 1f954cdae9688043 | the Grafana UI. This will allow you to explore the available dashboards to visualize telemetry metrics. They also serve as examples that you can customize.
Detailed explanations for understanding each dashboard are provided within the telemetry sections of the respective GenAIExamples. These sections offer insights int... | ai_ref_knowledge | OPEA Documentation | the Grafana UI. This will allow you to explore the available dashboards to visualize telemetry metrics. They also serve as examples that you can customize.
Detailed explanations for understanding each dashboard are provided within the telemetry sections of the respective GenAIExamples. These sections offer insights int... | the Grafana UI. This will allow you to explore the available dashboards to visualize telemetry metrics. They also serve as examples that you can customize.
Detailed explanations for understanding each dashboard are provided within the telemetry sections of the respective GenAIExamples. These sections offer insights int... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ca74f155-1f95-452e-9bd1-43795ac7b4ab | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/OpenTelemetry_OPEA_Guide.rst | unknown | 75234309-f49e-4ee5-8ec0-bc9c13e2af19 | 3 | opea-semantic-v1 | 2cfc55b407fea60c | a crucial role in collecting metrics from OPEA service endpoints, while Grafana leverages Prometheus as a data source to visualize these metrics on pre-configured dashboards.
OPEA also supports OpenTelemetry tracing, with several OPEA GenAIExamples instrumented to trace key functions such as microservice execution and ... | ai_ref_knowledge | OPEA Documentation | a crucial role in collecting metrics from OPEA service endpoints, while Grafana leverages Prometheus as a data source to visualize these metrics on pre-configured dashboards.
OPEA also supports OpenTelemetry tracing, with several OPEA GenAIExamples instrumented to trace key functions such as microservice execution and ... | a crucial role in collecting metrics from OPEA service endpoints, while Grafana leverages Prometheus as a data source to visualize these metrics on pre-configured dashboards.
OPEA also supports OpenTelemetry tracing, with several OPEA GenAIExamples instrumented to trace key functions such as microservice execution and ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d233a36f-d06d-4842-ad04-ced5eff7f463 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/OpenTelemetry_OPEA_Guide.rst | unknown | 75234309-f49e-4ee5-8ec0-bc9c13e2af19 | 22 | opea-semantic-v1 | cf52636fd06e3d30 | the timeline. Meanwhile, the diagrams in the lower right corner illustrate all spans within each request, offering detailed insights into the execution flow and timing.
Detailed explanations for understanding each Jaeger diagrams are provided within the telemetry sections of the respective GenAIExamples. These sections... | ai_ref_knowledge | OPEA Documentation | the timeline. Meanwhile, the diagrams in the lower right corner illustrate all spans within each request, offering detailed insights into the execution flow and timing.
Detailed explanations for understanding each Jaeger diagrams are provided within the telemetry sections of the respective GenAIExamples. These sections... | the timeline. Meanwhile, the diagrams in the lower right corner illustrate all spans within each request, offering detailed insights into the execution flow and timing.
Detailed explanations for understanding each Jaeger diagrams are provided within the telemetry sections of the respective GenAIExamples. These sections... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d813c329-45c0-4f98-bcae-1ee7d05c7ddd | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/OpenTelemetry_OPEA_Guide.rst | unknown | 75234309-f49e-4ee5-8ec0-bc9c13e2af19 | 26 | opea-semantic-v1 | aff0d2a8c95d4127 | you need to include an additional telemetry Docker Compose YAML file. For instance, adding compose.telemetry.yaml alongside compose.yaml will activate all telemetry features for the example.
source ./set_env.sh
docker compose -f compose.yaml -f compose.telemetry.yaml up -d | ai_ref_knowledge | OPEA Documentation | you need to include an additional telemetry Docker Compose YAML file. For instance, adding compose.telemetry.yaml alongside compose.yaml will activate all telemetry features for the example.
source ./set_env.sh
docker compose -f compose.yaml -f compose.telemetry.yaml up -d | you need to include an additional telemetry Docker Compose YAML file. For instance, adding compose.telemetry.yaml alongside compose.yaml will activate all telemetry features for the example.
source ./set_env.sh
docker compose -f compose.yaml -f compose.telemetry.yaml up -d | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d922af00-3abf-470c-95a7-c8220363602e | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/OpenTelemetry_OPEA_Guide.rst | unknown | 75234309-f49e-4ee5-8ec0-bc9c13e2af19 | 10 | opea-semantic-v1 | 0c653074ef7ee96b | will be marked as "up" in Prometheus. If a service is marked as "down," Grafana Dashboards will be unable to display the associated metrics information.
2. Grafana
+++++++++++++++ | ai_ref_knowledge | OPEA Documentation | will be marked as "up" in Prometheus. If a service is marked as "down," Grafana Dashboards will be unable to display the associated metrics information.
2. Grafana
+++++++++++++++ | will be marked as "up" in Prometheus. If a service is marked as "down," Grafana Dashboards will be unable to display the associated metrics information.
2. Grafana
+++++++++++++++ | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e8bed489-9819-45cd-bdb3-564a10685ed6 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/OpenTelemetry_OPEA_Guide.rst | unknown | 75234309-f49e-4ee5-8ec0-bc9c13e2af19 | 25 | opea-semantic-v1 | 795313ba80c74919 | OpenTelemetry on GenAIExamples ******************************
In the OpenTelemetry-enabled GenAIExamples, OpenTelemetry Metrics is activated by default, while OpenTelemetry Tracing is initially disabled. Similarly, the Telemetry UI services, including Grafana, Prometheus, and Jaeger, are also disabled by default. To en... | ai_ref_knowledge | OPEA Documentation | OpenTelemetry on GenAIExamples ******************************
In the OpenTelemetry-enabled GenAIExamples, OpenTelemetry Metrics is activated by default, while OpenTelemetry Tracing is initially disabled. Similarly, the Telemetry UI services, including Grafana, Prometheus, and Jaeger, are also disabled by default. To en... | OpenTelemetry on GenAIExamples ******************************
In the OpenTelemetry-enabled GenAIExamples, OpenTelemetry Metrics is activated by default, while OpenTelemetry Tracing is initially disabled. Similarly, the Telemetry UI services, including Grafana, Prometheus, and Jaeger, are also disabled by default. To en... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
eaddbc52-1a47-40af-8c39-ef0089f3755c | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/OpenTelemetry_OPEA_Guide.rst | unknown | 75234309-f49e-4ee5-8ec0-bc9c13e2af19 | 13 | opea-semantic-v1 | ad70427f2fcb219d | http://${host_ip}:3000
To view the pre-defined dashboards, click on the "Dashboard" tab located on the left-hand side of the Grafana UI. This will allow you to explore the available dashboards to visualize telemetry metrics. They also serve as examples that you can customize. | ai_ref_knowledge | OPEA Documentation | http://${host_ip}:3000
To view the pre-defined dashboards, click on the "Dashboard" tab located on the left-hand side of the Grafana UI. This will allow you to explore the available dashboards to visualize telemetry metrics. They also serve as examples that you can customize. | http://${host_ip}:3000
To view the pre-defined dashboards, click on the "Dashboard" tab located on the left-hand side of the Grafana UI. This will allow you to explore the available dashboards to visualize telemetry metrics. They also serve as examples that you can customize. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
01d6e72f-2406-4133-8655-c040ee765b11 | OPEA Documentation | file://datasets/opea-docs/tutorial/VideoQnA/VideoQnA_Guide.rst | unknown | 5f135690-a171-4957-a224-c44a64a06dc6 | 5 | opea-semantic-v1 | e30ad8467c3173d9 | the best-matching videos. Contextual Inference: The retrieved videos are then sent to the Large Vision Model (LVM) for inference, providing supplemental context for the query.
.. mermaid:: | ai_ref_knowledge | OPEA Documentation | the best-matching videos. Contextual Inference: The retrieved videos are then sent to the Large Vision Model (LVM) for inference, providing supplemental context for the query.
.. mermaid:: | the best-matching videos. Contextual Inference: The retrieved videos are then sent to the Large Vision Model (LVM) for inference, providing supplemental context for the query.
.. mermaid:: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
2bd8f77d-3f9a-47c9-b163-0a111ace55b9 | OPEA Documentation | file://datasets/opea-docs/tutorial/VideoQnA/VideoQnA_Guide.rst | unknown | 5f135690-a171-4957-a224-c44a64a06dc6 | 10 | opea-semantic-v1 | 4cfb1cfaca4066db | %% Questions interaction direction LR a[User Input Query] --> UI UI --> GW GW <==> VideoQnA-MegaService EM ==> RET RET ==> RER RER ==> LVM
%% Embedding service flow
direction LR
EM <-.-> CLIP_EM
RET <-.-> V_RET
RER <-.-> LOCAL_RER
LVM <-.-> LVM_gen | ai_ref_knowledge | OPEA Documentation | %% Questions interaction direction LR a[User Input Query] --> UI UI --> GW GW <==> VideoQnA-MegaService EM ==> RET RET ==> RER RER ==> LVM
%% Embedding service flow
direction LR
EM <-.-> CLIP_EM
RET <-.-> V_RET
RER <-.-> LOCAL_RER
LVM <-.-> LVM_gen | %% Questions interaction direction LR a[User Input Query] --> UI UI --> GW GW <==> VideoQnA-MegaService EM ==> RET RET ==> RER RER ==> LVM
%% Embedding service flow
direction LR
EM <-.-> CLIP_EM
RET <-.-> V_RET
RER <-.-> LOCAL_RER
LVM <-.-> LVM_gen | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
309a34fd-dcb2-48ae-be0b-c4f34e72cdb5 | OPEA Documentation | file://datasets/opea-docs/tutorial/VideoQnA/VideoQnA_Guide.rst | unknown | 5f135690-a171-4957-a224-c44a64a06dc6 | 9 | opea-semantic-v1 | 1b883ad6a4a29528 | %% Data Preparation flow %% Ingest data flow direction LR Ingest[Ingest data] --> UI UI --> DP DP <-.-> CLIP_EM
%% Questions interaction
direction LR
a[User Input Query] --> UI
UI --> GW
GW <==> VideoQnA-MegaService
EM ==> RET
RET ==> RER
RER ==> LVM | ai_ref_knowledge | OPEA Documentation | %% Data Preparation flow %% Ingest data flow direction LR Ingest[Ingest data] --> UI UI --> DP DP <-.-> CLIP_EM
%% Questions interaction
direction LR
a[User Input Query] --> UI
UI --> GW
GW <==> VideoQnA-MegaService
EM ==> RET
RET ==> RER
RER ==> LVM | %% Data Preparation flow %% Ingest data flow direction LR Ingest[Ingest data] --> UI UI --> DP DP <-.-> CLIP_EM
%% Questions interaction
direction LR
a[User Input Query] --> UI
UI --> GW
GW <==> VideoQnA-MegaService
EM ==> RET
RET ==> RER
RER ==> LVM | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
351b02e9-22bb-4b1b-9958-60edb6dc58a8 | OPEA Documentation | file://datasets/opea-docs/tutorial/VideoQnA/VideoQnA_Guide.rst | unknown | 5f135690-a171-4957-a224-c44a64a06dc6 | 2 | opea-semantic-v1 | f4279fbb8934404d | Purpose *******
* Efficient Search: Utilizes video embeddings for accurate and efficient retrieval. * Long-form Video Support: Capable of handling extensive video archives and time-based searches. * Microservice Architecture: Built on GenAIComps, incorporating microservices for embedding, retrieval, reranking, and lang... | ai_ref_knowledge | OPEA Documentation | Purpose *******
* Efficient Search: Utilizes video embeddings for accurate and efficient retrieval. * Long-form Video Support: Capable of handling extensive video archives and time-based searches. * Microservice Architecture: Built on GenAIComps, incorporating microservices for embedding, retrieval, reranking, and lang... | Purpose *******
* Efficient Search: Utilizes video embeddings for accurate and efficient retrieval. * Long-form Video Support: Capable of handling extensive video archives and time-based searches. * Microservice Architecture: Built on GenAIComps, incorporating microservices for embedding, retrieval, reranking, and lang... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
94ce98d8-f4ce-45be-b777-5482ac02a27e | OPEA Documentation | file://datasets/opea-docs/tutorial/VideoQnA/VideoQnA_Guide.rst | unknown | 5f135690-a171-4957-a224-c44a64a06dc6 | 1 | opea-semantic-v1 | bd8434fba9292d2a | vector similarity search in Intel's VDMS vector database and performs all operations on Intel Xeon CPU. The pipeline supports long form videos and time-based search.
Purpose
******* | ai_ref_knowledge | OPEA Documentation | vector similarity search in Intel's VDMS vector database and performs all operations on Intel Xeon CPU. The pipeline supports long form videos and time-based search.
Purpose
******* | vector similarity search in Intel's VDMS vector database and performs all operations on Intel Xeon CPU. The pipeline supports long form videos and time-based search.
Purpose
******* | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
9f8833a6-6f93-4ab3-bc79-9fcbf51994e8 | OPEA Documentation | file://datasets/opea-docs/tutorial/VideoQnA/VideoQnA_Guide.rst | unknown | 5f135690-a171-4957-a224-c44a64a06dc6 | 3 | opea-semantic-v1 | 10cdd5b27ad63197 | Capable of handling extensive video archives and time-based searches. * Microservice Architecture: Built on GenAIComps, incorporating microservices for embedding, retrieval, reranking, and language model integration.
How It Works
************ | ai_ref_knowledge | OPEA Documentation | Capable of handling extensive video archives and time-based searches. * Microservice Architecture: Built on GenAIComps, incorporating microservices for embedding, retrieval, reranking, and language model integration.
How It Works
************ | Capable of handling extensive video archives and time-based searches. * Microservice Architecture: Built on GenAIComps, incorporating microservices for embedding, retrieval, reranking, and language model integration.
How It Works
************ | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a2b39588-7ae0-4396-a775-f61dc82df6d5 | OPEA Documentation | file://datasets/opea-docs/tutorial/VideoQnA/VideoQnA_Guide.rst | unknown | 5f135690-a171-4957-a224-c44a64a06dc6 | 11 | opea-semantic-v1 | 52508ba54004982e | %% Embedding service flow direction LR EM <-.-> CLIP_EM RET <-.-> V_RET RER <-.-> LOCAL_RER LVM <-.-> LVM_gen
direction TB
%% Vector DB interaction
V_RET <-.->VDB
DP <-.->VDB | ai_ref_knowledge | OPEA Documentation | %% Embedding service flow direction LR EM <-.-> CLIP_EM RET <-.-> V_RET RER <-.-> LOCAL_RER LVM <-.-> LVM_gen
direction TB
%% Vector DB interaction
V_RET <-.->VDB
DP <-.->VDB | %% Embedding service flow direction LR EM <-.-> CLIP_EM RET <-.-> V_RET RER <-.-> LOCAL_RER LVM <-.-> LVM_gen
direction TB
%% Vector DB interaction
V_RET <-.->VDB
DP <-.->VDB | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
acd805e3-557d-4b1b-8c89-7af19b2f8802 | OPEA Documentation | file://datasets/opea-docs/tutorial/VideoQnA/VideoQnA_Guide.rst | unknown | 5f135690-a171-4957-a224-c44a64a06dc6 | 8 | opea-semantic-v1 | dc50f5b0ed1ca24c | LOCAL_RER{{Reranking service<br>}} CLIP_EM{{Embedding service <br>}} VDB{{Vector DB<br><br>}} V_RET{{Retriever service <br>}} Ingest{{Ingest data <br>}} DP([Data Preparation<br>]):::blue LVM_gen{{LVM Service <br>}} GW([VideoQnA GateWay<br>]):::orange
%% Data Preparation flow
%% Ingest data flow
direction LR
Ingest[I... | ai_ref_knowledge | OPEA Documentation | LOCAL_RER{{Reranking service<br>}} CLIP_EM{{Embedding service <br>}} VDB{{Vector DB<br><br>}} V_RET{{Retriever service <br>}} Ingest{{Ingest data <br>}} DP([Data Preparation<br>]):::blue LVM_gen{{LVM Service <br>}} GW([VideoQnA GateWay<br>]):::orange
%% Data Preparation flow
%% Ingest data flow
direction LR
Ingest[I... | LOCAL_RER{{Reranking service<br>}} CLIP_EM{{Embedding service <br>}} VDB{{Vector DB<br><br>}} V_RET{{Retriever service <br>}} Ingest{{Ingest data <br>}} DP([Data Preparation<br>]):::blue LVM_gen{{LVM Service <br>}} GW([VideoQnA GateWay<br>]):::orange
%% Data Preparation flow
%% Ingest data flow
direction LR
Ingest[I... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c8c9d71b-27b8-4bd0-b8ab-07912632d633 | OPEA Documentation | file://datasets/opea-docs/tutorial/VideoQnA/VideoQnA_Guide.rst | unknown | 5f135690-a171-4957-a224-c44a64a06dc6 | 6 | opea-semantic-v1 | 3cda87ee3406c9fa | config: flowchart: nodeSpacing: 400 rankSpacing: 100 curve: linear themeVariables: fontSize: 50px
flowchart LR
%% Colors %%
classDef blue fill:#ADD8E6,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5
classDef orange fill:#FBAA60,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5
classDef orchid fill:#C26DBC,stroke:#A... | ai_ref_knowledge | OPEA Documentation | config: flowchart: nodeSpacing: 400 rankSpacing: 100 curve: linear themeVariables: fontSize: 50px
flowchart LR
%% Colors %%
classDef blue fill:#ADD8E6,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5
classDef orange fill:#FBAA60,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5
classDef orchid fill:#C26DBC,stroke:#A... | config: flowchart: nodeSpacing: 400 rankSpacing: 100 curve: linear themeVariables: fontSize: 50px
flowchart LR
%% Colors %%
classDef blue fill:#ADD8E6,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5
classDef orange fill:#FBAA60,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5
classDef orchid fill:#C26DBC,stroke:#A... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d6185ef9-6c5d-47ca-8199-b5866cae37b0 | OPEA Documentation | file://datasets/opea-docs/tutorial/VideoQnA/VideoQnA_Guide.rst | unknown | 5f135690-a171-4957-a224-c44a64a06dc6 | 0 | opea-semantic-v1 | 7beaec31a60211d8 | Overview ********
VideoQnA is a framework that retrieves video based on provided user prompt. It uses only the video embeddings to perform vector similarity search in Intel's VDMS vector database and performs all operations on Intel Xeon CPU. The pipeline supports long form videos and time-based search. | ai_ref_knowledge | OPEA Documentation | Overview ********
VideoQnA is a framework that retrieves video based on provided user prompt. It uses only the video embeddings to perform vector similarity search in Intel's VDMS vector database and performs all operations on Intel Xeon CPU. The pipeline supports long form videos and time-based search. | Overview ********
VideoQnA is a framework that retrieves video based on provided user prompt. It uses only the video embeddings to perform vector similarity search in Intel's VDMS vector database and performs all operations on Intel Xeon CPU. The pipeline supports long form videos and time-based search. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ef1bd748-a0ce-46c7-9556-731c424464cc | OPEA Documentation | file://datasets/opea-docs/tutorial/VideoQnA/VideoQnA_Guide.rst | unknown | 5f135690-a171-4957-a224-c44a64a06dc6 | 7 | opea-semantic-v1 | db2ce44c6c6919cd | subgraph VideoQnA-MegaService["VideoQnA-MegaService"] direction LR EM([Embedding MicroService]):::blue RET([Retrieval MicroService]):::blue RER([Rerank MicroService]):::blue LVM([LVM MicroService]):::blue end subgraph User Interface direction LR a([User Input Query]):::orchid UI([UI server<br>]):::orchid Ingest([Ingest... | ai_ref_knowledge | OPEA Documentation | subgraph VideoQnA-MegaService["VideoQnA-MegaService"] direction LR EM([Embedding MicroService]):::blue RET([Retrieval MicroService]):::blue RER([Rerank MicroService]):::blue LVM([LVM MicroService]):::blue end subgraph User Interface direction LR a([User Input Query]):::orchid UI([UI server<br>]):::orchid Ingest([Ingest... | subgraph VideoQnA-MegaService["VideoQnA-MegaService"] direction LR EM([Embedding MicroService]):::blue RET([Retrieval MicroService]):::blue RER([Rerank MicroService]):::blue LVM([LVM MicroService]):::blue end subgraph User Interface direction LR a([User Input Query]):::orchid UI([UI server<br>]):::orchid Ingest([Ingest... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f646bfa3-4e77-4808-b996-dfc89f0b4e6e | OPEA Documentation | file://datasets/opea-docs/tutorial/VideoQnA/VideoQnA_Guide.rst | unknown | 5f135690-a171-4957-a224-c44a64a06dc6 | 4 | opea-semantic-v1 | 500e9e67e5f0035e | How It Works ************
It utilizes the `GenAIComps <https://github.com/opea-project/GenAIComps>`_ microservice pipeline on Intel Xeon server. The steps include Docker image creation, container deployment via Docker Compose, and service execution to integrate microservices such as embedding, retriever, rerank, and lv... | ai_ref_knowledge | OPEA Documentation | How It Works ************
It utilizes the `GenAIComps <https://github.com/opea-project/GenAIComps>`_ microservice pipeline on Intel Xeon server. The steps include Docker image creation, container deployment via Docker Compose, and service execution to integrate microservices such as embedding, retriever, rerank, and lv... | How It Works ************
It utilizes the `GenAIComps <https://github.com/opea-project/GenAIComps>`_ microservice pipeline on Intel Xeon server. The steps include Docker image creation, container deployment via Docker Compose, and service execution to integrate microservices such as embedding, retriever, rerank, and lv... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
83640aa2-50c2-4b9f-a1ab-f45516da2243 | OPEA Documentation | file://datasets/opea-docs/tutorial/index.rst | unknown | e8e378b0-efe9-4993-b87f-657edbc3df13 | 0 | opea-semantic-v1 | e22aab713be0595d | Tutorials are created to help users learn how to deploy and use OPEA resources quickly.
The following tutorials are provided to cover common use cases: | ai_ref_knowledge | OPEA Documentation | Tutorials are created to help users learn how to deploy and use OPEA resources quickly.
The following tutorials are provided to cover common use cases: | Tutorials are created to help users learn how to deploy and use OPEA resources quickly.
The following tutorials are provided to cover common use cases: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
da5c6c67-e5d9-4f81-9d91-125fe1f14216 | unknown | file:///home/sanjeev/Downloads/depthapi/datasets/30-seconds-of-code/spec/fixtures/content/snippets/js/s/array-grouping.md | unknown | e01450bf-fef4-4d4e-92e6-04b82fe2fa27 | 0 | SemanticChunker@1.0.0 | 3e2d8b6e4cd56358b8d693f7722e4f3377f7de9732a3cb3ae1872dbdd5894e27 | ---
title: Group array elements
test: true
language: javascript
tags: [array,object]
cover: man-cup-laptop
excerpt: Groups the elements of an array based on the given function.
listed: true
dateModified: 2020-10-22
---
Groups the elements of an array based on the given function.
- Use `Array.prototype.map()` to map t... | unknown | unknown | ---
title: Group array elements
test: true
language: javascript
tags: [array,object]
cover: man-cup-laptop
excerpt: Groups the elements of an array based on the given function.
listed: true
dateModified: 2020-10-22
---
Groups the elements of an array based on the given function.
- Use `Array.prototype.map()` to map t... | ---
title: Group array elements
test: true
language: javascript
tags: [array,object]
cover: man-cup-laptop
excerpt: Groups the elements of an array based on the given function.
listed: true
dateModified: 2020-10-22
---
Groups the elements of an array based on the given function.
- Use `Array.prototype.map()` to map t... | ai_ref_knowledge | ||
f5e749e2-ebaf-4408-9f52-85baf19d93c3 | unknown | file:///home/sanjeev/Downloads/depthapi/datasets/30-seconds-of-code/spec/fixtures/content/snippets/js/s/array-initialize.md | unknown | e01450bf-fef4-4d4e-92e6-04b82fe2fa27 | 1 | SemanticChunker@1.0.0 | b83bdabe4cfb9e61888b24f5fa44fdd1f472e3e22453f1fe3bcab3fb68293337 | [Array() constructor]
## Array() constructor
The first thing you'd reach for would probably be the `Array()` constructor.
```js
const arr = Array(3); // [ , , ] - 3 empty slots
arr.map(() => 1); // [ , , ] - map() skips empty slots
arr.map((_, i) => i); // [ , , ] - map() skips empty slots
arr[0]; // undefined - act... | unknown | unknown | [Array() constructor]
## Array() constructor
The first thing you'd reach for would probably be the `Array()` constructor.
```js
const arr = Array(3); // [ , , ] - 3 empty slots
arr.map(() => 1); // [ , , ] - map() skips empty slots
arr.map((_, i) => i); // [ , , ] - map() skips empty slots
arr[0]; // undefined - act... | [Array() constructor]
## Array() constructor
The first thing you'd reach for would probably be the `Array()` constructor.
```js
const arr = Array(3); // [ , , ] - 3 empty slots
arr.map(() => 1); // [ , , ] - map() skips empty slots
arr.map((_, i) => i); // [ , , ] - map() skips empty slots
arr[0]; // undefined - act... | ai_ref_knowledge | ||
97e1e76f-8912-4b1b-a382-3e9077133f8b | unknown | file:///home/sanjeev/Downloads/depthapi/datasets/30-seconds-of-code/spec/fixtures/content/snippets/js/s/array-initialize.md | unknown | e01450bf-fef4-4d4e-92e6-04b82fe2fa27 | 3 | SemanticChunker@1.0.0 | 40c919b683f8996d6f791d9bfcd53a055b2d72e72892c06c753fe0ef1c6be9d7 | [Array() constructor > Array.prototype.fill()]
## Array.prototype.fill()
While `Array.from()` is quite flexible, using a mapping function to fill it with the same value isn't particularly efficient, so use `Array.prototype.fill()`.
```js
const nullArr = new Array(3).fill(null); // [null, null, null]
const staticArr ... | unknown | unknown | [Array() constructor > Array.prototype.fill()]
## Array.prototype.fill()
While `Array.from()` is quite flexible, using a mapping function to fill it with the same value isn't particularly efficient, so use `Array.prototype.fill()`.
```js
const nullArr = new Array(3).fill(null); // [null, null, null]
const staticArr ... | [Array() constructor > Array.prototype.fill()]
## Array.prototype.fill()
While `Array.from()` is quite flexible, using a mapping function to fill it with the same value isn't particularly efficient, so use `Array.prototype.fill()`.
```js
const nullArr = new Array(3).fill(null); // [null, null, null]
const staticArr ... | ai_ref_knowledge | ||
d6795313-dff9-4177-921e-7ade4a55358c | unknown | file:///home/sanjeev/Downloads/depthapi/datasets/30-seconds-of-code/spec/fixtures/content/snippets/js/s/array-initialize.md | unknown | e01450bf-fef4-4d4e-92e6-04b82fe2fa27 | 2 | SemanticChunker@1.0.0 | b72e6fac76bf000ed55d8c447a5d455c33b6fc6364c16e1356f4218549e64a2a | [Array() constructor > Array.from()]
## Array.from()
`Array.from()` is a static method that creates a new, shallow-copied Array instance from an **array-like or iterable object**.
```js
const arr = Array.from({ length: 3 }); // [undefined, undefined, undefined]
arr.map(() => 1); // [1, 1, 1]
arr.map((_, i) => i); //... | unknown | unknown | [Array() constructor > Array.from()]
## Array.from()
`Array.from()` is a static method that creates a new, shallow-copied Array instance from an **array-like or iterable object**.
```js
const arr = Array.from({ length: 3 }); // [undefined, undefined, undefined]
arr.map(() => 1); // [1, 1, 1]
arr.map((_, i) => i); //... | [Array() constructor > Array.from()]
## Array.from()
`Array.from()` is a static method that creates a new, shallow-copied Array instance from an **array-like or iterable object**.
```js
const arr = Array.from({ length: 3 }); // [undefined, undefined, undefined]
arr.map(() => 1); // [1, 1, 1]
arr.map((_, i) => i); //... | ai_ref_knowledge | ||
0c9fabd5-e85d-49b2-b9df-b8b6ea76a27f | unknown | file:///home/sanjeev/Downloads/depthapi/datasets/30-seconds-of-code/spec/fixtures/content/snippets/css/s/content-centering.md | unknown | e01450bf-fef4-4d4e-92e6-04b82fe2fa27 | 4 | SemanticChunker@1.0.0 | 9b463c29c37468421d4ac5f42d40ba6a18e5426d9bef4415be37e673d96c09c6 | [Flexbox centering > Table centering]
## Table centering
Last but not least, **table** centering is an older technique which you might favor when working with **older browsers**.
```html
<div class="table-centering">
<div class="content">Content</div>
</div>
```
```css
.table-centering {
display: table;
height: ... | unknown | unknown | [Flexbox centering > Table centering]
## Table centering
Last but not least, **table** centering is an older technique which you might favor when working with **older browsers**.
```html
<div class="table-centering">
<div class="content">Content</div>
</div>
```
```css
.table-centering {
display: table;
height: ... | [Flexbox centering > Table centering]
## Table centering
Last but not least, **table** centering is an older technique which you might favor when working with **older browsers**.
```html
<div class="table-centering">
<div class="content">Content</div>
</div>
```
```css
.table-centering {
display: table;
height: ... | ai_ref_knowledge | ||
0d31e57b-27ff-4806-b661-6f819fafafa0 | unknown | file:///home/sanjeev/Downloads/depthapi/datasets/30-seconds-of-code/content/snippets/react/s/window-global-environment-ssr-hooks.md | unknown | e01450bf-fef4-4d4e-92e6-04b82fe2fa27 | 7 | SemanticChunker@1.0.0 | 18fae72ebf5b3c62fc44da5ef114cad5219dbc03b4a4235d332a94f964eedbcb | [`useUnload` hook > `useSSR` hook]
## `useSSR` hook
As we've already touched upon the subject of **server-side rendering** (SSR), it's worth mentioning the `useSSR` hook. This hook can be used to check if the code is running on the browser or the server.
To implement this hook, you'll need to check if `Window`, `Win... | unknown | unknown | [`useUnload` hook > `useSSR` hook]
## `useSSR` hook
As we've already touched upon the subject of **server-side rendering** (SSR), it's worth mentioning the `useSSR` hook. This hook can be used to check if the code is running on the browser or the server.
To implement this hook, you'll need to check if `Window`, `Win... | [`useUnload` hook > `useSSR` hook]
## `useSSR` hook
As we've already touched upon the subject of **server-side rendering** (SSR), it's worth mentioning the `useSSR` hook. This hook can be used to check if the code is running on the browser or the server.
To implement this hook, you'll need to check if `Window`, `Win... | ai_ref_knowledge | ||
35f0b893-a587-4632-bd05-02b58cf59988 | unknown | file:///home/sanjeev/Downloads/depthapi/datasets/30-seconds-of-code/content/snippets/react/s/window-global-environment-ssr-hooks.md | unknown | e01450bf-fef4-4d4e-92e6-04b82fe2fa27 | 8 | SemanticChunker@1.0.0 | d9423d9bd2ae0c057a0f5e1c055917568593f5e96673fea8546da9383e34188d | [`useUnload` hook > `useIsomorphicEffect` hook]
## `useIsomorphicEffect` hook
A further use-case for handling window and environment events is the `useIsomorphicEffect` hook. This hook resolves to `useEffect()` on the server and `useLayoutEffect()` on the client.
To implement this hook, use `typeof` to check if the ... | unknown | unknown | [`useUnload` hook > `useIsomorphicEffect` hook]
## `useIsomorphicEffect` hook
A further use-case for handling window and environment events is the `useIsomorphicEffect` hook. This hook resolves to `useEffect()` on the server and `useLayoutEffect()` on the client.
To implement this hook, use `typeof` to check if the ... | [`useUnload` hook > `useIsomorphicEffect` hook]
## `useIsomorphicEffect` hook
A further use-case for handling window and environment events is the `useIsomorphicEffect` hook. This hook resolves to `useEffect()` on the server and `useLayoutEffect()` on the client.
To implement this hook, use `typeof` to check if the ... | ai_ref_knowledge | ||
3e1bd30b-2ec0-4759-bad6-8e15268702a1 | unknown | file:///home/sanjeev/Downloads/depthapi/datasets/30-seconds-of-code/content/snippets/react/s/rendering.md | unknown | e01450bf-fef4-4d4e-92e6-04b82fe2fa27 | 9 | SemanticChunker@1.0.0 | 630aa8776d154911360333cafb27757e57d1ef00e51cbc1c2c513a6757ac7b7a | [Rendering introduction > Context API]
## Context API
React's **Context API** provides a way to pass data through the component tree without using `props`, but should not be used for state management as it requires manual updating. Any component inside a context's `Provider` can access the data in the context instanc... | unknown | unknown | [Rendering introduction > Context API]
## Context API
React's **Context API** provides a way to pass data through the component tree without using `props`, but should not be used for state management as it requires manual updating. Any component inside a context's `Provider` can access the data in the context instanc... | [Rendering introduction > Context API]
## Context API
React's **Context API** provides a way to pass data through the component tree without using `props`, but should not be used for state management as it requires manual updating. Any component inside a context's `Provider` can access the data in the context instanc... | ai_ref_knowledge | ||
418d43f4-c4d9-4bbe-8580-b169bdf14301 | unknown | file:///home/sanjeev/Downloads/depthapi/datasets/30-seconds-of-code/content/snippets/react/s/window-global-environment-ssr-hooks.md | unknown | e01450bf-fef4-4d4e-92e6-04b82fe2fa27 | 5 | SemanticChunker@1.0.0 | 16bc213cacc4769e435e4b306ec956643ce3479bb9484274d399fed4018e7f99 | [`useUnload` hook > `useOnWindowScroll` hook]
## `useOnWindowScroll` hook
Similar to the `useOnWindowResize` hook, the `useOnWindowScroll` hook executes a callback whenever the **window is scrolled**.
The implementation is virtually identical to the previous hook, except that it listens for the `'scroll'` event inst... | unknown | unknown | [`useUnload` hook > `useOnWindowScroll` hook]
## `useOnWindowScroll` hook
Similar to the `useOnWindowResize` hook, the `useOnWindowScroll` hook executes a callback whenever the **window is scrolled**.
The implementation is virtually identical to the previous hook, except that it listens for the `'scroll'` event inst... | [`useUnload` hook > `useOnWindowScroll` hook]
## `useOnWindowScroll` hook
Similar to the `useOnWindowResize` hook, the `useOnWindowScroll` hook executes a callback whenever the **window is scrolled**.
The implementation is virtually identical to the previous hook, except that it listens for the `'scroll'` event inst... | ai_ref_knowledge | ||
c5a07f3a-1bda-4853-8f3f-b548fd939b87 | unknown | file:///home/sanjeev/Downloads/depthapi/datasets/30-seconds-of-code/content/snippets/react/s/window-global-environment-ssr-hooks.md | unknown | e01450bf-fef4-4d4e-92e6-04b82fe2fa27 | 6 | SemanticChunker@1.0.0 | 9937d63354d406a3283495cf1c329c04464821c05138c3ee054a6ab63e16b2c9 | [`useUnload` hook > `useMediaQuery` hook]
## `useMediaQuery` hook
Speaking of responsive design, the `useMediaQuery` hook can be used to check if the current environment matches a given **media query** and return the appropriate value.
In order to implement this hook, you'll need to check if `Window` and `Window.mat... | unknown | unknown | [`useUnload` hook > `useMediaQuery` hook]
## `useMediaQuery` hook
Speaking of responsive design, the `useMediaQuery` hook can be used to check if the current environment matches a given **media query** and return the appropriate value.
In order to implement this hook, you'll need to check if `Window` and `Window.mat... | [`useUnload` hook > `useMediaQuery` hook]
## `useMediaQuery` hook
Speaking of responsive design, the `useMediaQuery` hook can be used to check if the current environment matches a given **media query** and return the appropriate value.
In order to implement this hook, you'll need to check if `Window` and `Window.mat... | ai_ref_knowledge | ||
d637d591-33e7-4d7e-922b-fd2bbdf27ca8 | unknown | file:///home/sanjeev/Downloads/depthapi/datasets/30-seconds-of-code/content/snippets/react/s/rendering.md | unknown | e01450bf-fef4-4d4e-92e6-04b82fe2fa27 | 10 | SemanticChunker@1.0.0 | f6bc68dcf18d52929fb28ae8f1979b8fc875985c67a3fe56b8890a795f15d1da | [Rendering introduction > Context optimization]
## Context optimization
By default, any update to a parent component that renders a context `Provider` will cause all of the child components to re-render regardless of changes in the context, due to React's rendering process. To avoid re-rendering child components when... | unknown | unknown | [Rendering introduction > Context optimization]
## Context optimization
By default, any update to a parent component that renders a context `Provider` will cause all of the child components to re-render regardless of changes in the context, due to React's rendering process. To avoid re-rendering child components when... | [Rendering introduction > Context optimization]
## Context optimization
By default, any update to a parent component that renders a context `Provider` will cause all of the child components to re-render regardless of changes in the context, due to React's rendering process. To avoid re-rendering child components when... | ai_ref_knowledge | ||
2a00981b-b87c-43d4-8a23-0ecf0d282a58 | unknown | file:///home/sanjeev/Downloads/depthapi/datasets/30-seconds-of-code/content/snippets/react/s/rendering.md | unknown | e01450bf-fef4-4d4e-92e6-04b82fe2fa27 | 12 | SemanticChunker@1.0.0 | 29fe018bcb08dd9c59d4d5800f5f642759fb571dd4d2b4586b6cb60fb8e8fdd7 | [Rendering introduction > React-Redux optimization]
## React-Redux optimization
React-Redux provides two ways of connecting to its store, performing the necessary work and returning the combined `props`:
- `connect` (any component): Higher-order component (HOC) that wraps any given component
- `useSelector` (functio... | unknown | unknown | [Rendering introduction > React-Redux optimization]
## React-Redux optimization
React-Redux provides two ways of connecting to its store, performing the necessary work and returning the combined `props`:
- `connect` (any component): Higher-order component (HOC) that wraps any given component
- `useSelector` (functio... | [Rendering introduction > React-Redux optimization]
## React-Redux optimization
React-Redux provides two ways of connecting to its store, performing the necessary work and returning the combined `props`:
- `connect` (any component): Higher-order component (HOC) that wraps any given component
- `useSelector` (functio... | ai_ref_knowledge |
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