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c5e872b1-82ba-4e64-bb9a-7d44eb98d3ca | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/AudioQnA_Guide.rst | unknown | bc21d3d3-6dda-44c6-a2fc-8ca0a8d4ee31 | 7 | opea-semantic-v1 | 906aaa468a2b44a2 | is implemented using the component-level microservices defined in `GenAI Components <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 | is implemented using the component-level microservices defined in `GenAI Components <https://github.com/opea-project/GenAIComps>`. The flow chart below shows the information flow between different microservices for this example.
.. mermaid:: | is implemented using the component-level microservices defined in `GenAI Components <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 | |
d8d28f6a-bb4c-4a3a-8f58-6ee0b46b7530 | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/AudioQnA_Guide.rst | unknown | bc21d3d3-6dda-44c6-a2fc-8ca0a8d4ee31 | 3 | opea-semantic-v1 | 86db61b5d5af3918 | LLMs**: AudioAnA is to develop an innovative voice-to-text-to-LLM-to-text-to-voice conversational system that leverages advanced language models to facilitate seamless and natural communication between humans and machines.
Key Implementation Details
************************** | ai_ref_knowledge | OPEA Documentation | LLMs**: AudioAnA is to develop an innovative voice-to-text-to-LLM-to-text-to-voice conversational system that leverages advanced language models to facilitate seamless and natural communication between humans and machines.
Key Implementation Details
************************** | LLMs**: AudioAnA is to develop an innovative voice-to-text-to-LLM-to-text-to-voice conversational system that leverages advanced language models to facilitate seamless and natural communication between humans and machines.
Key Implementation Details
************************** | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f7a52798-7043-4a21-8c44-603bcfd76557 | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/AudioQnA_Guide.rst | unknown | bc21d3d3-6dda-44c6-a2fc-8ca0a8d4ee31 | 12 | opea-semantic-v1 | b7b7df219b73087c | %% Questions interaction direction LR a[User Audio Query] --> UI UI --> GW GW <==> AudioQnA-MegaService ASR ==> LLM LLM ==> TTS
%% Embedding service flow
direction LR
ASR <-.-> WSP_SRV
LLM <-.-> LLM_gen
TTS <-.-> SPC_SRV | ai_ref_knowledge | OPEA Documentation | %% Questions interaction direction LR a[User Audio Query] --> UI UI --> GW GW <==> AudioQnA-MegaService ASR ==> LLM LLM ==> TTS
%% Embedding service flow
direction LR
ASR <-.-> WSP_SRV
LLM <-.-> LLM_gen
TTS <-.-> SPC_SRV | %% Questions interaction direction LR a[User Audio Query] --> UI UI --> GW GW <==> AudioQnA-MegaService ASR ==> LLM LLM ==> TTS
%% Embedding service flow
direction LR
ASR <-.-> WSP_SRV
LLM <-.-> LLM_gen
TTS <-.-> SPC_SRV | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
03f411f0-6fa2-4777-8b76-7d9e7114dd84 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 44 | opea-semantic-v1 | 460fbe6050a78ca8 | Prometheus is a tool used for recording real-time metrics and is specifically designed for monitoring microservices and alerting based on their metrics.
The `/metrics` endpoint on the port running each microservice exposes the metrics in the Prometheus format. The Prometheus server scrapes these metrics and stores them... | ai_ref_knowledge | OPEA Documentation | Prometheus is a tool used for recording real-time metrics and is specifically designed for monitoring microservices and alerting based on their metrics.
The `/metrics` endpoint on the port running each microservice exposes the metrics in the Prometheus format. The Prometheus server scrapes these metrics and stores them... | Prometheus is a tool used for recording real-time metrics and is specifically designed for monitoring microservices and alerting based on their metrics.
The `/metrics` endpoint on the port running each microservice exposes the metrics in the Prometheus format. The Prometheus server scrapes these metrics and stores them... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0ef6d898-9bc0-4771-bde8-85d6001349ba | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 53 | opea-semantic-v1 | e4053aec12dbf77f | Here is another example of exporting metrics data from a TGI microservice (inside a Kubernetes cluster) to Prometheus:
scrape_configs:
- job_name: "tgi" | ai_ref_knowledge | OPEA Documentation | Here is another example of exporting metrics data from a TGI microservice (inside a Kubernetes cluster) to Prometheus:
scrape_configs:
- job_name: "tgi" | Here is another example of exporting metrics data from a TGI microservice (inside a Kubernetes cluster) to Prometheus:
scrape_configs:
- job_name: "tgi" | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
132cf209-f8fb-4991-a204-c614675f5bf0 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 51 | opea-semantic-v1 | 13ff79880a593370 | Here it's Prometheus itself. scrape_configs: # The job name is added as a label `job=<job_name>` to any timeseries scraped from this config. - job_name: "tgi"
# metrics_path defaults to '/metrics'
# scheme defaults to 'http'. | ai_ref_knowledge | OPEA Documentation | Here it's Prometheus itself. scrape_configs: # The job name is added as a label `job=<job_name>` to any timeseries scraped from this config. - job_name: "tgi"
# metrics_path defaults to '/metrics'
# scheme defaults to 'http'. | Here it's Prometheus itself. scrape_configs: # The job name is added as a label `job=<job_name>` to any timeseries scraped from this config. - job_name: "tgi"
# metrics_path defaults to '/metrics'
# scheme defaults to 'http'. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1891b38c-3945-4b7d-bdba-96c365903bee | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 72 | opea-semantic-v1 | 27284bd08357335a | Incoming requests to the microservice, the response time per token, etc., can also be monitored in real time.
Summary and Next Steps | ai_ref_knowledge | OPEA Documentation | Incoming requests to the microservice, the response time per token, etc., can also be monitored in real time.
Summary and Next Steps | Incoming requests to the microservice, the response time per token, etc., can also be monitored in real time.
Summary and Next Steps | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1960a37d-bc7a-4a2a-b01e-e3c1cdfbd6d0 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 56 | opea-semantic-v1 | 5331afcdacbbdcc9 | http://localhost:9090/targets?search=
>Note: Before starting Prometheus, ensure that no other processes are running on the designated port (default is 9090). Otherwise, Prometheus will not be able to scrape the metrics. | ai_ref_knowledge | OPEA Documentation | http://localhost:9090/targets?search=
>Note: Before starting Prometheus, ensure that no other processes are running on the designated port (default is 9090). Otherwise, Prometheus will not be able to scrape the metrics. | http://localhost:9090/targets?search=
>Note: Before starting Prometheus, ensure that no other processes are running on the designated port (default is 9090). Otherwise, Prometheus will not be able to scrape the metrics. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1a890f63-def5-47f0-9544-47e8f2d8dd06 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 66 | opea-semantic-v1 | 576a3f647c27bcc0 | >Note: Before starting Grafana, ensure that no other processes are running on port 3000.
Log in to Grafana using the default credentials: | ai_ref_knowledge | OPEA Documentation | >Note: Before starting Grafana, ensure that no other processes are running on port 3000.
Log in to Grafana using the default credentials: | >Note: Before starting Grafana, ensure that no other processes are running on port 3000.
Log in to Grafana using the default credentials: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1dc96839-d9dc-4f00-b68e-c23072ffba7d | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 19 | opea-semantic-v1 | c9f1d74d4b88c1d6 | Adding a new VectorDB to OPEA involves minimal changes to OPEA sub-project `GenAI Components <https://github.com/opea-project/GenAIComps>`_ that covers installation, launch, usage, and tests.
For more details, please refer to the following document: | ai_ref_knowledge | OPEA Documentation | Adding a new VectorDB to OPEA involves minimal changes to OPEA sub-project `GenAI Components <https://github.com/opea-project/GenAIComps>`_ that covers installation, launch, usage, and tests.
For more details, please refer to the following document: | Adding a new VectorDB to OPEA involves minimal changes to OPEA sub-project `GenAI Components <https://github.com/opea-project/GenAIComps>`_ that covers installation, launch, usage, and tests.
For more details, please refer to the following document: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
213e5818-68d9-415d-a335-31c74faa525e | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 30 | opea-semantic-v1 | 768beab75e556794 | subgraph User Interface direction TB a[User Input Query] Ingest[Ingest data] UI[UI server<br>Port: 5173] end
subgraph ChatQnA GateWay
direction LR
GW[ChatQnA GateWay<br>Port: 8888]
end | ai_ref_knowledge | OPEA Documentation | subgraph User Interface direction TB a[User Input Query] Ingest[Ingest data] UI[UI server<br>Port: 5173] end
subgraph ChatQnA GateWay
direction LR
GW[ChatQnA GateWay<br>Port: 8888]
end | subgraph User Interface direction TB a[User Input Query] Ingest[Ingest data] UI[UI server<br>Port: 5173] end
subgraph ChatQnA GateWay
direction LR
GW[ChatQnA GateWay<br>Port: 8888]
end | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
23ed97c5-ef96-49d9-bcd4-c3f0867d6323 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 64 | opea-semantic-v1 | c2edee9fdb51de90 | nohup ./bin/grafana-server &
3. Access the Grafana dashboard UI:
On a web browser, access the Grafana dashboard UI at the following URL: | ai_ref_knowledge | OPEA Documentation | nohup ./bin/grafana-server &
3. Access the Grafana dashboard UI:
On a web browser, access the Grafana dashboard UI at the following URL: | nohup ./bin/grafana-server &
3. Access the Grafana dashboard UI:
On a web browser, access the Grafana dashboard UI at the following URL: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
2558b572-eeeb-4391-9df5-f1869ec86227 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 11 | opea-semantic-v1 | d781cb837beb2da6 | How It Works ************
The ChatQnA Examples follows a basic flow of information in the chatbot system,
starting from the user input and going through the retrieve, re-ranker, and
generate components, ultimately resulting in the bot's output. | ai_ref_knowledge | OPEA Documentation | How It Works ************
The ChatQnA Examples follows a basic flow of information in the chatbot system,
starting from the user input and going through the retrieve, re-ranker, and
generate components, ultimately resulting in the bot's output. | How It Works ************
The ChatQnA Examples follows a basic flow of information in the chatbot system,
starting from the user input and going through the retrieve, re-ranker, and
generate components, ultimately resulting in the bot's output. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
29cbaa1a-40c2-4003-896b-458f7f538ee5 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 21 | opea-semantic-v1 | 285272c28532fc4c | used to ensure the megaservice is working properly. The example below assumes a document containing new information is uploaded to the vector database before querying.
curl http://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{
"messages": "What is the revenue of Nike in 2023?"
}' | ai_ref_knowledge | OPEA Documentation | used to ensure the megaservice is working properly. The example below assumes a document containing new information is uploaded to the vector database before querying.
curl http://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{
"messages": "What is the revenue of Nike in 2023?"
}' | used to ensure the megaservice is working properly. The example below assumes a document containing new information is uploaded to the vector database before querying.
curl http://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{
"messages": "What is the revenue of Nike in 2023?"
}' | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
2c854869-3423-4d17-be3a-b64018287bcc | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 20 | opea-semantic-v1 | 7bb1b05b4ef6e480 | Expected Output
After launching the ChatQnA application, a curl command can be used to ensure the
megaservice is working properly. The example below assumes a document containing
new information is uploaded to the vector database before querying. | ai_ref_knowledge | OPEA Documentation | Expected Output
After launching the ChatQnA application, a curl command can be used to ensure the
megaservice is working properly. The example below assumes a document containing
new information is uploaded to the vector database before querying. | Expected Output
After launching the ChatQnA application, a curl command can be used to ensure the
megaservice is working properly. The example below assumes a document containing
new information is uploaded to the vector database before querying. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
306b3838-b108-4a29-b3a2-af661c4f95bb | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 62 | opea-semantic-v1 | db676bb9fe9cb380 | 1. Download Grafana: Download the Grafana v8.0.6 from the official site, and extract the files:
wget https://dl.grafana.com/oss/release/grafana-11.0.0.linux-amd64.tar.gz
tar -zxvf grafana-11.0.0.linux-amd64.tar.gz | ai_ref_knowledge | OPEA Documentation | 1. Download Grafana: Download the Grafana v8.0.6 from the official site, and extract the files:
wget https://dl.grafana.com/oss/release/grafana-11.0.0.linux-amd64.tar.gz
tar -zxvf grafana-11.0.0.linux-amd64.tar.gz | 1. Download Grafana: Download the Grafana v8.0.6 from the official site, and extract the files:
wget https://dl.grafana.com/oss/release/grafana-11.0.0.linux-amd64.tar.gz
tar -zxvf grafana-11.0.0.linux-amd64.tar.gz | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
30be007f-57e0-4298-90fb-9fb076e55403 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 31 | opea-semantic-v1 | cb43f053d53535cd | subgraph ChatQnA GateWay direction LR GW[ChatQnA GateWay<br>Port: 8888] end
%% Data Preparation flow
%% Ingest data flow
direction LR
Ingest[Ingest data] -->|a| UI
UI -->|b| DP
DP -.->|c| TEI_EM | ai_ref_knowledge | OPEA Documentation | subgraph ChatQnA GateWay direction LR GW[ChatQnA GateWay<br>Port: 8888] end
%% Data Preparation flow
%% Ingest data flow
direction LR
Ingest[Ingest data] -->|a| UI
UI -->|b| DP
DP -.->|c| TEI_EM | subgraph ChatQnA GateWay direction LR GW[ChatQnA GateWay<br>Port: 8888] end
%% Data Preparation flow
%% Ingest data flow
direction LR
Ingest[Ingest data] -->|a| UI
UI -->|b| DP
DP -.->|c| TEI_EM | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
33f380c8-ab0a-4ae2-8994-3066bc9f7f5e | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 69 | opea-semantic-v1 | d7436f213560108c | the dashboard's configuration. Upload it in the Grafana UI under ``Home > Dashboards > Import dashboard``. A sample JSON file is supported here: `tgi_grafana.json <https://github.com/huggingface/text-generation-inference/blob/main/assets/tgi_grafana.json>`_
5. View the dashboard:
Finally, open the dashboard in the Gra... | ai_ref_knowledge | OPEA Documentation | the dashboard's configuration. Upload it in the Grafana UI under ``Home > Dashboards > Import dashboard``. A sample JSON file is supported here: `tgi_grafana.json <https://github.com/huggingface/text-generation-inference/blob/main/assets/tgi_grafana.json>`_
5. View the dashboard:
Finally, open the dashboard in the Gra... | the dashboard's configuration. Upload it in the Grafana UI under ``Home > Dashboards > Import dashboard``. A sample JSON file is supported here: `tgi_grafana.json <https://github.com/huggingface/text-generation-inference/blob/main/assets/tgi_grafana.json>`_
5. View the dashboard:
Finally, open the dashboard in the Gra... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4635714e-be8c-499f-ac80-ab814a79d10d | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 42 | opea-semantic-v1 | 9bcecef38dd50090 | **Grafana**, both open-source toolkits, are used to collect metrics including latency and throughput of different microservices in real time, and visualize them in a dashboard.
Set Up the Prometheus Server | ai_ref_knowledge | OPEA Documentation | **Grafana**, both open-source toolkits, are used to collect metrics including latency and throughput of different microservices in real time, and visualize them in a dashboard.
Set Up the Prometheus Server | **Grafana**, both open-source toolkits, are used to collect metrics including latency and throughput of different microservices in real time, and visualize them in a dashboard.
Set Up the Prometheus Server | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
494c81cd-7e76-4872-ae56-5506d7efcc4b | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 50 | opea-semantic-v1 | b9f9c32c6e35d5fc | Here is an example of exporting metrics data from a TGI microservice to Prometheus:
# A scrape configuration containing exactly one endpoint to scrape:
# Here it's Prometheus itself. scrape_configs:
# The job name is added as a label `job=<job_name>` to any timeseries scraped from this config. - job_name: "tgi" | ai_ref_knowledge | OPEA Documentation | Here is an example of exporting metrics data from a TGI microservice to Prometheus:
# A scrape configuration containing exactly one endpoint to scrape:
# Here it's Prometheus itself. scrape_configs:
# The job name is added as a label `job=<job_name>` to any timeseries scraped from this config. - job_name: "tgi" | Here is an example of exporting metrics data from a TGI microservice to Prometheus:
# A scrape configuration containing exactly one endpoint to scrape:
# Here it's Prometheus itself. scrape_configs:
# The job name is added as a label `job=<job_name>` to any timeseries scraped from this config. - job_name: "tgi" | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
541fcc9c-77b3-4e1a-8679-26c82e7e48d8 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 4 | opea-semantic-v1 | dbf128710a00a6c6 | 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.
Central to the RAG architecture is the use of a generative model, which is
responsible for generating responses to user queries. The generative model ... | 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.
Central to the RAG architecture is the use of a generative model, which is
responsible for generating responses to user queries. The generative model ... | 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.
Central to the RAG architecture is the use of a generative model, which is
responsible for generating responses to user queries. The generative model ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
54b35589-a677-4bad-b6c9-54c9dc294d3e | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 34 | opea-semantic-v1 | 8e42b300bd41e96c | %% Embedding service flow direction TB EM -.->|3'| TEI_EM RET -.->|4'| TEI_EM RER -.->|5'| TEI_RER LLM -.->|6'| LLM_gen
subgraph Legend
X([Microservice])
Y{{Service from industry peers}}
Z[Gateway]
end | ai_ref_knowledge | OPEA Documentation | %% Embedding service flow direction TB EM -.->|3'| TEI_EM RET -.->|4'| TEI_EM RER -.->|5'| TEI_RER LLM -.->|6'| LLM_gen
subgraph Legend
X([Microservice])
Y{{Service from industry peers}}
Z[Gateway]
end | %% Embedding service flow direction TB EM -.->|3'| TEI_EM RET -.->|4'| TEI_EM RER -.->|5'| TEI_RER LLM -.->|6'| LLM_gen
subgraph Legend
X([Microservice])
Y{{Service from industry peers}}
Z[Gateway]
end | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
556d1c6f-cc5a-488e-8b64-357d78d53fbb | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 26 | opea-semantic-v1 | 81fca1b79956c968 | interface for users to access. The gateway routes incoming requests to the appropriate microservices within the megaservice architecture. See `GenAI Components <https://github.com/opea-project/GenAIComps>`_ for more information.
.. mermaid:: | ai_ref_knowledge | OPEA Documentation | interface for users to access. The gateway routes incoming requests to the appropriate microservices within the megaservice architecture. See `GenAI Components <https://github.com/opea-project/GenAIComps>`_ for more information.
.. mermaid:: | interface for users to access. The gateway routes incoming requests to the appropriate microservices within the megaservice architecture. See `GenAI Components <https://github.com/opea-project/GenAIComps>`_ for more information.
.. mermaid:: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
58bdd191-b046-43ab-9a67-d1a2ee94a590 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 48 | opea-semantic-v1 | 9a8bffa3de17993a | vim prometheus.yml
Change the ``job_name`` to the name of the microservice to monitor. Also change the ``targets`` to the job target endpoint of that microservice. Make sure the service is running and the port is open, and that it exposes the metrics that follow Prometheus convention at the ``/metrics`` endpoint. | ai_ref_knowledge | OPEA Documentation | vim prometheus.yml
Change the ``job_name`` to the name of the microservice to monitor. Also change the ``targets`` to the job target endpoint of that microservice. Make sure the service is running and the port is open, and that it exposes the metrics that follow Prometheus convention at the ``/metrics`` endpoint. | vim prometheus.yml
Change the ``job_name`` to the name of the microservice to monitor. Also change the ``targets`` to the job target endpoint of that microservice. Make sure the service is running and the port is open, and that it exposes the metrics that follow Prometheus convention at the ``/metrics`` endpoint. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5a3b25d1-b66c-4470-acbe-064e0f7d717f | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 2 | opea-semantic-v1 | d0fcc1f3eaee9aec | Purpose *******
The ChatQnA example uses retrieval augmented generation (RAG) architecture,
which is quickly becoming the industry standard for chatbot development. It
combines the benefits of a knowledge base (via a vector store) and generative
models to reduce hallucinations, maintain up-to-date information, and leve... | ai_ref_knowledge | OPEA Documentation | Purpose *******
The ChatQnA example uses retrieval augmented generation (RAG) architecture,
which is quickly becoming the industry standard for chatbot development. It
combines the benefits of a knowledge base (via a vector store) and generative
models to reduce hallucinations, maintain up-to-date information, and leve... | Purpose *******
The ChatQnA example uses retrieval augmented generation (RAG) architecture,
which is quickly becoming the industry standard for chatbot development. It
combines the benefits of a knowledge base (via a vector store) and generative
models to reduce hallucinations, maintain up-to-date information, and leve... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5b438e17-b266-4e4d-8aac-b98f4acf0e88 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 57 | opea-semantic-v1 | 5239d62526663d1d | Prometheus, ensure that no other processes are running on the designated port (default is 9090). Otherwise, Prometheus will not be able to scrape the metrics.
On the Prometheus UI, look at the status of the targets and the metrics that are being scraped. To search for a metrics variable, type it in the search bar. | ai_ref_knowledge | OPEA Documentation | Prometheus, ensure that no other processes are running on the designated port (default is 9090). Otherwise, Prometheus will not be able to scrape the metrics.
On the Prometheus UI, look at the status of the targets and the metrics that are being scraped. To search for a metrics variable, type it in the search bar. | Prometheus, ensure that no other processes are running on the designated port (default is 9090). Otherwise, Prometheus will not be able to scrape the metrics.
On the Prometheus UI, look at the status of the targets and the metrics that are being scraped. To search for a metrics variable, type it in the search bar. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5bcb1d4f-bb60-4216-aaeb-784fd1b84fb6 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 5 | opea-semantic-v1 | 7f6ac03bb88ca9bd | use cases and requirements. By combining the generative model with the vector database, RAG can provide accurate and contextually relevant responses specific to users' queries.
The ChatQnA example is designed to be a simple, yet powerful, demonstration of
the RAG architecture. It is a great starting point for developer... | ai_ref_knowledge | OPEA Documentation | use cases and requirements. By combining the generative model with the vector database, RAG can provide accurate and contextually relevant responses specific to users' queries.
The ChatQnA example is designed to be a simple, yet powerful, demonstration of
the RAG architecture. It is a great starting point for developer... | use cases and requirements. By combining the generative model with the vector database, RAG can provide accurate and contextually relevant responses specific to users' queries.
The ChatQnA example is designed to be a simple, yet powerful, demonstration of
the RAG architecture. It is a great starting point for developer... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5bd42eca-9b9a-44e6-8904-4f72ac3c5af1 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 3 | opea-semantic-v1 | f800d71d0811f789 | It combines the benefits of a knowledge base (via a vector store) and generative models to reduce hallucinations, maintain up-to-date information, and leverage domain-specific knowledge.
RAG bridges the knowledge gap by dynamically fetching relevant information from
external sources, ensuring that responses generated r... | ai_ref_knowledge | OPEA Documentation | It combines the benefits of a knowledge base (via a vector store) and generative models to reduce hallucinations, maintain up-to-date information, and leverage domain-specific knowledge.
RAG bridges the knowledge gap by dynamically fetching relevant information from
external sources, ensuring that responses generated r... | It combines the benefits of a knowledge base (via a vector store) and generative models to reduce hallucinations, maintain up-to-date information, and leverage domain-specific knowledge.
RAG bridges the knowledge gap by dynamically fetching relevant information from
external sources, ensuring that responses generated r... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5df906c3-107b-4646-9427-90a876388ed8 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 52 | opea-semantic-v1 | 0e5fc871bf1f0ee2 | static_configs: - targets: ["localhost:9009"]
Here is another example of exporting metrics data from a TGI microservice (inside a Kubernetes cluster) to Prometheus: | ai_ref_knowledge | OPEA Documentation | static_configs: - targets: ["localhost:9009"]
Here is another example of exporting metrics data from a TGI microservice (inside a Kubernetes cluster) to Prometheus: | static_configs: - targets: ["localhost:9009"]
Here is another example of exporting metrics data from a TGI microservice (inside a Kubernetes cluster) to Prometheus: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5e31f38c-861e-47af-afc5-0985984c4792 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 43 | opea-semantic-v1 | c165b5b1e8eeff2d | Set Up the Prometheus Server
Prometheus is a tool used for recording real-time metrics and is specifically designed for monitoring microservices and alerting based on their metrics. | ai_ref_knowledge | OPEA Documentation | Set Up the Prometheus Server
Prometheus is a tool used for recording real-time metrics and is specifically designed for monitoring microservices and alerting based on their metrics. | Set Up the Prometheus Server
Prometheus is a tool used for recording real-time metrics and is specifically designed for monitoring microservices and alerting based on their metrics. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
604e111d-34a4-43f1-8615-fedf26ca70d9 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 13 | opea-semantic-v1 | c45697fe8e41d561 | .. figure:: /GenAIExamples/ChatQnA/assets/img/chatqna_architecture.png
This diagram illustrates the flow of information in the chatbot system,
starting from the user input and going through the retrieve, analyze, and
generate components, ultimately resulting in the bot's output. | ai_ref_knowledge | OPEA Documentation | .. figure:: /GenAIExamples/ChatQnA/assets/img/chatqna_architecture.png
This diagram illustrates the flow of information in the chatbot system,
starting from the user input and going through the retrieve, analyze, and
generate components, ultimately resulting in the bot's output. | .. figure:: /GenAIExamples/ChatQnA/assets/img/chatqna_architecture.png
This diagram illustrates the flow of information in the chatbot system,
starting from the user input and going through the retrieve, analyze, and
generate components, ultimately resulting in the bot's output. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
64336162-e220-4b53-a65c-00764d2952d1 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 61 | opea-semantic-v1 | 39e616f152443eb9 | To set up the Grafana dashboard, follow these steps:
1. Download Grafana:
Download the Grafana v8.0.6 from the official site, and extract the files: | ai_ref_knowledge | OPEA Documentation | To set up the Grafana dashboard, follow these steps:
1. Download Grafana:
Download the Grafana v8.0.6 from the official site, and extract the files: | To set up the Grafana dashboard, follow these steps:
1. Download Grafana:
Download the Grafana v8.0.6 from the official site, and extract the files: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
68d6ad0f-83c5-4f81-be96-475911d75294 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 35 | opea-semantic-v1 | 0998418abb8e31f6 | Deployment **********
Here are some deployment options depending on the hardware and environment. It includes both single-node and orchestrated multi-node configurations. Choose the one that best fits requirements. | ai_ref_knowledge | OPEA Documentation | Deployment **********
Here are some deployment options depending on the hardware and environment. It includes both single-node and orchestrated multi-node configurations. Choose the one that best fits requirements. | Deployment **********
Here are some deployment options depending on the hardware and environment. It includes both single-node and orchestrated multi-node configurations. Choose the one that best fits requirements. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6b84a4ac-b26a-4dc2-8e2e-50113db83fb9 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 27 | opea-semantic-v1 | 3856cb89fdefd66c | .. mermaid::
graph LR
subgraph ChatQnA-MegaService["ChatQnA-MegaService"]
direction LR
EM([Embedding 'LangChain TEI' <br>6000])
RET([Retrieval 'LangChain Redis'<br>7000])
RER([Rerank 'TEI'<br>8000])
LLM([LLM 'text-generation TGI'<br>9000])
end | ai_ref_knowledge | OPEA Documentation | .. mermaid::
graph LR
subgraph ChatQnA-MegaService["ChatQnA-MegaService"]
direction LR
EM([Embedding 'LangChain TEI' <br>6000])
RET([Retrieval 'LangChain Redis'<br>7000])
RER([Rerank 'TEI'<br>8000])
LLM([LLM 'text-generation TGI'<br>9000])
end | .. mermaid::
graph LR
subgraph ChatQnA-MegaService["ChatQnA-MegaService"]
direction LR
EM([Embedding 'LangChain TEI' <br>6000])
RET([Retrieval 'LangChain Redis'<br>7000])
RER([Rerank 'TEI'<br>8000])
LLM([LLM 'text-generation TGI'<br>9000])
end | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6c71802d-cdbc-41a8-b531-490eccd7d5b4 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 73 | opea-semantic-v1 | 12abe59e6713c928 | Summary and Next Steps
The ChatQnA application deploys a RAG architecture consisting of the following microservices -
embedding, vectorDB, retrieval, reranker, and LLM text generation. It is a chatbot that can
leverage new information from uploaded documents and websites to provide more accurate answers. The microser... | ai_ref_knowledge | OPEA Documentation | Summary and Next Steps
The ChatQnA application deploys a RAG architecture consisting of the following microservices -
embedding, vectorDB, retrieval, reranker, and LLM text generation. It is a chatbot that can
leverage new information from uploaded documents and websites to provide more accurate answers. The microser... | Summary and Next Steps
The ChatQnA application deploys a RAG architecture consisting of the following microservices -
embedding, vectorDB, retrieval, reranker, and LLM text generation. It is a chatbot that can
leverage new information from uploaded documents and websites to provide more accurate answers. The microser... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6ffcacf9-5216-43ab-bc13-9f78a000d7be | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 39 | opea-semantic-v1 | 549d79e90b3b39ae | also supports HTTPS. To enable HTTPS, specify the certificate file paths in the MicroService class. For more details, please refer to the `source code <https://github.com/opea-project/GenAIComps/blob/main/comps/cores/mega/micro_service.py#L33>`_.
2. For other troubles, please check the `doc <https://opea-project.github... | ai_ref_knowledge | OPEA Documentation | also supports HTTPS. To enable HTTPS, specify the certificate file paths in the MicroService class. For more details, please refer to the `source code <https://github.com/opea-project/GenAIComps/blob/main/comps/cores/mega/micro_service.py#L33>`_.
2. For other troubles, please check the `doc <https://opea-project.github... | also supports HTTPS. To enable HTTPS, specify the certificate file paths in the MicroService class. For more details, please refer to the `source code <https://github.com/opea-project/GenAIComps/blob/main/comps/cores/mega/micro_service.py#L33>`_.
2. For other troubles, please check the `doc <https://opea-project.github... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
76f26a00-91a8-4966-a723-248541bd8da4 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 7 | opea-semantic-v1 | b2fc89a074efd04f | LLM. Upstream Vanilla Kubernetes or Red Hat OpenShift Container Platform (RHOCP) can be used with or without GMC, while use with GMC provides additional features.
The ChatQnA provides several deployment options, including single-node
deployments on-premise or in a cloud environment using hardware such as Xeon
Scalable ... | ai_ref_knowledge | OPEA Documentation | LLM. Upstream Vanilla Kubernetes or Red Hat OpenShift Container Platform (RHOCP) can be used with or without GMC, while use with GMC provides additional features.
The ChatQnA provides several deployment options, including single-node
deployments on-premise or in a cloud environment using hardware such as Xeon
Scalable ... | LLM. Upstream Vanilla Kubernetes or Red Hat OpenShift Container Platform (RHOCP) can be used with or without GMC, while use with GMC provides additional features.
The ChatQnA provides several deployment options, including single-node
deployments on-premise or in a cloud environment using hardware such as Xeon
Scalable ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
77132500-bf37-48a5-81f2-a64c9cbaa7e9 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 63 | opea-semantic-v1 | cf1b0f085a6afa6d | For additional instructions, see the complete `Grafana installation instructions <https://grafana.com/docs/grafana/latest/setup-grafana/installation/>`_.
2. Run the Grafana server:
Change the directory to the Grafana folder: | ai_ref_knowledge | OPEA Documentation | For additional instructions, see the complete `Grafana installation instructions <https://grafana.com/docs/grafana/latest/setup-grafana/installation/>`_.
2. Run the Grafana server:
Change the directory to the Grafana folder: | For additional instructions, see the complete `Grafana installation instructions <https://grafana.com/docs/grafana/latest/setup-grafana/installation/>`_.
2. Run the Grafana server:
Change the directory to the Grafana folder: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7916f695-08c3-4cf0-97a0-92e8a3a4731e | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 60 | opea-semantic-v1 | c69d11ea1d2766c6 | is a tool used for visualizing metrics and creating dashboards. It can be used to create custom dashboards that display the metrics collected by Prometheus.
To set up the Grafana dashboard, follow these steps: | ai_ref_knowledge | OPEA Documentation | is a tool used for visualizing metrics and creating dashboards. It can be used to create custom dashboards that display the metrics collected by Prometheus.
To set up the Grafana dashboard, follow these steps: | is a tool used for visualizing metrics and creating dashboards. It can be used to create custom dashboards that display the metrics collected by Prometheus.
To set up the Grafana dashboard, follow these steps: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7a9a9843-9a2c-4afe-b27c-1778160b237a | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 24 | opea-semantic-v1 | dbcd16b532c7ddf6 | data: b'ues' data: b' of' data: b' $' data: b'5' data: b'1' data: b'.' data: b'2' data: b' billion' data: b'.' data: b'</s>' data: [DONE]
The UI will show a similar response with formatted output. | ai_ref_knowledge | OPEA Documentation | data: b'ues' data: b' of' data: b' $' data: b'5' data: b'1' data: b'.' data: b'2' data: b' billion' data: b'.' data: b'</s>' data: [DONE]
The UI will show a similar response with formatted output. | data: b'ues' data: b' of' data: b' $' data: b'5' data: b'1' data: b'.' data: b'2' data: b' billion' data: b'.' data: b'</s>' data: [DONE]
The UI will show a similar response with formatted output. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7aa5b85b-8f8a-4193-9647-a4afe86ed54d | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 6 | opea-semantic-v1 | 49e28eb5d60ecd17 | of the RAG architecture. It is a great starting point for developers looking to build chatbots that can provide accurate and up-to-date information to users.
To facilitate sharing of individual services across multiple GenAI applications,
use the GenAI Microservices Connector (GMC) to deploy the application. Apart
fr... | ai_ref_knowledge | OPEA Documentation | of the RAG architecture. It is a great starting point for developers looking to build chatbots that can provide accurate and up-to-date information to users.
To facilitate sharing of individual services across multiple GenAI applications,
use the GenAI Microservices Connector (GMC) to deploy the application. Apart
fr... | of the RAG architecture. It is a great starting point for developers looking to build chatbots that can provide accurate and up-to-date information to users.
To facilitate sharing of individual services across multiple GenAI applications,
use the GenAI Microservices Connector (GMC) to deploy the application. Apart
fr... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7c5ced66-093c-4c95-8abb-bd25fa8be776 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 41 | opea-semantic-v1 | f131dd598c3d14fb | bottlenecks, detect anomalies, and optimize the performance of individual microservices. This helps proactively address any issues and ensure that the ChatQnA pipeline is running efficiently.
**Prometheus** and **Grafana**, both open-source toolkits, are used to collect metrics including latency and throughput of diffe... | ai_ref_knowledge | OPEA Documentation | bottlenecks, detect anomalies, and optimize the performance of individual microservices. This helps proactively address any issues and ensure that the ChatQnA pipeline is running efficiently.
**Prometheus** and **Grafana**, both open-source toolkits, are used to collect metrics including latency and throughput of diffe... | bottlenecks, detect anomalies, and optimize the performance of individual microservices. This helps proactively address any issues and ensure that the ChatQnA pipeline is running efficiently.
**Prometheus** and **Grafana**, both open-source toolkits, are used to collect metrics including latency and throughput of diffe... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7cbfdbea-669f-4019-a508-470956aae6eb | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 16 | opea-semantic-v1 | 3ff844a86037ba26 | Uses a model to rank the retrieved data on their saliency. The vector database retrieves the most relevant data points based on the query embedding.
These data points can include documents,
articles, or any other relevant information that can help generate accurate
responses. #. **LLM**: The retrieved data points are... | ai_ref_knowledge | OPEA Documentation | Uses a model to rank the retrieved data on their saliency. The vector database retrieves the most relevant data points based on the query embedding.
These data points can include documents,
articles, or any other relevant information that can help generate accurate
responses. #. **LLM**: The retrieved data points are... | Uses a model to rank the retrieved data on their saliency. The vector database retrieves the most relevant data points based on the query embedding.
These data points can include documents,
articles, or any other relevant information that can help generate accurate
responses. #. **LLM**: The retrieved data points are... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7d54027b-6694-4a60-9ac9-5768cd225148 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 15 | opea-semantic-v1 | 2edc0fef4f0d2cb1 | The architecture follows a series of steps to process user queries and generate responses:
1. **Embedding**: The user query is first transformed into a numerical
representation called an embedding. This embedding captures the semantic
meaning of the query and allows for efficient comparison with other
embeddings. #.... | ai_ref_knowledge | OPEA Documentation | The architecture follows a series of steps to process user queries and generate responses:
1. **Embedding**: The user query is first transformed into a numerical
representation called an embedding. This embedding captures the semantic
meaning of the query and allows for efficient comparison with other
embeddings. #.... | The architecture follows a series of steps to process user queries and generate responses:
1. **Embedding**: The user query is first transformed into a numerical
representation called an embedding. This embedding captures the semantic
meaning of the query and allows for efficient comparison with other
embeddings. #.... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7ef1864f-ff28-429d-9260-c1977470c344 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 65 | opea-semantic-v1 | d58b0026fa1c3b19 | 3. Access the Grafana dashboard UI: On a web browser, access the Grafana dashboard UI at the following URL:
http://localhost:3000 | ai_ref_knowledge | OPEA Documentation | 3. Access the Grafana dashboard UI: On a web browser, access the Grafana dashboard UI at the following URL:
http://localhost:3000 | 3. Access the Grafana dashboard UI: On a web browser, access the Grafana dashboard UI at the following URL:
http://localhost:3000 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
81111c82-13ce-483b-9f37-a818ffbf0e4c | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 59 | opea-semantic-v1 | 20011a7c0b9ede9c | Set Up the Grafana Dashboard
Grafana is a tool used for visualizing metrics and creating dashboards. It can be used to create custom dashboards that display the metrics collected by Prometheus. | ai_ref_knowledge | OPEA Documentation | Set Up the Grafana Dashboard
Grafana is a tool used for visualizing metrics and creating dashboards. It can be used to create custom dashboards that display the metrics collected by Prometheus. | Set Up the Grafana Dashboard
Grafana is a tool used for visualizing metrics and creating dashboards. It can be used to create custom dashboards that display the metrics collected by Prometheus. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
82aa1ae7-d245-4fbb-a822-79a717f1bab4 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 1 | opea-semantic-v1 | 29532b62327359c8 | the GenAI space. Consider it the “hello world” of GenAI applications and can be leveraged for solutions across wide enterprise verticals, both internally and externally.
Purpose
******* | ai_ref_knowledge | OPEA Documentation | the GenAI space. Consider it the “hello world” of GenAI applications and can be leveraged for solutions across wide enterprise verticals, both internally and externally.
Purpose
******* | the GenAI space. Consider it the “hello world” of GenAI applications and can be leveraged for solutions across wide enterprise verticals, both internally and externally.
Purpose
******* | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
84898a15-7255-484f-98a5-bb45e8263c44 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 49 | opea-semantic-v1 | d0a9dc77f8240d39 | Make sure the service is running and the port is open, and that it exposes the metrics that follow Prometheus convention at the ``/metrics`` endpoint.
Here is an example of exporting metrics data from a TGI microservice to Prometheus: | ai_ref_knowledge | OPEA Documentation | Make sure the service is running and the port is open, and that it exposes the metrics that follow Prometheus convention at the ``/metrics`` endpoint.
Here is an example of exporting metrics data from a TGI microservice to Prometheus: | Make sure the service is running and the port is open, and that it exposes the metrics that follow Prometheus convention at the ``/metrics`` endpoint.
Here is an example of exporting metrics data from a TGI microservice to Prometheus: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8509a36c-f9df-472f-aded-da19b84fb1fc | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 28 | opea-semantic-v1 | e133348f786211b4 | graph LR subgraph ChatQnA-MegaService["ChatQnA-MegaService"] direction LR EM([Embedding 'LangChain TEI' <br>6000]) RET([Retrieval 'LangChain Redis'<br>7000]) RER([Rerank 'TEI'<br>8000]) LLM([LLM 'text-generation TGI'<br>9000]) end
direction TB
TEI_EM{{TEI embedding service<br>8090}}
VDB{{Vector DB<br>8001}}
%% Vecto... | ai_ref_knowledge | OPEA Documentation | graph LR subgraph ChatQnA-MegaService["ChatQnA-MegaService"] direction LR EM([Embedding 'LangChain TEI' <br>6000]) RET([Retrieval 'LangChain Redis'<br>7000]) RER([Rerank 'TEI'<br>8000]) LLM([LLM 'text-generation TGI'<br>9000]) end
direction TB
TEI_EM{{TEI embedding service<br>8090}}
VDB{{Vector DB<br>8001}}
%% Vecto... | graph LR subgraph ChatQnA-MegaService["ChatQnA-MegaService"] direction LR EM([Embedding 'LangChain TEI' <br>6000]) RET([Retrieval 'LangChain Redis'<br>7000]) RER([Rerank 'TEI'<br>8000]) LLM([LLM 'text-generation TGI'<br>9000]) end
direction TB
TEI_EM{{TEI embedding service<br>8090}}
VDB{{Vector DB<br>8001}}
%% Vecto... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
932858f5-b91c-4339-ac3d-c1498422d9b1 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 58 | opea-semantic-v1 | 8d6ecd17c9c1b676 | at the status of the targets and the metrics that are being scraped. To search for a metrics variable, type it in the search bar.
The TGI metrics can be accessed at: | ai_ref_knowledge | OPEA Documentation | at the status of the targets and the metrics that are being scraped. To search for a metrics variable, type it in the search bar.
The TGI metrics can be accessed at: | at the status of the targets and the metrics that are being scraped. To search for a metrics variable, type it in the search bar.
The TGI metrics can be accessed at: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
96cd5b97-7a1a-4496-abe3-c2047d1838c2 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 47 | opea-semantic-v1 | e06fa080b00c8b08 | 1. Download Prometheus: Download the Prometheus v2.52.0 from the official site, and extract the files:
wget https://github.com/prometheus/prometheus/releases/download/v2.52.0/prometheus-2.52.0.linux-amd64.tar.gz
tar -xvzf prometheus-2.52.0.linux-amd64.tar.gz | ai_ref_knowledge | OPEA Documentation | 1. Download Prometheus: Download the Prometheus v2.52.0 from the official site, and extract the files:
wget https://github.com/prometheus/prometheus/releases/download/v2.52.0/prometheus-2.52.0.linux-amd64.tar.gz
tar -xvzf prometheus-2.52.0.linux-amd64.tar.gz | 1. Download Prometheus: Download the Prometheus v2.52.0 from the official site, and extract the files:
wget https://github.com/prometheus/prometheus/releases/download/v2.52.0/prometheus-2.52.0.linux-amd64.tar.gz
tar -xvzf prometheus-2.52.0.linux-amd64.tar.gz | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
979df48a-feb8-4f0d-a7b6-b68012a07d71 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 40 | opea-semantic-v1 | 75d57bc3af3038f5 | Monitoring **********
Monitoring the performance of microservices is crucial for ensuring the smooth operation of the generative AI systems. Monitoring metrics such as latency and throughput can identify bottlenecks, detect anomalies, and optimize the performance of individual microservices. This helps proactively addr... | ai_ref_knowledge | OPEA Documentation | Monitoring **********
Monitoring the performance of microservices is crucial for ensuring the smooth operation of the generative AI systems. Monitoring metrics such as latency and throughput can identify bottlenecks, detect anomalies, and optimize the performance of individual microservices. This helps proactively addr... | Monitoring **********
Monitoring the performance of microservices is crucial for ensuring the smooth operation of the generative AI systems. Monitoring metrics such as latency and throughput can identify bottlenecks, detect anomalies, and optimize the performance of individual microservices. This helps proactively addr... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
99f874f4-d990-4b0e-a1c4-fe7ba7631f34 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 32 | opea-semantic-v1 | 3438ad40c85d493c | %% Data Preparation flow %% Ingest data flow direction LR Ingest[Ingest data] -->|a| UI UI -->|b| DP DP -.->|c| TEI_EM
%% Questions interaction
direction LR
a[User Input Query] -->|1| UI
UI -->|2| GW
GW ==>|3| ChatQnA-MegaService
EM ==>|4| RET
RET ==>|5| RER
RER ==>|6| LLM | ai_ref_knowledge | OPEA Documentation | %% Data Preparation flow %% Ingest data flow direction LR Ingest[Ingest data] -->|a| UI UI -->|b| DP DP -.->|c| TEI_EM
%% Questions interaction
direction LR
a[User Input Query] -->|1| UI
UI -->|2| GW
GW ==>|3| ChatQnA-MegaService
EM ==>|4| RET
RET ==>|5| RER
RER ==>|6| LLM | %% Data Preparation flow %% Ingest data flow direction LR Ingest[Ingest data] -->|a| UI UI -->|b| DP DP -.->|c| TEI_EM
%% Questions interaction
direction LR
a[User Input Query] -->|1| UI
UI -->|2| GW
GW ==>|3| ChatQnA-MegaService
EM ==>|4| RET
RET ==>|5| RER
RER ==>|6| LLM | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
9cde5d23-680c-4a74-91b5-b15cfb76fd2a | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 22 | opea-semantic-v1 | 3bad929f3a4307fa | curl http://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{ "messages": "What is the revenue of Nike in 2023?" }'
Here is the output for reference: | ai_ref_knowledge | OPEA Documentation | curl http://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{ "messages": "What is the revenue of Nike in 2023?" }'
Here is the output for reference: | curl http://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{ "messages": "What is the revenue of Nike in 2023?" }'
Here is the output for reference: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a12b0ad0-7848-4657-b51b-9ac4d6e16f2d | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 17 | opea-semantic-v1 | 9dcfb1bcb71e8ec7 | LLMs generate a response based on the input data and the user query. This response is then returned to the user as the chatbot's answer.
Customize with new VectorDB | ai_ref_knowledge | OPEA Documentation | LLMs generate a response based on the input data and the user query. This response is then returned to the user as the chatbot's answer.
Customize with new VectorDB | LLMs generate a response based on the input data and the user query. This response is then returned to the user as the chatbot's answer.
Customize with new VectorDB | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ae00942b-4ccb-4c9b-a431-5bfa8a06e75d | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 33 | opea-semantic-v1 | c98a5b6a709e41a7 | %% Questions interaction direction LR a[User Input Query] -->|1| UI UI -->|2| GW GW ==>|3| ChatQnA-MegaService EM ==>|4| RET RET ==>|5| RER RER ==>|6| LLM
%% Embedding service flow
direction TB
EM -.->|3'| TEI_EM
RET -.->|4'| TEI_EM
RER -.->|5'| TEI_RER
LLM -.->|6'| LLM_gen | ai_ref_knowledge | OPEA Documentation | %% Questions interaction direction LR a[User Input Query] -->|1| UI UI -->|2| GW GW ==>|3| ChatQnA-MegaService EM ==>|4| RET RET ==>|5| RER RER ==>|6| LLM
%% Embedding service flow
direction TB
EM -.->|3'| TEI_EM
RET -.->|4'| TEI_EM
RER -.->|5'| TEI_RER
LLM -.->|6'| LLM_gen | %% Questions interaction direction LR a[User Input Query] -->|1| UI UI -->|2| GW GW ==>|3| ChatQnA-MegaService EM ==>|4| RET RET ==>|5| RER RER ==>|6| LLM
%% Embedding service flow
direction TB
EM -.->|3'| TEI_EM
RET -.->|4'| TEI_EM
RER -.->|5'| TEI_RER
LLM -.->|6'| LLM_gen | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
af0b6305-a0fa-4a3f-8bfe-23485b832467 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 67 | opea-semantic-v1 | fd01b08752e457ec | username: admin password: admin
4. Add Prometheus as a data source:
The data source for Grafana needs to be configured to scrape data. Click on the "Data Source" button, select Prometheus, and specify the Prometheus URL ``http://localhost:9090``. | ai_ref_knowledge | OPEA Documentation | username: admin password: admin
4. Add Prometheus as a data source:
The data source for Grafana needs to be configured to scrape data. Click on the "Data Source" button, select Prometheus, and specify the Prometheus URL ``http://localhost:9090``. | username: admin password: admin
4. Add Prometheus as a data source:
The data source for Grafana needs to be configured to scrape data. Click on the "Data Source" button, select Prometheus, and specify the Prometheus URL ``http://localhost:9090``. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b27e678d-4b43-4ef6-a292-2d59a3330c7a | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 14 | opea-semantic-v1 | f57aa750085491bb | information in the chatbot system, starting from the user input and going through the retrieve, analyze, and generate components, ultimately resulting in the bot's output.
The architecture follows a series of steps to process user queries and generate responses: | ai_ref_knowledge | OPEA Documentation | information in the chatbot system, starting from the user input and going through the retrieve, analyze, and generate components, ultimately resulting in the bot's output.
The architecture follows a series of steps to process user queries and generate responses: | information in the chatbot system, starting from the user input and going through the retrieve, analyze, and generate components, ultimately resulting in the bot's output.
The architecture follows a series of steps to process user queries and generate responses: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b441d3ea-1662-4cca-9166-f31f0320e63f | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 10 | opea-semantic-v1 | d08f0cad6aab7189 | The training and utilization of LLMs for generating responses. Deployment Options: production ready deployment options for the ChatQnA example, including single-node deployments and Kubernetes deployments.
How It Works
************ | ai_ref_knowledge | OPEA Documentation | The training and utilization of LLMs for generating responses. Deployment Options: production ready deployment options for the ChatQnA example, including single-node deployments and Kubernetes deployments.
How It Works
************ | The training and utilization of LLMs for generating responses. Deployment Options: production ready deployment options for the ChatQnA example, including single-node deployments and Kubernetes deployments.
How It Works
************ | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
bc17931e-2857-46d2-95b5-86330e989885 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 68 | opea-semantic-v1 | 0ed4422b9fcd9e7c | data source for Grafana needs to be configured to scrape data. Click on the "Data Source" button, select Prometheus, and specify the Prometheus URL ``http://localhost:9090``.
Then, upload a JSON file for the dashboard's configuration. Upload it in the Grafana UI under ``Home > Dashboards > Import dashboard``. A sample ... | ai_ref_knowledge | OPEA Documentation | data source for Grafana needs to be configured to scrape data. Click on the "Data Source" button, select Prometheus, and specify the Prometheus URL ``http://localhost:9090``.
Then, upload a JSON file for the dashboard's configuration. Upload it in the Grafana UI under ``Home > Dashboards > Import dashboard``. A sample ... | data source for Grafana needs to be configured to scrape data. Click on the "Data Source" button, select Prometheus, and specify the Prometheus URL ``http://localhost:9090``.
Then, upload a JSON file for the dashboard's configuration. Upload it in the Grafana UI under ``Home > Dashboards > Import dashboard``. A sample ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
bd795350-66fe-43d1-9fef-3fc61adf79a1 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 23 | opea-semantic-v1 | 9f9b41edda7bbe52 | Here is the output for reference:
data: b'\n'
data: b'An'
data: b'swer'
data: b':'
data: b' In'
data: b' fiscal'
data: b' '
data: b'2'
data: b'0'
data: b'2'
data: b'3'
data: b','
data: b' N'
data: b'I'
data: b'KE'
data: b','
data: b' Inc'
data: b'.'
data: b' achieved'
data: b' record'
data: b' Rev'
... | ai_ref_knowledge | OPEA Documentation | Here is the output for reference:
data: b'\n'
data: b'An'
data: b'swer'
data: b':'
data: b' In'
data: b' fiscal'
data: b' '
data: b'2'
data: b'0'
data: b'2'
data: b'3'
data: b','
data: b' N'
data: b'I'
data: b'KE'
data: b','
data: b' Inc'
data: b'.'
data: b' achieved'
data: b' record'
data: b' Rev'
... | Here is the output for reference:
data: b'\n'
data: b'An'
data: b'swer'
data: b':'
data: b' In'
data: b' fiscal'
data: b' '
data: b'2'
data: b'0'
data: b'2'
data: b'3'
data: b','
data: b' N'
data: b'I'
data: b'KE'
data: b','
data: b' Inc'
data: b'.'
data: b' achieved'
data: b' record'
data: b' Rev'
... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c1d71787-2b1a-497f-8ef6-70e0b0165878 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 70 | opea-semantic-v1 | f0c548e74e5ed467 | 5. View the dashboard: Finally, open the dashboard in the Grafana UI to see different panels displaying the metrics data.
Taking the TGI microservice as an example, look at the following metrics:
* Time to first token
* Decode per-token latency
* Throughput (generated tokens/sec)
* Number of tokens per prompt
* Nu... | ai_ref_knowledge | OPEA Documentation | 5. View the dashboard: Finally, open the dashboard in the Grafana UI to see different panels displaying the metrics data.
Taking the TGI microservice as an example, look at the following metrics:
* Time to first token
* Decode per-token latency
* Throughput (generated tokens/sec)
* Number of tokens per prompt
* Nu... | 5. View the dashboard: Finally, open the dashboard in the Grafana UI to see different panels displaying the metrics data.
Taking the TGI microservice as an example, look at the following metrics:
* Time to first token
* Decode per-token latency
* Throughput (generated tokens/sec)
* Number of tokens per prompt
* Nu... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c436c50c-46e3-4ab0-9c83-a0ae0e686702 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 36 | opea-semantic-v1 | 20cb01488114764c | are some deployment options depending on the hardware and environment. It includes both single-node and orchestrated multi-node configurations. Choose the one that best fits requirements.
Single Node
*********** | ai_ref_knowledge | OPEA Documentation | are some deployment options depending on the hardware and environment. It includes both single-node and orchestrated multi-node configurations. Choose the one that best fits requirements.
Single Node
*********** | are some deployment options depending on the hardware and environment. It includes both single-node and orchestrated multi-node configurations. Choose the one that best fits requirements.
Single Node
*********** | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c8bd6b46-bedb-4898-b151-ed8b374d73e7 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 0 | opea-semantic-v1 | 1344ac458ec30e0d | Overview ********
Chatbots are a widely adopted use case for leveraging the powerful chat and
reasoning capabilities of large language models (LLMs). The ChatQnA example
provides the starting point for developers to begin working in the GenAI space. Consider it the “hello world” of GenAI applications and can be leverag... | ai_ref_knowledge | OPEA Documentation | Overview ********
Chatbots are a widely adopted use case for leveraging the powerful chat and
reasoning capabilities of large language models (LLMs). The ChatQnA example
provides the starting point for developers to begin working in the GenAI space. Consider it the “hello world” of GenAI applications and can be leverag... | Overview ********
Chatbots are a widely adopted use case for leveraging the powerful chat and
reasoning capabilities of large language models (LLMs). The ChatQnA example
provides the starting point for developers to begin working in the GenAI space. Consider it the “hello world” of GenAI applications and can be leverag... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d0cbc777-2991-4bcd-91ad-50a876cdf3da | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 54 | opea-semantic-v1 | ec59a7dc00aa317f | static_configs: - targets: ["llm-dependency-svc.default.svc.cluster.local:9009"]
3. Run the Prometheus server:
Run the Prometheus server, without hanging-up the process:
```bash
nohup ./prometheus --config.file=./prometheus.yml & | ai_ref_knowledge | OPEA Documentation | static_configs: - targets: ["llm-dependency-svc.default.svc.cluster.local:9009"]
3. Run the Prometheus server:
Run the Prometheus server, without hanging-up the process:
```bash
nohup ./prometheus --config.file=./prometheus.yml & | static_configs: - targets: ["llm-dependency-svc.default.svc.cluster.local:9009"]
3. Run the Prometheus server:
Run the Prometheus server, without hanging-up the process:
```bash
nohup ./prometheus --config.file=./prometheus.yml & | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d3ce7d4e-8ff1-4176-9390-e3fbd7ee86df | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 9 | opea-semantic-v1 | 8fc0cf9fe18d2d46 | Key Implementation Details **************************
Embedding:
The process of transforming user queries into numerical representations called
embeddings. Vector Database:
The storage and retrieval of relevant data points using vector databases. RAG Architecture:
The use of the RAG architecture to combine knowledg... | ai_ref_knowledge | OPEA Documentation | Key Implementation Details **************************
Embedding:
The process of transforming user queries into numerical representations called
embeddings. Vector Database:
The storage and retrieval of relevant data points using vector databases. RAG Architecture:
The use of the RAG architecture to combine knowledg... | Key Implementation Details **************************
Embedding:
The process of transforming user queries into numerical representations called
embeddings. Vector Database:
The storage and retrieval of relevant data points using vector databases. RAG Architecture:
The use of the RAG architecture to combine knowledg... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
da19513b-2358-4301-9c2b-07d483c28f75 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 18 | opea-semantic-v1 | 4f5565b86f176063 | Customize with new VectorDB
Adding a new VectorDB to OPEA involves minimal changes to OPEA sub-project `GenAI Components <https://github.com/opea-project/GenAIComps>`_ that covers installation, launch, usage, and tests. | ai_ref_knowledge | OPEA Documentation | Customize with new VectorDB
Adding a new VectorDB to OPEA involves minimal changes to OPEA sub-project `GenAI Components <https://github.com/opea-project/GenAIComps>`_ that covers installation, launch, usage, and tests. | Customize with new VectorDB
Adding a new VectorDB to OPEA involves minimal changes to OPEA sub-project `GenAI Components <https://github.com/opea-project/GenAIComps>`_ that covers installation, launch, usage, and tests. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
dbc520d9-c9dc-4e22-863b-99ed065f447a | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 71 | opea-semantic-v1 | 28a3ea652ae504d8 | Time to first token * Decode per-token latency * Throughput (generated tokens/sec) * Number of tokens per prompt * Number of generated tokens per request
Incoming requests to the microservice, the response time per token, etc., can also be monitored in real time. | ai_ref_knowledge | OPEA Documentation | Time to first token * Decode per-token latency * Throughput (generated tokens/sec) * Number of tokens per prompt * Number of generated tokens per request
Incoming requests to the microservice, the response time per token, etc., can also be monitored in real time. | Time to first token * Decode per-token latency * Throughput (generated tokens/sec) * Number of tokens per prompt * Number of generated tokens per request
Incoming requests to the microservice, the response time per token, etc., can also be monitored in real time. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
dcd8604e-2d0d-49bd-9a7a-1cdbee5b97e2 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 29 | opea-semantic-v1 | ddee5b0fd0cefff1 | direction TB RER([OPEA Reranking<br>8000]) TEI_RER{{TEI Reranking service<br>8808}}
subgraph User Interface
direction TB
a[User Input Query]
Ingest[Ingest data]
UI[UI server<br>Port: 5173]
end | ai_ref_knowledge | OPEA Documentation | direction TB RER([OPEA Reranking<br>8000]) TEI_RER{{TEI Reranking service<br>8808}}
subgraph User Interface
direction TB
a[User Input Query]
Ingest[Ingest data]
UI[UI server<br>Port: 5173]
end | direction TB RER([OPEA Reranking<br>8000]) TEI_RER{{TEI Reranking service<br>8808}}
subgraph User Interface
direction TB
a[User Input Query]
Ingest[Ingest data]
UI[UI server<br>Port: 5173]
end | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
de28e419-44be-4527-a6b1-756cc0fcddd3 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 8 | opea-semantic-v1 | afde394c0b4bf676 | and even on AI PCs. It also supports Kubernetes deployments with and without the GenAI Management Console (GMC), as well as cloud-native deployments using RHOCP.
Key Implementation Details
************************** | ai_ref_knowledge | OPEA Documentation | and even on AI PCs. It also supports Kubernetes deployments with and without the GenAI Management Console (GMC), as well as cloud-native deployments using RHOCP.
Key Implementation Details
************************** | and even on AI PCs. It also supports Kubernetes deployments with and without the GenAI Management Console (GMC), as well as cloud-native deployments using RHOCP.
Key Implementation Details
************************** | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
def6892c-d12c-475f-aa4d-c362f594548e | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 12 | opea-semantic-v1 | 15d71b117c027050 | information in the chatbot system, starting from the user input and going through the retrieve, re-ranker, and generate components, ultimately resulting in the bot's output.
.. figure:: /GenAIExamples/ChatQnA/assets/img/chatqna_architecture.png | ai_ref_knowledge | OPEA Documentation | information in the chatbot system, starting from the user input and going through the retrieve, re-ranker, and generate components, ultimately resulting in the bot's output.
.. figure:: /GenAIExamples/ChatQnA/assets/img/chatqna_architecture.png | information in the chatbot system, starting from the user input and going through the retrieve, re-ranker, and generate components, ultimately resulting in the bot's output.
.. figure:: /GenAIExamples/ChatQnA/assets/img/chatqna_architecture.png | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ea47370e-1603-469f-a412-8df3e3b6d617 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 45 | opea-semantic-v1 | 895fbcee0f9d2a68 | server scrapes these metrics and stores them in its time series database. For example, metrics for the Text Generation Interface (TGI) service are available at:
http://${host_ip}:9009/metrics | ai_ref_knowledge | OPEA Documentation | server scrapes these metrics and stores them in its time series database. For example, metrics for the Text Generation Interface (TGI) service are available at:
http://${host_ip}:9009/metrics | server scrapes these metrics and stores them in its time series database. For example, metrics for the Text Generation Interface (TGI) service are available at:
http://${host_ip}:9009/metrics | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ed4895fe-83ef-4087-981c-3e64d07f62d0 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 55 | opea-semantic-v1 | f75beda87807a757 | 3. Run the Prometheus server: Run the Prometheus server, without hanging-up the process: ```bash nohup ./prometheus --config.file=./prometheus.yml &
4. Access the Prometheus UI
Access the Prometheus UI at the following URL: | ai_ref_knowledge | OPEA Documentation | 3. Run the Prometheus server: Run the Prometheus server, without hanging-up the process: ```bash nohup ./prometheus --config.file=./prometheus.yml &
4. Access the Prometheus UI
Access the Prometheus UI at the following URL: | 3. Run the Prometheus server: Run the Prometheus server, without hanging-up the process: ```bash nohup ./prometheus --config.file=./prometheus.yml &
4. Access the Prometheus UI
Access the Prometheus UI at the following URL: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f08eb00c-50b9-45ba-b6f4-874be084a1aa | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 46 | opea-semantic-v1 | bd5ef4c2b78f5148 | Set up the Prometheus server:
1. Download Prometheus:
Download the Prometheus v2.52.0 from the official site, and extract the files: | ai_ref_knowledge | OPEA Documentation | Set up the Prometheus server:
1. Download Prometheus:
Download the Prometheus v2.52.0 from the official site, and extract the files: | Set up the Prometheus server:
1. Download Prometheus:
Download the Prometheus v2.52.0 from the official site, and extract the files: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f628ad52-7b6d-4156-a836-ae955f0d0ed5 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 38 | opea-semantic-v1 | d14f88a1bc960f8c | trying to access the UI interface. By default, typing the :5173 resolves to https://:5173. Chrome shows the following warning message：xx.xx.xx.xx doesn't support a secure connection
A: By default, the browser resolves xx.xx.xx.xx:5173 to https://xx.xx.xx.xx:5173. But to meet security requirements, users need to deploy ... | ai_ref_knowledge | OPEA Documentation | trying to access the UI interface. By default, typing the :5173 resolves to https://:5173. Chrome shows the following warning message：xx.xx.xx.xx doesn't support a secure connection
A: By default, the browser resolves xx.xx.xx.xx:5173 to https://xx.xx.xx.xx:5173. But to meet security requirements, users need to deploy ... | trying to access the UI interface. By default, typing the :5173 resolves to https://:5173. Chrome shows the following warning message：xx.xx.xx.xx doesn't support a secure connection
A: By default, the browser resolves xx.xx.xx.xx:5173 to https://xx.xx.xx.xx:5173. But to meet security requirements, users need to deploy ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
fc731eeb-271d-481e-ab69-c227421ede8a | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 25 | opea-semantic-v1 | 359a475dce966479 | Microservice Outline and Diagram
A GenAI application or pipeline in OPEA typically consists of a collection of microservices to create a megaservice, accessed via a gateway. A microservice is a component designed to perform a specific function or task. Microservices are building blocks, offering the fundamental service... | ai_ref_knowledge | OPEA Documentation | Microservice Outline and Diagram
A GenAI application or pipeline in OPEA typically consists of a collection of microservices to create a megaservice, accessed via a gateway. A microservice is a component designed to perform a specific function or task. Microservices are building blocks, offering the fundamental service... | Microservice Outline and Diagram
A GenAI application or pipeline in OPEA typically consists of a collection of microservices to create a megaservice, accessed via a gateway. A microservice is a component designed to perform a specific function or task. Microservices are building blocks, offering the fundamental service... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
fd0d5e49-23c5-4eab-b109-eedbff7a6440 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/ChatQnA_Guide.rst | unknown | 97a8406a-48b5-4fd6-a3c3-39a82c0ef039 | 37 | opea-semantic-v1 | e1af7b9f635d3480 | 1. Browser interface https link failed
Q: For example, started ChatQnA example in IBM Cloud and trying to access the UI interface. By default, typing the :5173 resolves to https://:5173. Chrome shows the following warning message：xx.xx.xx.xx doesn't support a secure connection | ai_ref_knowledge | OPEA Documentation | 1. Browser interface https link failed
Q: For example, started ChatQnA example in IBM Cloud and trying to access the UI interface. By default, typing the :5173 resolves to https://:5173. Chrome shows the following warning message：xx.xx.xx.xx doesn't support a secure connection | 1. Browser interface https link failed
Q: For example, started ChatQnA example in IBM Cloud and trying to access the UI interface. By default, typing the :5173 resolves to https://:5173. Chrome shows the following warning message：xx.xx.xx.xx doesn't support a secure connection | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4d852568-1ff3-47bf-b9a4-58da1235e393 | OPEA Documentation | file://datasets/opea-docs/tutorial/CodeGen/CodeGen_Guide.rst | unknown | ce65fef5-003f-464f-8c59-b76229666898 | 6 | opea-semantic-v1 | 324f6becd1049460 | Deployment ********** Here are some deployment options, depending on the hardware and environment:
Intel® Xeon® Scalable processor <deploy/xeon>
Gaudi AI Accelerator <deploy/gaudi> | ai_ref_knowledge | OPEA Documentation | Deployment ********** Here are some deployment options, depending on the hardware and environment:
Intel® Xeon® Scalable processor <deploy/xeon>
Gaudi AI Accelerator <deploy/gaudi> | Deployment ********** Here are some deployment options, depending on the hardware and environment:
Intel® Xeon® Scalable processor <deploy/xeon>
Gaudi AI Accelerator <deploy/gaudi> | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5107847f-1ed8-49a1-9f4e-61c0b6d90b10 | OPEA Documentation | file://datasets/opea-docs/tutorial/CodeGen/CodeGen_Guide.rst | unknown | ce65fef5-003f-464f-8c59-b76229666898 | 3 | opea-semantic-v1 | 20bb61e323473d03 | * Error Detection and Debugging: Detect errors in code and provide detailed descriptions and potential fixes, expediting debugging processes.
How It Works
************ | ai_ref_knowledge | OPEA Documentation | * Error Detection and Debugging: Detect errors in code and provide detailed descriptions and potential fixes, expediting debugging processes.
How It Works
************ | * Error Detection and Debugging: Detect errors in code and provide detailed descriptions and potential fixes, expediting debugging processes.
How It Works
************ | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6d0123ee-edaf-4227-8cf7-87d7c994a291 | OPEA Documentation | file://datasets/opea-docs/tutorial/CodeGen/CodeGen_Guide.rst | unknown | ce65fef5-003f-464f-8c59-b76229666898 | 5 | opea-semantic-v1 | f443840d5953e97b | code generation model with Text Generation Inference (TGI) for serving deployment. It is presented as a Code Copilot application as shown in the diagram below.
.. figure:: /GenAIExamples/CodeGen/assets/img/codegen_architecture.png | ai_ref_knowledge | OPEA Documentation | code generation model with Text Generation Inference (TGI) for serving deployment. It is presented as a Code Copilot application as shown in the diagram below.
.. figure:: /GenAIExamples/CodeGen/assets/img/codegen_architecture.png | code generation model with Text Generation Inference (TGI) for serving deployment. It is presented as a Code Copilot application as shown in the diagram below.
.. figure:: /GenAIExamples/CodeGen/assets/img/codegen_architecture.png | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8fc1e07b-fa90-4cfc-8c49-a0506255556d | OPEA Documentation | file://datasets/opea-docs/tutorial/CodeGen/CodeGen_Guide.rst | unknown | ce65fef5-003f-464f-8c59-b76229666898 | 0 | opea-semantic-v1 | 28059bf60bb38229 | Overview ********
The CodeGen example uses specialized AI models that went through training with datasets that encompass repositories, documentation, programming code, and web data. With an understanding
of various programming languages, coding patterns, and software development concepts, CodeGen LLMs assist developers... | ai_ref_knowledge | OPEA Documentation | Overview ********
The CodeGen example uses specialized AI models that went through training with datasets that encompass repositories, documentation, programming code, and web data. With an understanding
of various programming languages, coding patterns, and software development concepts, CodeGen LLMs assist developers... | Overview ********
The CodeGen example uses specialized AI models that went through training with datasets that encompass repositories, documentation, programming code, and web data. With an understanding
of various programming languages, coding patterns, and software development concepts, CodeGen LLMs assist developers... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
9f230346-d466-444d-8569-6565413eb13c | OPEA Documentation | file://datasets/opea-docs/tutorial/CodeGen/CodeGen_Guide.rst | unknown | ce65fef5-003f-464f-8c59-b76229666898 | 2 | opea-semantic-v1 | cc71c0b863646078 | Offer suggestions for code refactoring, enhancing code performance and efficiency. * AI-Assisted Testing: Assist in creating test cases, ensuring code robustness and accelerating development cycles.
* Error Detection and Debugging: Detect errors in code and provide detailed descriptions and potential fixes, expediting ... | ai_ref_knowledge | OPEA Documentation | Offer suggestions for code refactoring, enhancing code performance and efficiency. * AI-Assisted Testing: Assist in creating test cases, ensuring code robustness and accelerating development cycles.
* Error Detection and Debugging: Detect errors in code and provide detailed descriptions and potential fixes, expediting ... | Offer suggestions for code refactoring, enhancing code performance and efficiency. * AI-Assisted Testing: Assist in creating test cases, ensuring code robustness and accelerating development cycles.
* Error Detection and Debugging: Detect errors in code and provide detailed descriptions and potential fixes, expediting ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a4fab501-2794-4d2c-abdb-f0470bc3458d | OPEA Documentation | file://datasets/opea-docs/tutorial/CodeGen/CodeGen_Guide.rst | unknown | ce65fef5-003f-464f-8c59-b76229666898 | 1 | opea-semantic-v1 | 481dffe891f5ef8b | can be integrated into the developers' Integrated Development Environments (IDEs) to have more contextual awareness to write more refined and relevant code based on suggestions.
Purpose
*******
* Code Generation: Streamline coding through Code Generation, enabling non-programmers to describe tasks for code creation. * ... | ai_ref_knowledge | OPEA Documentation | can be integrated into the developers' Integrated Development Environments (IDEs) to have more contextual awareness to write more refined and relevant code based on suggestions.
Purpose
*******
* Code Generation: Streamline coding through Code Generation, enabling non-programmers to describe tasks for code creation. * ... | can be integrated into the developers' Integrated Development Environments (IDEs) to have more contextual awareness to write more refined and relevant code based on suggestions.
Purpose
*******
* Code Generation: Streamline coding through Code Generation, enabling non-programmers to describe tasks for code creation. * ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ba5948f0-f295-4249-9d7c-595a29836cac | OPEA Documentation | file://datasets/opea-docs/tutorial/CodeGen/CodeGen_Guide.rst | unknown | ce65fef5-003f-464f-8c59-b76229666898 | 4 | opea-semantic-v1 | 6c81745fc3fae82f | How It Works ************
The CodeGen example uses an open-source code generation model with Text Generation Inference (TGI)
for serving deployment. It is presented as a Code Copilot application as shown in the diagram below. | ai_ref_knowledge | OPEA Documentation | How It Works ************
The CodeGen example uses an open-source code generation model with Text Generation Inference (TGI)
for serving deployment. It is presented as a Code Copilot application as shown in the diagram below. | How It Works ************
The CodeGen example uses an open-source code generation model with Text Generation Inference (TGI)
for serving deployment. It is presented as a Code Copilot application as shown in the diagram below. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
00e9baf9-148c-4713-a2ef-dbf7fa37ebfd | OPEA Documentation | file://datasets/opea-docs/tutorial/CodeTrans/CodeTrans_Guide.rst | unknown | febe9df8-5507-4ecd-9564-1e03a664e607 | 3 | opea-semantic-v1 | 0f4d107f0470f95d | multi-language support**: By providing a system that understands multiple programming languages, organizations can unify their development approaches and reduce the barrier to adopting new languages.
* **Improve developer productivity**: Automated code translation drastically reduces manual, time-consuming porting effo... | ai_ref_knowledge | OPEA Documentation | multi-language support**: By providing a system that understands multiple programming languages, organizations can unify their development approaches and reduce the barrier to adopting new languages.
* **Improve developer productivity**: Automated code translation drastically reduces manual, time-consuming porting effo... | multi-language support**: By providing a system that understands multiple programming languages, organizations can unify their development approaches and reduce the barrier to adopting new languages.
* **Improve developer productivity**: Automated code translation drastically reduces manual, time-consuming porting effo... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
098c3343-a1ec-45fe-8707-fd180b7cb63e | OPEA Documentation | file://datasets/opea-docs/tutorial/CodeTrans/CodeTrans_Guide.rst | unknown | febe9df8-5507-4ecd-9564-1e03a664e607 | 7 | opea-semantic-v1 | 27002dc88307986a | request is sent to the CodeTrans gateway, which orchestrates the call to the LLM microservice. The gateway handles details like constructing prompts and managing responses.
3. The large language model processes the user’s code snippet by analyzing syntax and semantics before generating an equivalent snippet in the targ... | ai_ref_knowledge | OPEA Documentation | request is sent to the CodeTrans gateway, which orchestrates the call to the LLM microservice. The gateway handles details like constructing prompts and managing responses.
3. The large language model processes the user’s code snippet by analyzing syntax and semantics before generating an equivalent snippet in the targ... | request is sent to the CodeTrans gateway, which orchestrates the call to the LLM microservice. The gateway handles details like constructing prompts and managing responses.
3. The large language model processes the user’s code snippet by analyzing syntax and semantics before generating an equivalent snippet in the targ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1c65c111-b23e-4b53-bc57-f38f1adb4194 | OPEA Documentation | file://datasets/opea-docs/tutorial/CodeTrans/CodeTrans_Guide.rst | unknown | febe9df8-5507-4ecd-9564-1e03a664e607 | 8 | opea-semantic-v1 | d4a714c74510a259 | 3. The large language model processes the user’s code snippet by analyzing syntax and semantics before generating an equivalent snippet in the target language.
4. The gateway formats the model’s output and returns the translated code to the user, via an API response or rendered within the UI. | ai_ref_knowledge | OPEA Documentation | 3. The large language model processes the user’s code snippet by analyzing syntax and semantics before generating an equivalent snippet in the target language.
4. The gateway formats the model’s output and returns the translated code to the user, via an API response or rendered within the UI. | 3. The large language model processes the user’s code snippet by analyzing syntax and semantics before generating an equivalent snippet in the target language.
4. The gateway formats the model’s output and returns the translated code to the user, via an API response or rendered within the UI. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
3648e778-1c8f-47c1-a992-1c82cadcf8c3 | OPEA Documentation | file://datasets/opea-docs/tutorial/CodeTrans/CodeTrans_Guide.rst | unknown | febe9df8-5507-4ecd-9564-1e03a664e607 | 1 | opea-semantic-v1 | 6311eff580333baa | gateway service and a user interface allow users to submit their source code in a given language and receive the translated output in another language.
Purpose
*******
* **Enable code conversion and modernization**: Developers can seamlessly migrate legacy code to newer languages or frameworks, leveraging modern best p... | ai_ref_knowledge | OPEA Documentation | gateway service and a user interface allow users to submit their source code in a given language and receive the translated output in another language.
Purpose
*******
* **Enable code conversion and modernization**: Developers can seamlessly migrate legacy code to newer languages or frameworks, leveraging modern best p... | gateway service and a user interface allow users to submit their source code in a given language and receive the translated output in another language.
Purpose
*******
* **Enable code conversion and modernization**: Developers can seamlessly migrate legacy code to newer languages or frameworks, leveraging modern best p... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4f06d0af-250e-45ce-870b-bd6bc752d3c9 | OPEA Documentation | file://datasets/opea-docs/tutorial/CodeTrans/CodeTrans_Guide.rst | unknown | febe9df8-5507-4ecd-9564-1e03a664e607 | 4 | opea-semantic-v1 | 03576451701d4c19 | * **Improve developer productivity**: Automated code translation drastically reduces manual, time-consuming porting efforts, allowing developers to focus on higher-level tasks like feature design and optimization.
How It Works
************ | ai_ref_knowledge | OPEA Documentation | * **Improve developer productivity**: Automated code translation drastically reduces manual, time-consuming porting efforts, allowing developers to focus on higher-level tasks like feature design and optimization.
How It Works
************ | * **Improve developer productivity**: Automated code translation drastically reduces manual, time-consuming porting efforts, allowing developers to focus on higher-level tasks like feature design and optimization.
How It Works
************ | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
614e43de-a402-4cd9-9616-35dafe14e44d | OPEA Documentation | file://datasets/opea-docs/tutorial/CodeTrans/CodeTrans_Guide.rst | unknown | febe9df8-5507-4ecd-9564-1e03a664e607 | 5 | opea-semantic-v1 | da8d862ae83d3403 | .. figure:: /GenAIExamples/CodeTrans/assets/img/code_trans_architecture.png
1. A user specifies the source language, the target language, and the snippet of code to be translated. This request is handled by the front-end UI or via a direct API call. | ai_ref_knowledge | OPEA Documentation | .. figure:: /GenAIExamples/CodeTrans/assets/img/code_trans_architecture.png
1. A user specifies the source language, the target language, and the snippet of code to be translated. This request is handled by the front-end UI or via a direct API call. | .. figure:: /GenAIExamples/CodeTrans/assets/img/code_trans_architecture.png
1. A user specifies the source language, the target language, and the snippet of code to be translated. This request is handled by the front-end UI or via a direct API call. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8c7bc1f3-04ef-4db8-b187-9dffb8617004 | OPEA Documentation | file://datasets/opea-docs/tutorial/CodeTrans/CodeTrans_Guide.rst | unknown | febe9df8-5507-4ecd-9564-1e03a664e607 | 6 | opea-semantic-v1 | 817dc535d1e6b0a7 | the target language, and the snippet of code to be translated. This request is handled by the front-end UI or via a direct API call.
2. The user’s request is sent to the CodeTrans gateway, which orchestrates the call to the LLM microservice. The gateway handles details like constructing prompts and managing responses. | ai_ref_knowledge | OPEA Documentation | the target language, and the snippet of code to be translated. This request is handled by the front-end UI or via a direct API call.
2. The user’s request is sent to the CodeTrans gateway, which orchestrates the call to the LLM microservice. The gateway handles details like constructing prompts and managing responses. | the target language, and the snippet of code to be translated. This request is handled by the front-end UI or via a direct API call.
2. The user’s request is sent to the CodeTrans gateway, which orchestrates the call to the LLM microservice. The gateway handles details like constructing prompts and managing responses. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ad6c20f5-01a7-49b3-851b-792a800ac31f | OPEA Documentation | file://datasets/opea-docs/tutorial/CodeTrans/CodeTrans_Guide.rst | unknown | febe9df8-5507-4ecd-9564-1e03a664e607 | 2 | opea-semantic-v1 | 105abf78745eb0b3 | modernization**: Developers can seamlessly migrate legacy code to newer languages or frameworks, leveraging modern best practices without having to rewrite large code bases from scratch.
* **Facilitate multi-language support**: By providing a system that understands multiple programming languages, organizations can uni... | ai_ref_knowledge | OPEA Documentation | modernization**: Developers can seamlessly migrate legacy code to newer languages or frameworks, leveraging modern best practices without having to rewrite large code bases from scratch.
* **Facilitate multi-language support**: By providing a system that understands multiple programming languages, organizations can uni... | modernization**: Developers can seamlessly migrate legacy code to newer languages or frameworks, leveraging modern best practices without having to rewrite large code bases from scratch.
* **Facilitate multi-language support**: By providing a system that understands multiple programming languages, organizations can uni... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
bc4d4e2a-c008-4770-adb9-bc8f6c653bb4 | OPEA Documentation | file://datasets/opea-docs/tutorial/CodeTrans/CodeTrans_Guide.rst | unknown | febe9df8-5507-4ecd-9564-1e03a664e607 | 0 | opea-semantic-v1 | 6c64fdb344ce50ae | Overview ********
This example showcases a code translation system that converts code from one programming language to another while preserving the original logic and functionality. The primary component is the CodeTrans MegaService, which encompasses an LLM microservice that performs the actual translation. A lightwei... | ai_ref_knowledge | OPEA Documentation | Overview ********
This example showcases a code translation system that converts code from one programming language to another while preserving the original logic and functionality. The primary component is the CodeTrans MegaService, which encompasses an LLM microservice that performs the actual translation. A lightwei... | Overview ********
This example showcases a code translation system that converts code from one programming language to another while preserving the original logic and functionality. The primary component is the CodeTrans MegaService, which encompasses an LLM microservice that performs the actual translation. A lightwei... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
dd232f75-faeb-42f4-95fc-ef1326dd9c77 | OPEA Documentation | file://datasets/opea-docs/tutorial/CodeTrans/CodeTrans_Guide.rst | unknown | febe9df8-5507-4ecd-9564-1e03a664e607 | 9 | opea-semantic-v1 | 92e1b5a0397fa86d | 4. The gateway formats the model’s output and returns the translated code to the user, via an API response or rendered within the UI.
Deployment
**********
Here are some deployment options, depending on the hardware and environment: | ai_ref_knowledge | OPEA Documentation | 4. The gateway formats the model’s output and returns the translated code to the user, via an API response or rendered within the UI.
Deployment
**********
Here are some deployment options, depending on the hardware and environment: | 4. The gateway formats the model’s output and returns the translated code to the user, via an API response or rendered within the UI.
Deployment
**********
Here are some deployment options, depending on the hardware and environment: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0186ea0c-4bef-4740-b1e0-0d6d40de7b37 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/DocIndexRetriever_Guide.rst | unknown | bde3c268-bdd8-402f-9dbc-f02ca1bd0cbb | 18 | opea-semantic-v1 | b06cb46e3198bfbc | The architecture follows a series of steps to process user queries and generate responses:
1. **Embedding**: The Embedding MicroService converts the user query into a vector
representation. #. **Retriever**: The Retrieval MicroService retrieves relevant documents from the
vector database based on the vector represe... | ai_ref_knowledge | OPEA Documentation | The architecture follows a series of steps to process user queries and generate responses:
1. **Embedding**: The Embedding MicroService converts the user query into a vector
representation. #. **Retriever**: The Retrieval MicroService retrieves relevant documents from the
vector database based on the vector represe... | The architecture follows a series of steps to process user queries and generate responses:
1. **Embedding**: The Embedding MicroService converts the user query into a vector
representation. #. **Retriever**: The Retrieval MicroService retrieves relevant documents from the
vector database based on the vector represe... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
33e44f81-f426-47eb-8f10-3caa12b9f114 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/DocIndexRetriever_Guide.rst | unknown | bde3c268-bdd8-402f-9dbc-f02ca1bd0cbb | 10 | opea-semantic-v1 | 20155aa8fbaffcfc | DocIndexRetriever-MegaService["DocIndexRetriever MegaService "] direction LR EM([Embedding MicroService]):::blue RET([Retrieval MicroService]):::blue RER([Rerank MicroService]):::blue end subgraph UserInput[" User Input "] direction LR a([User Input Query]):::orchid Ingest([Ingest data]):::orchid end
DP([Data Preparati... | ai_ref_knowledge | OPEA Documentation | DocIndexRetriever-MegaService["DocIndexRetriever MegaService "] direction LR EM([Embedding MicroService]):::blue RET([Retrieval MicroService]):::blue RER([Rerank MicroService]):::blue end subgraph UserInput[" User Input "] direction LR a([User Input Query]):::orchid Ingest([Ingest data]):::orchid end
DP([Data Preparati... | DocIndexRetriever-MegaService["DocIndexRetriever MegaService "] direction LR EM([Embedding MicroService]):::blue RET([Retrieval MicroService]):::blue RER([Rerank MicroService]):::blue end subgraph UserInput[" User Input "] direction LR a([User Input Query]):::orchid Ingest([Ingest data]):::orchid end
DP([Data Preparati... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
3c1f04b3-01ac-4483-967c-608c26b7fa86 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/DocIndexRetriever_Guide.rst | unknown | bde3c268-bdd8-402f-9dbc-f02ca1bd0cbb | 0 | opea-semantic-v1 | 85b504af0c7796d5 | Overview ********
DocIndexRetriever is the most widely adopted use case for leveraging the different
methodologies to match user query against a set of free-text records. DocIndexRetriever
is essential to RAG system, which bridges the knowledge gap by dynamically fetching
relevant information from external sources, e... | ai_ref_knowledge | OPEA Documentation | Overview ********
DocIndexRetriever is the most widely adopted use case for leveraging the different
methodologies to match user query against a set of free-text records. DocIndexRetriever
is essential to RAG system, which bridges the knowledge gap by dynamically fetching
relevant information from external sources, e... | Overview ********
DocIndexRetriever is the most widely adopted use case for leveraging the different
methodologies to match user query against a set of free-text records. DocIndexRetriever
is essential to RAG system, which bridges the knowledge gap by dynamically fetching
relevant information from external sources, e... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
425f59fe-a0e8-493a-8a6d-910a518bf675 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/DocIndexRetriever_Guide.rst | unknown | bde3c268-bdd8-402f-9dbc-f02ca1bd0cbb | 9 | opea-semantic-v1 | 4bb6d8b3cdfd751c | 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:#ADD8E6,stroke-width:2px,fill-opacity:0.5 classDef invisible fill:transparent,stroke:transparent; style ... | ai_ref_knowledge | OPEA Documentation | 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:#ADD8E6,stroke-width:2px,fill-opacity:0.5 classDef invisible fill:transparent,stroke:transparent; style ... | 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:#ADD8E6,stroke-width:2px,fill-opacity:0.5 classDef invisible fill:transparent,stroke:transparent; style ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
490baca8-fc1d-4780-8c40-d2469690ad63 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/DocIndexRetriever_Guide.rst | unknown | bde3c268-bdd8-402f-9dbc-f02ca1bd0cbb | 19 | opea-semantic-v1 | 14708ba4371172b5 | the most pertinent documents or data points based on semantic similarity. #. **Data Preparation**: The Data Preparation MicroService prepares the data for the vector database.
Deployment
********** | ai_ref_knowledge | OPEA Documentation | the most pertinent documents or data points based on semantic similarity. #. **Data Preparation**: The Data Preparation MicroService prepares the data for the vector database.
Deployment
********** | the most pertinent documents or data points based on semantic similarity. #. **Data Preparation**: The Data Preparation MicroService prepares the data for the vector database.
Deployment
********** | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
57c62b9a-3d73-4f5b-b591-18115e9ccee9 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/DocIndexRetriever_Guide.rst | unknown | bde3c268-bdd8-402f-9dbc-f02ca1bd0cbb | 13 | opea-semantic-v1 | 64687ab064ce4ca4 | %% Questions interaction direction LR a[User Input Query] --> GW GW <==> DocIndexRetriever-MegaService EM ==> RET RET ==> RER
%% Embedding service flow
direction LR
EM <-.-> TEI_EM
RET <-.-> R_RET
RER <-.-> TEI_RER | ai_ref_knowledge | OPEA Documentation | %% Questions interaction direction LR a[User Input Query] --> GW GW <==> DocIndexRetriever-MegaService EM ==> RET RET ==> RER
%% Embedding service flow
direction LR
EM <-.-> TEI_EM
RET <-.-> R_RET
RER <-.-> TEI_RER | %% Questions interaction direction LR a[User Input Query] --> GW GW <==> DocIndexRetriever-MegaService EM ==> RET RET ==> RER
%% Embedding service flow
direction LR
EM <-.-> TEI_EM
RET <-.-> R_RET
RER <-.-> TEI_RER | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation |
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