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e9fdf99f-267f-4e6e-ba91-986a037c0954 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/xeon.md | unknown | 29f14fc0-57b6-4af3-929a-f858c9986b2c | 3 | opea-semantic-v1 | 81187baaac40ebe9 | Navigate to the `docker compose` directory for this hardware platform. ```bash cd $WORKSPACE/GenAIExamples/DocSum/docker_compose/intel/cpu/xeon
Run `docker compose` with the provided YAML file to start all the services mentioned above as containers. The vLLM or TGI service can be used for DocSum. | ai_ref_knowledge | OPEA Documentation | Navigate to the `docker compose` directory for this hardware platform. ```bash cd $WORKSPACE/GenAIExamples/DocSum/docker_compose/intel/cpu/xeon
Run `docker compose` with the provided YAML file to start all the services mentioned above as containers. The vLLM or TGI service can be used for DocSum. | Navigate to the `docker compose` directory for this hardware platform. ```bash cd $WORKSPACE/GenAIExamples/DocSum/docker_compose/intel/cpu/xeon
Run `docker compose` with the provided YAML file to start all the services mentioned above as containers. The vLLM or TGI service can be used for DocSum. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ffc6a888-ecbb-48b2-bf3e-de378b4ae98e | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/xeon.md | unknown | 29f14fc0-57b6-4af3-929a-f858c9986b2c | 2 | opea-semantic-v1 | 3ba5ce5fb31d79fe | associated tools. The tools and models listed in the table can be configured via environment variables in either the `set_env.sh` script or the `compose.yaml` file.
|Use Case Components | Tools | Model | Service Type |
|---------------- |--------------|----------------------------|------------------ |
|LLM | vLLM or TG... | ai_ref_knowledge | OPEA Documentation | associated tools. The tools and models listed in the table can be configured via environment variables in either the `set_env.sh` script or the `compose.yaml` file.
|Use Case Components | Tools | Model | Service Type |
|---------------- |--------------|----------------------------|------------------ |
|LLM | vLLM or TG... | associated tools. The tools and models listed in the table can be configured via environment variables in either the `set_env.sh` script or the `compose.yaml` file.
|Use Case Components | Tools | Model | Service Type |
|---------------- |--------------|----------------------------|------------------ |
|LLM | vLLM or TG... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
08ca0a63-59ee-40a6-bbb9-9e834b53cd23 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/AgentQnA.md | unknown | 03fc3abb-7506-4fbf-88e6-f3df4d8f1e1e | 0 | opea-semantic-v1 | 5c538c1db6a1d008 | # OpenTelemetry on AgentQnA Application
Each microservice in AgentQnA is instrumented with opea_telemetry, enabling Jaeger to provide a detailed time breakdown across microservices for each request. Additionally, AgentQnA features a pre-defined Grafana dashboard for its Agent services like the React Agent service, alon... | ai_ref_knowledge | OPEA Documentation | # OpenTelemetry on AgentQnA Application
Each microservice in AgentQnA is instrumented with opea_telemetry, enabling Jaeger to provide a detailed time breakdown across microservices for each request. Additionally, AgentQnA features a pre-defined Grafana dashboard for its Agent services like the React Agent service, alon... | # OpenTelemetry on AgentQnA Application
Each microservice in AgentQnA is instrumented with opea_telemetry, enabling Jaeger to provide a detailed time breakdown across microservices for each request. Additionally, AgentQnA features a pre-defined Grafana dashboard for its Agent services like the React Agent service, alon... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0c86c6cc-42fb-4f7c-9131-1b7449723b01 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/AgentQnA.md | unknown | 03fc3abb-7506-4fbf-88e6-f3df4d8f1e1e | 23 | opea-semantic-v1 | e213ff2f9c588401 | ### LLM Dashboard
This dashboard presents metrics for the LLM service, including key performance indicators such as request latency, time per output token latency,
and time to first token latency, among others. These metrics offer valuable insights into the efficiency and responsiveness of the LLM service,
helping to i... | ai_ref_knowledge | OPEA Documentation | ### LLM Dashboard
This dashboard presents metrics for the LLM service, including key performance indicators such as request latency, time per output token latency,
and time to first token latency, among others. These metrics offer valuable insights into the efficiency and responsiveness of the LLM service,
helping to i... | ### LLM Dashboard
This dashboard presents metrics for the LLM service, including key performance indicators such as request latency, time per output token latency,
and time to first token latency, among others. These metrics offer valuable insights into the efficiency and responsiveness of the LLM service,
helping to i... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1a4af214-3258-4b56-b1da-e0c5f5255473 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/AgentQnA.md | unknown | 03fc3abb-7506-4fbf-88e6-f3df4d8f1e1e | 11 | opea-semantic-v1 | 666f605d7fabf41c | question compared to the first. By tracing these functions, it becomes easier to understand the number of reasoning steps involved across the different questions. 
The OPEA Agent components allow for the integration of new tools into the React Agent when existing too... | ai_ref_knowledge | OPEA Documentation | question compared to the first. By tracing these functions, it becomes easier to understand the number of reasoning steps involved across the different questions. 
The OPEA Agent components allow for the integration of new tools into the React Agent when existing too... | question compared to the first. By tracing these functions, it becomes easier to understand the number of reasoning steps involved across the different questions. 
The OPEA Agent components allow for the integration of new tools into the React Agent when existing too... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1bd38ac0-b0e3-414a-8363-9527632d4a2c | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/AgentQnA.md | unknown | 03fc3abb-7506-4fbf-88e6-f3df4d8f1e1e | 4 | opea-semantic-v1 | f091e56824da0398 | in the example are set up. In the scenario below, the React Agent, SQL Agent, and RAG Agent are utilized within the AgentQnA example. 
By expanding the React Agent, the ReactAgentNodeLlama is identified as the core function implementing the ReactAgent. ![jaeger_react_init]... | ai_ref_knowledge | OPEA Documentation | in the example are set up. In the scenario below, the React Agent, SQL Agent, and RAG Agent are utilized within the AgentQnA example. 
By expanding the React Agent, the ReactAgentNodeLlama is identified as the core function implementing the ReactAgent. ![jaeger_react_init]... | in the example are set up. In the scenario below, the React Agent, SQL Agent, and RAG Agent are utilized within the AgentQnA example. 
By expanding the React Agent, the ReactAgentNodeLlama is identified as the core function implementing the ReactAgent. ![jaeger_react_init]... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1d38b6d3-4032-4502-b4c0-0546f8097957 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/AgentQnA.md | unknown | 03fc3abb-7506-4fbf-88e6-f3df4d8f1e1e | 20 | opea-semantic-v1 | f51590a2673e7250 | ### AgentQnA MicroServices Dashboard
This dashboard provides metrics for services within the AgentQnA microservices. By clicking the job_name, supported service names such as supervisor-react-agent, worker-rag-agent and worker-sql-agent will be shown. Select one of the supported services from the list. 
For the first question, the llm_generate function from the React Agent is called initially. Upon expanding the funct... | ai_ref_knowledge | OPEA Documentation | SQL Agent. The SQL Agent's traces contain more spans because it continues reasoning extensively, as no answer can be found in the SQL database. 
For the first question, the llm_generate function from the React Agent is called initially. Upon expanding the funct... | SQL Agent. The SQL Agent's traces contain more spans because it continues reasoning extensively, as no answer can be found in the SQL database. 
For the first question, the llm_generate function from the React Agent is called initially. Upon expanding the funct... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
3621297a-7ec3-4305-a57c-4ade304c367d | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/AgentQnA.md | unknown | 03fc3abb-7506-4fbf-88e6-f3df4d8f1e1e | 10 | opea-semantic-v1 | 7a24f1f7faf367f0 | first question. The 'search_sql_database' tool is called to retrieve relevant data, and the language model (LLM) is used to reason through the subsequent steps. 
Fewer reasoning steps are required to answer the second question compared to the first. By tracing t... | ai_ref_knowledge | OPEA Documentation | first question. The 'search_sql_database' tool is called to retrieve relevant data, and the language model (LLM) is used to reason through the subsequent steps. 
Fewer reasoning steps are required to answer the second question compared to the first. By tracing t... | first question. The 'search_sql_database' tool is called to retrieve relevant data, and the language model (LLM) is used to reason through the subsequent steps. 
Fewer reasoning steps are required to answer the second question compared to the first. By tracing t... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4a78ffb3-dba2-4d08-ac8b-b623619d1e02 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/AgentQnA.md | unknown | 03fc3abb-7506-4fbf-88e6-f3df4d8f1e1e | 6 | opea-semantic-v1 | 867a8cba7a5001b2 | Follow the steps in [AgentQnA validate services session](https://github.com/opea-project/GenAIExamples/tree/main/AgentQnA#validate-services) to test the AgentQnA application with some pre-defined questions. 
Once the agents respond to the two questions, four traces will be disp... | ai_ref_knowledge | OPEA Documentation | Follow the steps in [AgentQnA validate services session](https://github.com/opea-project/GenAIExamples/tree/main/AgentQnA#validate-services) to test the AgentQnA application with some pre-defined questions. 
Once the agents respond to the two questions, four traces will be disp... | Follow the steps in [AgentQnA validate services session](https://github.com/opea-project/GenAIExamples/tree/main/AgentQnA#validate-services) to test the AgentQnA application with some pre-defined questions. 
Once the agents respond to the two questions, four traces will be disp... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5695c26f-a351-4710-afb4-abddcd5cb4cc | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/AgentQnA.md | unknown | 03fc3abb-7506-4fbf-88e6-f3df4d8f1e1e | 16 | opea-semantic-v1 | ef539f04930bd494 | find an answer. After the 'search_sql_database' tool failed to provide an answer, the React Agent switched to the 'search_web_base' tool, quickly locating the answer. 
By examining the AgentNodeLlama trace from the SQL Agent, it is evident that numerous ... | ai_ref_knowledge | OPEA Documentation | find an answer. After the 'search_sql_database' tool failed to provide an answer, the React Agent switched to the 'search_web_base' tool, quickly locating the answer. 
By examining the AgentNodeLlama trace from the SQL Agent, it is evident that numerous ... | find an answer. After the 'search_sql_database' tool failed to provide an answer, the React Agent switched to the 'search_web_base' tool, quickly locating the answer. 
By examining the AgentNodeLlama trace from the SQL Agent, it is evident that numerous ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
76feb348-cb99-48e9-9177-ba161b18256c | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/AgentQnA.md | unknown | 03fc3abb-7506-4fbf-88e6-f3df4d8f1e1e | 17 | opea-semantic-v1 | 534af98cad2f3a03 | the SQL Agent, it is evident that numerous reasoning steps occurred due to the inability to find a suitable answer in the SQL database. 
## Telemetry Metrics with Grafana on Gaudi | ai_ref_knowledge | OPEA Documentation | the SQL Agent, it is evident that numerous reasoning steps occurred due to the inability to find a suitable answer in the SQL database. 
## Telemetry Metrics with Grafana on Gaudi | the SQL Agent, it is evident that numerous reasoning steps occurred due to the inability to find a suitable answer in the SQL database. 
## Telemetry Metrics with Grafana on Gaudi | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7de119e4-aba8-4429-bd4a-dd5dd4b45992 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/AgentQnA.md | unknown | 03fc3abb-7506-4fbf-88e6-f3df4d8f1e1e | 2 | opea-semantic-v1 | 5389020589f075ce | 1. [Telemetry Tracing with Jaeger on Gaudi](#telemetry-tracing-with-jaeger-on-gaudi) 2. [Telemetry Metrics with Grafana on Gaudi](#telemetry-metrics-with-grafana-on-gaudi)
## Telemetry Tracing with Jaeger on Gaudi | ai_ref_knowledge | OPEA Documentation | 1. [Telemetry Tracing with Jaeger on Gaudi](#telemetry-tracing-with-jaeger-on-gaudi) 2. [Telemetry Metrics with Grafana on Gaudi](#telemetry-metrics-with-grafana-on-gaudi)
## Telemetry Tracing with Jaeger on Gaudi | 1. [Telemetry Tracing with Jaeger on Gaudi](#telemetry-tracing-with-jaeger-on-gaudi) 2. [Telemetry Metrics with Grafana on Gaudi](#telemetry-metrics-with-grafana-on-gaudi)
## Telemetry Tracing with Jaeger on Gaudi | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8608bad1-0a90-4ad8-abce-a37e3b8cd2ad | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/AgentQnA.md | unknown | 03fc3abb-7506-4fbf-88e6-f3df4d8f1e1e | 12 | opea-semantic-v1 | 89767ea19907e21a | Consequently, the React Agent must employ the newly added web search tool to address the question regarding the most streamed albums on Spotify in 2024.
 | ai_ref_knowledge | OPEA Documentation | Consequently, the React Agent must employ the newly added web search tool to address the question regarding the most streamed albums on Spotify in 2024.
 | Consequently, the React Agent must employ the newly added web search tool to address the question regarding the most streamed albums on Spotify in 2024.
 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8a243a84-3eb5-4c72-9171-a36400556f0d | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/AgentQnA.md | unknown | 03fc3abb-7506-4fbf-88e6-f3df4d8f1e1e | 13 | opea-semantic-v1 | e522860d04953662 | 
After the agents respond to the two questions, four traces are displayed along the timeline. For the first question, the ReActAgentNodeLlama from the React Agent is invoked as an 'opea: llm_generate' trace, while the SQL Agent is not called. In contrast, for the second q... | ai_ref_knowledge | OPEA Documentation | 
After the agents respond to the two questions, four traces are displayed along the timeline. For the first question, the ReActAgentNodeLlama from the React Agent is invoked as an 'opea: llm_generate' trace, while the SQL Agent is not called. In contrast, for the second q... | 
After the agents respond to the two questions, four traces are displayed along the timeline. For the first question, the ReActAgentNodeLlama from the React Agent is invoked as an 'opea: llm_generate' trace, while the SQL Agent is not called. In contrast, for the second q... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8e11d1c1-24b9-4dcd-a11e-d0fb18829bc2 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/AgentQnA.md | unknown | 03fc3abb-7506-4fbf-88e6-f3df4d8f1e1e | 22 | opea-semantic-v1 | b503e009f2554ba5 | Agent. Additionally, the dashboard presents CPU and memory usage statistics for the React Agent, offering a comprehensive view of its performance and resource consumption. 
Similarly, the average response time latency for the worker-sql-agent will be displayed on... | ai_ref_knowledge | OPEA Documentation | Agent. Additionally, the dashboard presents CPU and memory usage statistics for the React Agent, offering a comprehensive view of its performance and resource consumption. 
Similarly, the average response time latency for the worker-sql-agent will be displayed on... | Agent. Additionally, the dashboard presents CPU and memory usage statistics for the React Agent, offering a comprehensive view of its performance and resource consumption. 
Similarly, the average response time latency for the worker-sql-agent will be displayed on... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8f148537-8f25-4f6f-a299-a470053f9f07 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/AgentQnA.md | unknown | 03fc3abb-7506-4fbf-88e6-f3df4d8f1e1e | 21 | opea-semantic-v1 | e9091713a4ffcd56 | clicking the job_name, supported service names such as supervisor-react-agent, worker-rag-agent and worker-sql-agent will be shown. Select one of the supported services from the list. 
The supervisor-react-agent service is highlighted with its average response ... | ai_ref_knowledge | OPEA Documentation | clicking the job_name, supported service names such as supervisor-react-agent, worker-rag-agent and worker-sql-agent will be shown. Select one of the supported services from the list. 
The supervisor-react-agent service is highlighted with its average response ... | clicking the job_name, supported service names such as supervisor-react-agent, worker-rag-agent and worker-sql-agent will be shown. Select one of the supported services from the list. 
The supervisor-react-agent service is highlighted with its average response ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
99a02786-93bc-41ca-99de-8e5c3ee12c44 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/AgentQnA.md | unknown | 03fc3abb-7506-4fbf-88e6-f3df4d8f1e1e | 15 | opea-semantic-v1 | dd5e66971f29bd2e | if it can conclude the process. If the React Agent were to use other tools instead of 'search_web_base', additional reasoning steps would be required. 
For the second question, the React Agent initially utilized the 'search_sql_database' tool instead of ... | ai_ref_knowledge | OPEA Documentation | if it can conclude the process. If the React Agent were to use other tools instead of 'search_web_base', additional reasoning steps would be required. 
For the second question, the React Agent initially utilized the 'search_sql_database' tool instead of ... | if it can conclude the process. If the React Agent were to use other tools instead of 'search_web_base', additional reasoning steps would be required. 
For the second question, the React Agent initially utilized the 'search_sql_database' tool instead of ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b087795d-b6ff-4b6e-b8cf-df86b39a5d05 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/AgentQnA.md | unknown | 03fc3abb-7506-4fbf-88e6-f3df4d8f1e1e | 19 | opea-semantic-v1 | 1a6ef5eac481b828 | monitor various aspects of the application, such as service execution times, resource utilization, and system health, enabling users to effectively manage and optimize the application.
### AgentQnA MicroServices Dashboard | ai_ref_knowledge | OPEA Documentation | monitor various aspects of the application, such as service execution times, resource utilization, and system health, enabling users to effectively manage and optimize the application.
### AgentQnA MicroServices Dashboard | monitor various aspects of the application, such as service execution times, resource utilization, and system health, enabling users to effectively manage and optimize the application.
### AgentQnA MicroServices Dashboard | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c0cc8026-21a6-4675-88cf-9c2f27fef87f | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/AgentQnA.md | unknown | 03fc3abb-7506-4fbf-88e6-f3df4d8f1e1e | 8 | opea-semantic-v1 | 591e26b740687444 | results from the 'search_sql_database', the function employs the LLM again to reason whether additional actions are necessary or if it can conclude the process. 
In the AgentNodeLlama trace, the 'search_sql_database' tool retrieves data from the SQL database. Withi... | ai_ref_knowledge | OPEA Documentation | results from the 'search_sql_database', the function employs the LLM again to reason whether additional actions are necessary or if it can conclude the process. 
In the AgentNodeLlama trace, the 'search_sql_database' tool retrieves data from the SQL database. Withi... | results from the 'search_sql_database', the function employs the LLM again to reason whether additional actions are necessary or if it can conclude the process. 
In the AgentNodeLlama trace, the 'search_sql_database' tool retrieves data from the SQL database. Withi... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
cf13b719-78e4-4fdd-bf39-d938cbacf503 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/AgentQnA.md | unknown | 03fc3abb-7506-4fbf-88e6-f3df4d8f1e1e | 24 | opea-semantic-v1 | fafa942eb3ea366c | others. These metrics offer valuable insights into the efficiency and responsiveness of the LLM service, helping to identify areas for optimization and ensuring smooth operation.
 | ai_ref_knowledge | OPEA Documentation | others. These metrics offer valuable insights into the efficiency and responsiveness of the LLM service, helping to identify areas for optimization and ensuring smooth operation.
 | others. These metrics offer valuable insights into the efficiency and responsiveness of the LLM service, helping to identify areas for optimization and ensuring smooth operation.
 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
cfe00afb-9505-4ede-823e-2224ac1669a2 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/AgentQnA.md | unknown | 03fc3abb-7506-4fbf-88e6-f3df4d8f1e1e | 5 | opea-semantic-v1 | a28706dd59625971 | By expanding the React Agent, the ReactAgentNodeLlama is identified as the core function implementing the ReactAgent. 
Follow the steps in [AgentQnA validate services session](https://github.com/opea-project/GenAIExamples/tree/main/AgentQnA#validate-services) t... | ai_ref_knowledge | OPEA Documentation | By expanding the React Agent, the ReactAgentNodeLlama is identified as the core function implementing the ReactAgent. 
Follow the steps in [AgentQnA validate services session](https://github.com/opea-project/GenAIExamples/tree/main/AgentQnA#validate-services) t... | By expanding the React Agent, the ReactAgentNodeLlama is identified as the core function implementing the ReactAgent. 
Follow the steps in [AgentQnA validate services session](https://github.com/opea-project/GenAIExamples/tree/main/AgentQnA#validate-services) t... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e9f83a6f-0a99-47c3-98b6-1a81ba1357d6 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/AgentQnA.md | unknown | 03fc3abb-7506-4fbf-88e6-f3df4d8f1e1e | 7 | opea-semantic-v1 | f60471cbcbbcf049 | timeline. Initially, the ReActAgentNodeLlama from the React Agent is invoked for each question, followed by a call to the AgentNodeLlama from the SQL Agent. 
For the first question, the ReActAgentNodeLlama is invoked initially. Upon expanding the function, it utilize... | ai_ref_knowledge | OPEA Documentation | timeline. Initially, the ReActAgentNodeLlama from the React Agent is invoked for each question, followed by a call to the AgentNodeLlama from the SQL Agent. 
For the first question, the ReActAgentNodeLlama is invoked initially. Upon expanding the function, it utilize... | timeline. Initially, the ReActAgentNodeLlama from the React Agent is invoked for each question, followed by a call to the AgentNodeLlama from the SQL Agent. 
For the first question, the ReActAgentNodeLlama is invoked initially. Upon expanding the function, it utilize... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
eb1ff1b3-004b-4c4d-8406-991db3211939 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/AgentQnA.md | unknown | 03fc3abb-7506-4fbf-88e6-f3df4d8f1e1e | 3 | opea-semantic-v1 | f9230069292677ba | ## Telemetry Tracing with Jaeger on Gaudi
Initially, all agents in the example are set up. In the scenario below, the React Agent, SQL Agent, and RAG Agent are utilized within the AgentQnA example.  | ai_ref_knowledge | OPEA Documentation | ## Telemetry Tracing with Jaeger on Gaudi
Initially, all agents in the example are set up. In the scenario below, the React Agent, SQL Agent, and RAG Agent are utilized within the AgentQnA example.  | ## Telemetry Tracing with Jaeger on Gaudi
Initially, all agents in the example are set up. In the scenario below, the React Agent, SQL Agent, and RAG Agent are utilized within the AgentQnA example.  | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f4481790-3e2c-4229-81b3-a735d725353e | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/AgentQnA.md | unknown | 03fc3abb-7506-4fbf-88e6-f3df4d8f1e1e | 1 | opea-semantic-v1 | cce3be83f53fa7b6 | Agent service, alongside a vLLM Grafana dashboard. A dashboard for monitoring CPU statistics is also available, offering comprehensive insights into system performance and resource utilization.
## Table of contents | ai_ref_knowledge | OPEA Documentation | Agent service, alongside a vLLM Grafana dashboard. A dashboard for monitoring CPU statistics is also available, offering comprehensive insights into system performance and resource utilization.
## Table of contents | Agent service, alongside a vLLM Grafana dashboard. A dashboard for monitoring CPU statistics is also available, offering comprehensive insights into system performance and resource utilization.
## Table of contents | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f60ac2c9-d3c0-4bec-a0bf-8977742bc6f2 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/AgentQnA.md | unknown | 03fc3abb-7506-4fbf-88e6-f3df4d8f1e1e | 9 | opea-semantic-v1 | 005e7d4a5989f028 | __call__ function, the language model (LLM) is then employed to reason about the next steps, determining how to proceed based on the data obtained. 
For the second question, the ReActAgentNodeLlama is invoked first, following a similar process follow as in the first qu... | ai_ref_knowledge | OPEA Documentation | __call__ function, the language model (LLM) is then employed to reason about the next steps, determining how to proceed based on the data obtained. 
For the second question, the ReActAgentNodeLlama is invoked first, following a similar process follow as in the first qu... | __call__ function, the language model (LLM) is then employed to reason about the next steps, determining how to proceed based on the data obtained. 
For the second question, the ReActAgentNodeLlama is invoked first, following a similar process follow as in the first qu... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
fc92b4e0-c06b-45f9-9f21-08635ec9b5ca | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/AgentQnA.md | unknown | 03fc3abb-7506-4fbf-88e6-f3df4d8f1e1e | 18 | opea-semantic-v1 | eb1422b2f603f9a1 | ## Telemetry Metrics with Grafana on Gaudi
The AgentQnA application offers several useful dashboards that provide valuable insights into its performance and operations. These dashboards are designed to help monitor various aspects of the application, such as service execution times, resource utilization, and system hea... | ai_ref_knowledge | OPEA Documentation | ## Telemetry Metrics with Grafana on Gaudi
The AgentQnA application offers several useful dashboards that provide valuable insights into its performance and operations. These dashboards are designed to help monitor various aspects of the application, such as service execution times, resource utilization, and system hea... | ## Telemetry Metrics with Grafana on Gaudi
The AgentQnA application offers several useful dashboards that provide valuable insights into its performance and operations. These dashboards are designed to help monitor various aspects of the application, such as service execution times, resource utilization, and system hea... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0f2f0d5d-5f16-42b6-a005-84272d66260a | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/ChatQnA.md | unknown | 3bfc60b3-8126-4025-a3b6-427323d708d7 | 3 | opea-semantic-v1 | 0c1a951130f09f00 | on Gaudi, stream requests are displayed under opea: llm_generate_stream. This trace contains two spans: one for the first token and another for all subsequent tokens.
 | ai_ref_knowledge | OPEA Documentation | on Gaudi, stream requests are displayed under opea: llm_generate_stream. This trace contains two spans: one for the first token and another for all subsequent tokens.
 | on Gaudi, stream requests are displayed under opea: llm_generate_stream. This trace contains two spans: one for the first token and another for all subsequent tokens.
 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
10251ffa-6da6-4c7a-bb74-9390d9a04713 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/ChatQnA.md | unknown | 3bfc60b3-8126-4025-a3b6-427323d708d7 | 2 | opea-semantic-v1 | b27568dc790e827e | ## Telemetry Tracing with Jaeger on Gaudi
After ChatQnA processes a question, two traces should appear along the timeline. The trace for opea: ServiceOrchestrator.schedule runs on the CPU and includes seven spans, one of which represents the LLM service running on CPU. For LLM functions executed on Gaudi, stream reques... | ai_ref_knowledge | OPEA Documentation | ## Telemetry Tracing with Jaeger on Gaudi
After ChatQnA processes a question, two traces should appear along the timeline. The trace for opea: ServiceOrchestrator.schedule runs on the CPU and includes seven spans, one of which represents the LLM service running on CPU. For LLM functions executed on Gaudi, stream reques... | ## Telemetry Tracing with Jaeger on Gaudi
After ChatQnA processes a question, two traces should appear along the timeline. The trace for opea: ServiceOrchestrator.schedule runs on the CPU and includes seven spans, one of which represents the LLM service running on CPU. For LLM functions executed on Gaudi, stream reques... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
193954f1-a9b3-44fc-88d6-3a0388611ab2 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/ChatQnA.md | unknown | 3bfc60b3-8126-4025-a3b6-427323d708d7 | 12 | opea-semantic-v1 | f8e202f76d04dde7 | 
Overall, the traces on the CPU consist of seven spans and are represented as larger circles. In contrast, the traces on Gaudi have two spans and are depicted as smaller circles. The diagrams below illustrate a run with 16 user requests, re... | ai_ref_knowledge | OPEA Documentation | 
Overall, the traces on the CPU consist of seven spans and are represented as larger circles. In contrast, the traces on Gaudi have two spans and are depicted as smaller circles. The diagrams below illustrate a run with 16 user requests, re... | 
Overall, the traces on the CPU consist of seven spans and are represented as larger circles. In contrast, the traces on Gaudi have two spans and are depicted as smaller circles. The diagrams below illustrate a run with 16 user requests, re... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
21726678-d9a9-45d1-a0b8-81ad84bdeaea | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/ChatQnA.md | unknown | 3bfc60b3-8126-4025-a3b6-427323d708d7 | 5 | opea-semantic-v1 | 9a9da9bb3bbac0c0 | It provides insights into the orchestration and scheduling of services within the ChatQnA megaservice, highlighting the execution flow during the process.
 | ai_ref_knowledge | OPEA Documentation | It provides insights into the orchestration and scheduling of services within the ChatQnA megaservice, highlighting the execution flow during the process.
 | It provides insights into the orchestration and scheduling of services within the ChatQnA megaservice, highlighting the execution flow during the process.
 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
24cbb6ce-4f3a-4009-9f98-8c03906dcfa9 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/ChatQnA.md | unknown | 3bfc60b3-8126-4025-a3b6-427323d708d7 | 14 | opea-semantic-v1 | 309a2c3929dd8455 | ## Telemetry Metrics with Grafana on Gaudi
The ChatQnA application offers several useful dashboards that provide valuable insights into its performance and operations. These dashboards are designed to help monitor various aspects of the application, such as service execution times, resource utilization, and system heal... | ai_ref_knowledge | OPEA Documentation | ## Telemetry Metrics with Grafana on Gaudi
The ChatQnA application offers several useful dashboards that provide valuable insights into its performance and operations. These dashboards are designed to help monitor various aspects of the application, such as service execution times, resource utilization, and system heal... | ## Telemetry Metrics with Grafana on Gaudi
The ChatQnA application offers several useful dashboards that provide valuable insights into its performance and operations. These dashboards are designed to help monitor various aspects of the application, such as service execution times, resource utilization, and system heal... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
2b6025cd-ae89-47a0-8ac9-ae8b10748c69 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/ChatQnA.md | unknown | 3bfc60b3-8126-4025-a3b6-427323d708d7 | 16 | opea-semantic-v1 | ff0e4f1b3a79dded | ### ChatQnA MegaService Dashboard
This dashboard provides metrics for services within the ChatQnA megaservice. The chatqna-backend-server service, which functions as the megaservice,
is highlighted with its average response time displayed across multiple runs. Additionally, the dashboard presents CPU and memory usage s... | ai_ref_knowledge | OPEA Documentation | ### ChatQnA MegaService Dashboard
This dashboard provides metrics for services within the ChatQnA megaservice. The chatqna-backend-server service, which functions as the megaservice,
is highlighted with its average response time displayed across multiple runs. Additionally, the dashboard presents CPU and memory usage s... | ### ChatQnA MegaService Dashboard
This dashboard provides metrics for services within the ChatQnA megaservice. The chatqna-backend-server service, which functions as the megaservice,
is highlighted with its average response time displayed across multiple runs. Additionally, the dashboard presents CPU and memory usage s... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
456a47ca-40ca-4804-acf5-81d3263730ad | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/ChatQnA.md | unknown | 3bfc60b3-8126-4025-a3b6-427323d708d7 | 1 | opea-semantic-v1 | 1b47519fdae3e4f7 | its megaservice, alongside a vLLM Grafana dashboard. A dashboard for monitoring CPU statistics is also available, offering comprehensive insights into system performance and resource utilization.
## Table of contents | ai_ref_knowledge | OPEA Documentation | its megaservice, alongside a vLLM Grafana dashboard. A dashboard for monitoring CPU statistics is also available, offering comprehensive insights into system performance and resource utilization.
## Table of contents | its megaservice, alongside a vLLM Grafana dashboard. A dashboard for monitoring CPU statistics is also available, offering comprehensive insights into system performance and resource utilization.
## Table of contents | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
48de5264-a6e1-4acc-8ef4-01b9c6d4117d | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/ChatQnA.md | unknown | 3bfc60b3-8126-4025-a3b6-427323d708d7 | 15 | opea-semantic-v1 | f91b2744b93a3609 | monitor various aspects of the application, such as service execution times, resource utilization, and system health, enabling users to effectively manage and optimize the application.
### ChatQnA MegaService Dashboard | ai_ref_knowledge | OPEA Documentation | monitor various aspects of the application, such as service execution times, resource utilization, and system health, enabling users to effectively manage and optimize the application.
### ChatQnA MegaService Dashboard | monitor various aspects of the application, such as service execution times, resource utilization, and system health, enabling users to effectively manage and optimize the application.
### ChatQnA MegaService Dashboard | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5735e7a1-bde2-4a12-990f-e16df571a98c | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/ChatQnA.md | unknown | 3bfc60b3-8126-4025-a3b6-427323d708d7 | 10 | opea-semantic-v1 | 06d2c8ef8a5dd9b3 | 
Clicking on the opea: llm_generate_stream trace will expand to reveal two spans along the timeline. The first span represents the execution time for the first token, which took 15.12 ms in this run. The second span captures the execution time for all ... | ai_ref_knowledge | OPEA Documentation | 
Clicking on the opea: llm_generate_stream trace will expand to reveal two spans along the timeline. The first span represents the execution time for the first token, which took 15.12 ms in this run. The second span captures the execution time for all ... | 
Clicking on the opea: llm_generate_stream trace will expand to reveal two spans along the timeline. The first span represents the execution time for the first token, which took 15.12 ms in this run. The second span captures the execution time for all ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
59bc8957-9048-4bda-8d4f-cd1452f8c760 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/ChatQnA.md | unknown | 3bfc60b3-8126-4025-a3b6-427323d708d7 | 4 | opea-semantic-v1 | 876d1a0371914b29 | The first trace along the timeline is opea: ServiceOrchestrator.schedule, which runs on the CPU.
It provides insights into the orchestration and scheduling of services within the ChatQnA megaservice, highlighting the execution flow during the process. | ai_ref_knowledge | OPEA Documentation | The first trace along the timeline is opea: ServiceOrchestrator.schedule, which runs on the CPU.
It provides insights into the orchestration and scheduling of services within the ChatQnA megaservice, highlighting the execution flow during the process. | The first trace along the timeline is opea: ServiceOrchestrator.schedule, which runs on the CPU.
It provides insights into the orchestration and scheduling of services within the ChatQnA megaservice, highlighting the execution flow during the process. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
632785db-d00c-4b38-9400-46b103f6b2eb | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/ChatQnA.md | unknown | 3bfc60b3-8126-4025-a3b6-427323d708d7 | 11 | opea-semantic-v1 | 04f85faf53f60d3f | took 15.12 ms in this run. The second span captures the execution time for all subsequent tokens, taking 920 ms as shown in the diagram.
 | ai_ref_knowledge | OPEA Documentation | took 15.12 ms in this run. The second span captures the execution time for all subsequent tokens, taking 920 ms as shown in the diagram.
 | took 15.12 ms in this run. The second span captures the execution time for all subsequent tokens, taking 920 ms as shown in the diagram.
 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6554cd4f-ee3f-49f6-ada5-adb042fccd3e | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/ChatQnA.md | unknown | 3bfc60b3-8126-4025-a3b6-427323d708d7 | 20 | opea-semantic-v1 | 0a05e28702e320d6 | others. These metrics offer valuable insights into the efficiency and responsiveness of the LLM service, helping to identify areas for optimization and ensuring smooth operation.
 | ai_ref_knowledge | OPEA Documentation | others. These metrics offer valuable insights into the efficiency and responsiveness of the LLM service, helping to identify areas for optimization and ensuring smooth operation.
 | others. These metrics offer valuable insights into the efficiency and responsiveness of the LLM service, helping to identify areas for optimization and ensuring smooth operation.
 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
835af9ec-8696-44cf-9d14-7803ffb55cef | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/ChatQnA.md | unknown | 3bfc60b3-8126-4025-a3b6-427323d708d7 | 19 | opea-semantic-v1 | 4de989f72c901278 | These metrics provide insights into the performance and resource utilization of these services, allowing for a more comprehensive understanding of the ChatQnA application's overall operation.
 | ai_ref_knowledge | OPEA Documentation | These metrics provide insights into the performance and resource utilization of these services, allowing for a more comprehensive understanding of the ChatQnA application's overall operation.
 | These metrics provide insights into the performance and resource utilization of these services, allowing for a more comprehensive understanding of the ChatQnA application's overall operation.
 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
873e5349-3afd-40e7-9542-c0470628b461 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/ChatQnA.md | unknown | 3bfc60b3-8126-4025-a3b6-427323d708d7 | 8 | opea-semantic-v1 | ed72c56e9ed92712 | 
The second trace following the schedule trace is opea: llm_generate_stream, which operates on Gaudi, as depicted in the diagram. This trace provides insights into the execution of LLM functions on Gaudi,
highlighting the processing of stream r... | ai_ref_knowledge | OPEA Documentation | 
The second trace following the schedule trace is opea: llm_generate_stream, which operates on Gaudi, as depicted in the diagram. This trace provides insights into the execution of LLM functions on Gaudi,
highlighting the processing of stream r... | 
The second trace following the schedule trace is opea: llm_generate_stream, which operates on Gaudi, as depicted in the diagram. This trace provides insights into the execution of LLM functions on Gaudi,
highlighting the processing of stream r... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
9489dbcc-ff9a-4628-80b2-51bab4da1f8c | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/ChatQnA.md | unknown | 3bfc60b3-8126-4025-a3b6-427323d708d7 | 9 | opea-semantic-v1 | 2abc1f35c487e73b | This trace provides insights into the execution of LLM functions on Gaudi, highlighting the processing of stream requests and the associated spans for token generation.
 | ai_ref_knowledge | OPEA Documentation | This trace provides insights into the execution of LLM functions on Gaudi, highlighting the processing of stream requests and the associated spans for token generation.
 | This trace provides insights into the execution of LLM functions on Gaudi, highlighting the processing of stream requests and the associated spans for token generation.
 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
9857a1ab-2683-4d1d-836d-34db6f2a283b | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/ChatQnA.md | unknown | 3bfc60b3-8126-4025-a3b6-427323d708d7 | 7 | opea-semantic-v1 | 89e27eb6f41733d5 | an execution time of 41.99 ms. These spans provide a detailed breakdown of the execution flow and timing for each component within the service orchestration.
 | ai_ref_knowledge | OPEA Documentation | an execution time of 41.99 ms. These spans provide a detailed breakdown of the execution flow and timing for each component within the service orchestration.
 | an execution time of 41.99 ms. These spans provide a detailed breakdown of the execution flow and timing for each component within the service orchestration.
 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
9b3e1691-bee3-4bcc-afd7-0d5479471403 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/ChatQnA.md | unknown | 3bfc60b3-8126-4025-a3b6-427323d708d7 | 0 | opea-semantic-v1 | eb48d4a2627aa6ef | # OpenTelemetry on ChatQnA Application
Each microservice in ChatQnA is instrumented with opea_telemetry, enabling Jaeger to provide a detailed time breakdown across microservices for each request. Additionally, ChatQnA features a pre-defined Grafana dashboard for its megaservice, alongside a vLLM Grafana dashboard. A d... | ai_ref_knowledge | OPEA Documentation | # OpenTelemetry on ChatQnA Application
Each microservice in ChatQnA is instrumented with opea_telemetry, enabling Jaeger to provide a detailed time breakdown across microservices for each request. Additionally, ChatQnA features a pre-defined Grafana dashboard for its megaservice, alongside a vLLM Grafana dashboard. A d... | # OpenTelemetry on ChatQnA Application
Each microservice in ChatQnA is instrumented with opea_telemetry, enabling Jaeger to provide a detailed time breakdown across microservices for each request. Additionally, ChatQnA features a pre-defined Grafana dashboard for its megaservice, alongside a vLLM Grafana dashboard. A d... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a2ee1895-0284-4a39-a993-7036cd588920 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/ChatQnA.md | unknown | 3bfc60b3-8126-4025-a3b6-427323d708d7 | 6 | opea-semantic-v1 | 12f17a4f43ad5fd3 | 
Clicking on the opea: ServiceOrchestrator.schedule trace will expand to reveal seven spans along the timeline. The first span represents the main schedule function, which has minimal self-execution time, indicated in black. The second span corresponds to ... | ai_ref_knowledge | OPEA Documentation | 
Clicking on the opea: ServiceOrchestrator.schedule trace will expand to reveal seven spans along the timeline. The first span represents the main schedule function, which has minimal self-execution time, indicated in black. The second span corresponds to ... | 
Clicking on the opea: ServiceOrchestrator.schedule trace will expand to reveal seven spans along the timeline. The first span represents the main schedule function, which has minimal self-execution time, indicated in black. The second span corresponds to ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e1663bac-f55c-4aaf-bf8b-fa8035b8b590 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/ChatQnA.md | unknown | 3bfc60b3-8126-4025-a3b6-427323d708d7 | 13 | opea-semantic-v1 | 7c8b6c82c8ca18c6 | circles, representing CPU traces, took less time than the smaller circles, indicating that the requests required more processing time on Gaudi compared to the CPU.
. | ai_ref_knowledge | OPEA Documentation | circles, representing CPU traces, took less time than the smaller circles, indicating that the requests required more processing time on Gaudi compared to the CPU.
. | circles, representing CPU traces, took less time than the smaller circles, indicating that the requests required more processing time on Gaudi compared to the CPU.
. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f955dad3-a7b8-4257-b301-0c2fc561a69b | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/ChatQnA.md | unknown | 3bfc60b3-8126-4025-a3b6-427323d708d7 | 17 | opea-semantic-v1 | ce826f12e8a1f1d9 | across multiple runs. Additionally, the dashboard presents CPU and memory usage statistics for the megaservice, offering a comprehensive view of its performance and resource consumption.
 | ai_ref_knowledge | OPEA Documentation | across multiple runs. Additionally, the dashboard presents CPU and memory usage statistics for the megaservice, offering a comprehensive view of its performance and resource consumption.
 | across multiple runs. Additionally, the dashboard presents CPU and memory usage statistics for the megaservice, offering a comprehensive view of its performance and resource consumption.
 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
faab1c82-d050-40f8-acc9-faca6ce92da5 | OPEA Documentation | file://datasets/opea-docs/tutorial/OpenTelemetry/deploy/ChatQnA.md | unknown | 3bfc60b3-8126-4025-a3b6-427323d708d7 | 18 | opea-semantic-v1 | 419bf40af9ec2d8d | 
The dashboard can also display metrics for the dataprep-redis-service and the retriever service. These metrics provide insights into the performance and resource utilization of these services,
allowing for a more comprehensive understanding of the ChatQnA ... | ai_ref_knowledge | OPEA Documentation | 
The dashboard can also display metrics for the dataprep-redis-service and the retriever service. These metrics provide insights into the performance and resource utilization of these services,
allowing for a more comprehensive understanding of the ChatQnA ... | 
The dashboard can also display metrics for the dataprep-redis-service and the retriever service. These metrics provide insights into the performance and resource utilization of these services,
allowing for a more comprehensive understanding of the ChatQnA ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
2327ee21-e897-4bdc-abe2-858b252c3d4b | OPEA Documentation | file://datasets/opea-docs/404.rst | unknown | bad82f82-d2c1-4199-bce1-bc0a1de01afb | 2 | opea-semantic-v1 | 6c1f58bde5c70d73 | Try using the navigation links on the left of this page to navigate the major sections of our site, or use the document search box.
.. raw:: html | ai_ref_knowledge | OPEA Documentation | Try using the navigation links on the left of this page to navigate the major sections of our site, or use the document search box.
.. raw:: html | Try using the navigation links on the left of this page to navigate the major sections of our site, or use the document search box.
.. raw:: html | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
74b4dd36-5897-40de-ae62-50645d481ca9 | OPEA Documentation | file://datasets/opea-docs/404.rst | unknown | bad82f82-d2c1-4199-bce1-bc0a1de01afb | 0 | opea-semantic-v1 | 50ed0949836d67d4 | Sorry. The page you requested was not found on this site.
Check the address for misspellings. It's also possible we've removed or renamed the page you're looking for. | ai_ref_knowledge | OPEA Documentation | Sorry. The page you requested was not found on this site.
Check the address for misspellings. It's also possible we've removed or renamed the page you're looking for. | Sorry. The page you requested was not found on this site.
Check the address for misspellings. It's also possible we've removed or renamed the page you're looking for. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b39cd9e5-9f14-45e3-9a33-0ab79840f989 | OPEA Documentation | file://datasets/opea-docs/404.rst | unknown | bad82f82-d2c1-4199-bce1-bc0a1de01afb | 1 | opea-semantic-v1 | 37124dab377477e6 | Check the address for misspellings. It's also possible we've removed or renamed the page you're looking for.
Try using the navigation links on the left of this page to navigate
the major sections of our site, or use the document search box. | ai_ref_knowledge | OPEA Documentation | Check the address for misspellings. It's also possible we've removed or renamed the page you're looking for.
Try using the navigation links on the left of this page to navigate
the major sections of our site, or use the document search box. | Check the address for misspellings. It's also possible we've removed or renamed the page you're looking for.
Try using the navigation links on the left of this page to navigate
the major sections of our site, or use the document search box. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
3ad2f35c-085b-4cd4-924c-9530f59e42f9 | OPEA Documentation | file://datasets/opea-docs/README.rst | unknown | e43d5cec-9456-4269-b6dc-615a04d53934 | 0 | opea-semantic-v1 | 75eb70f468d85c6a | ##########################
This repository holds the source and configuration files used to generate the
`OPEA Project documentation web site`_ from all the documentation maintained in
this docs repo and all the GenAI\* repos. | ai_ref_knowledge | OPEA Documentation | ##########################
This repository holds the source and configuration files used to generate the
`OPEA Project documentation web site`_ from all the documentation maintained in
this docs repo and all the GenAI\* repos. | ##########################
This repository holds the source and configuration files used to generate the
`OPEA Project documentation web site`_ from all the documentation maintained in
this docs repo and all the GenAI\* repos. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7c349768-2306-48b9-bd21-70873f62f0b8 | OPEA Documentation | file://datasets/opea-docs/community/TSC.rst | unknown | a42c67e2-8226-443e-8d13-6ffd3f51b95b | 3 | opea-semantic-v1 | 91ed733bee1ce136 | .. list-table:: TSC Members (as of September 6, 2024)
* - TSC Member Name
- Member's Title and Company
* - `Malini Bhandaru <https://www.linkedin.com/in/malinibhandaru/>`_ (Chair)
- Senior Principal Engineer, Intel
* - `Amr Abdelhalem <https://www.linkedin.com/in/amrhalem/>`_
- SVP, Head of Cloud Platforms, Fideli... | ai_ref_knowledge | OPEA Documentation | .. list-table:: TSC Members (as of September 6, 2024)
* - TSC Member Name
- Member's Title and Company
* - `Malini Bhandaru <https://www.linkedin.com/in/malinibhandaru/>`_ (Chair)
- Senior Principal Engineer, Intel
* - `Amr Abdelhalem <https://www.linkedin.com/in/amrhalem/>`_
- SVP, Head of Cloud Platforms, Fideli... | .. list-table:: TSC Members (as of September 6, 2024)
* - TSC Member Name
- Member's Title and Company
* - `Malini Bhandaru <https://www.linkedin.com/in/malinibhandaru/>`_ (Chair)
- Senior Principal Engineer, Intel
* - `Amr Abdelhalem <https://www.linkedin.com/in/amrhalem/>`_
- SVP, Head of Cloud Platforms, Fideli... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a070570d-585b-4599-8d8e-f846e650044c | OPEA Documentation | file://datasets/opea-docs/community/TSC.rst | unknown | a42c67e2-8226-443e-8d13-6ffd3f51b95b | 2 | opea-semantic-v1 | dbcf044bf9d710d5 | necessary, voting on technical matters relating to the code base that affect multiple sub-projects; * coordinating any marketing, events, or communications regarding the OPEA project.
Refer to the :doc:`OPEA Charter <charter>` for more details. | ai_ref_knowledge | OPEA Documentation | necessary, voting on technical matters relating to the code base that affect multiple sub-projects; * coordinating any marketing, events, or communications regarding the OPEA project.
Refer to the :doc:`OPEA Charter <charter>` for more details. | necessary, voting on technical matters relating to the code base that affect multiple sub-projects; * coordinating any marketing, events, or communications regarding the OPEA project.
Refer to the :doc:`OPEA Charter <charter>` for more details. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a08800d4-3366-424c-afd7-f384eec0a1ae | OPEA Documentation | file://datasets/opea-docs/community/TSC.rst | unknown | a42c67e2-8226-443e-8d13-6ffd3f51b95b | 1 | opea-semantic-v1 | 2a984fdec059215c | As defined in the :doc:`OPEA Charter <charter>`, the Technical Steering Committee is responsible for all technical oversight of the OPEA project including:
* coordinating the technical direction of the OPEA project;
* approving project or system proposals
* creating committees or working groups (for example, an executi... | ai_ref_knowledge | OPEA Documentation | As defined in the :doc:`OPEA Charter <charter>`, the Technical Steering Committee is responsible for all technical oversight of the OPEA project including:
* coordinating the technical direction of the OPEA project;
* approving project or system proposals
* creating committees or working groups (for example, an executi... | As defined in the :doc:`OPEA Charter <charter>`, the Technical Steering Committee is responsible for all technical oversight of the OPEA project including:
* coordinating the technical direction of the OPEA project;
* approving project or system proposals
* creating committees or working groups (for example, an executi... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a1997e91-0696-4b52-b855-a2c0cf508408 | OPEA Documentation | file://datasets/opea-docs/community/TSC.rst | unknown | a42c67e2-8226-443e-8d13-6ffd3f51b95b | 0 | opea-semantic-v1 | 575479797b3a24fc | Technical Steering Committee (TSC) ##################################
As defined in the :doc:`OPEA Charter <charter>`, the Technical Steering
Committee is responsible for all technical oversight of the OPEA project
including: | ai_ref_knowledge | OPEA Documentation | Technical Steering Committee (TSC) ##################################
As defined in the :doc:`OPEA Charter <charter>`, the Technical Steering
Committee is responsible for all technical oversight of the OPEA project
including: | Technical Steering Committee (TSC) ##################################
As defined in the :doc:`OPEA Charter <charter>`, the Technical Steering
Committee is responsible for all technical oversight of the OPEA project
including: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
33e70f0d-bb04-42a1-9dc2-d7060bda7029 | OPEA Documentation | file://datasets/opea-docs/community/index.rst | unknown | 2aecc3a7-fdf7-490b-a58c-87d7371a5efc | 1 | opea-semantic-v1 | 5c2c8c0a3e73769e | join the developer community and the community is always willing to help its members and the User Community to get the most out of OPEA.
Resources
********* | ai_ref_knowledge | OPEA Documentation | join the developer community and the community is always willing to help its members and the User Community to get the most out of OPEA.
Resources
********* | join the developer community and the community is always willing to help its members and the User Community to get the most out of OPEA.
Resources
********* | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
40686711-4c19-4957-b879-a67cf5053d85 | OPEA Documentation | file://datasets/opea-docs/community/index.rst | unknown | 2aecc3a7-fdf7-490b-a58c-87d7371a5efc | 6 | opea-semantic-v1 | 26570359cf2c62e2 | Github issues system within each of the major repositories such as: https://github.com/opea-project/GenAIComps/issues. You can browse through the reported issues and submit issues of your own.
* **Mailing List**: TBD | ai_ref_knowledge | OPEA Documentation | Github issues system within each of the major repositories such as: https://github.com/opea-project/GenAIComps/issues. You can browse through the reported issues and submit issues of your own.
* **Mailing List**: TBD | Github issues system within each of the major repositories such as: https://github.com/opea-project/GenAIComps/issues. You can browse through the reported issues and submit issues of your own.
* **Mailing List**: TBD | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6bc9cc72-ed4b-442e-a431-a4f57b466ccf | OPEA Documentation | file://datasets/opea-docs/community/index.rst | unknown | 2aecc3a7-fdf7-490b-a58c-87d7371a5efc | 4 | opea-semantic-v1 | 9ee0857997058ad2 | GitHub repository at https://github.com/opea-project. You'll find information about getting access to the repository and how to contribute to the project in this :doc:`Contribution Guide <CONTRIBUTING>`.
* **Documentation**: Project technical documentation is developed
along with the project's code, and can be found a... | ai_ref_knowledge | OPEA Documentation | GitHub repository at https://github.com/opea-project. You'll find information about getting access to the repository and how to contribute to the project in this :doc:`Contribution Guide <CONTRIBUTING>`.
* **Documentation**: Project technical documentation is developed
along with the project's code, and can be found a... | GitHub repository at https://github.com/opea-project. You'll find information about getting access to the repository and how to contribute to the project in this :doc:`Contribution Guide <CONTRIBUTING>`.
* **Documentation**: Project technical documentation is developed
along with the project's code, and can be found a... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a24f808f-a656-4d88-950b-9be77b005f6d | OPEA Documentation | file://datasets/opea-docs/community/index.rst | unknown | 2aecc3a7-fdf7-490b-a58c-87d7371a5efc | 0 | opea-semantic-v1 | a5466bbba3485dad | Welcome to the OPEA project community!
The OPEA Community includes developers from member organizations and the general
community all joining in the development of the project. Members contribute and
discuss ideas, submit bugs and bug fixes, and improve documentation. They also
help those in need through the community'... | ai_ref_knowledge | OPEA Documentation | Welcome to the OPEA project community!
The OPEA Community includes developers from member organizations and the general
community all joining in the development of the project. Members contribute and
discuss ideas, submit bugs and bug fixes, and improve documentation. They also
help those in need through the community'... | Welcome to the OPEA project community!
The OPEA Community includes developers from member organizations and the general
community all joining in the development of the project. Members contribute and
discuss ideas, submit bugs and bug fixes, and improve documentation. They also
help those in need through the community'... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
abb892aa-e653-45ad-aeea-73e5b1210a33 | OPEA Documentation | file://datasets/opea-docs/community/index.rst | unknown | 2aecc3a7-fdf7-490b-a58c-87d7371a5efc | 2 | opea-semantic-v1 | 9297d1f3e89cc745 | Here's a quick summary of resources to find your way around the OPEA Project support systems:
* **OPEA Project Website**: The https://opea.dev website is the
central source of information about what's going on with OPEA. On this site, you'll
find background and current information about the project as well as
releva... | ai_ref_knowledge | OPEA Documentation | Here's a quick summary of resources to find your way around the OPEA Project support systems:
* **OPEA Project Website**: The https://opea.dev website is the
central source of information about what's going on with OPEA. On this site, you'll
find background and current information about the project as well as
releva... | Here's a quick summary of resources to find your way around the OPEA Project support systems:
* **OPEA Project Website**: The https://opea.dev website is the
central source of information about what's going on with OPEA. On this site, you'll
find background and current information about the project as well as
releva... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b565284a-a631-455d-a527-73254bf0a822 | OPEA Documentation | file://datasets/opea-docs/community/index.rst | unknown | 2aecc3a7-fdf7-490b-a58c-87d7371a5efc | 3 | opea-semantic-v1 | 0b6ade5f11bad30f | what's going on with OPEA. On this site, you'll find background and current information about the project as well as relevant links to project material.
* **Source Code in GitHub**: OPEA Project source code is maintained on a
public GitHub repository at https://github.com/opea-project. You'll find information about ge... | ai_ref_knowledge | OPEA Documentation | what's going on with OPEA. On this site, you'll find background and current information about the project as well as relevant links to project material.
* **Source Code in GitHub**: OPEA Project source code is maintained on a
public GitHub repository at https://github.com/opea-project. You'll find information about ge... | what's going on with OPEA. On this site, you'll find background and current information about the project as well as relevant links to project material.
* **Source Code in GitHub**: OPEA Project source code is maintained on a
public GitHub repository at https://github.com/opea-project. You'll find information about ge... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ece6e37e-9903-4b44-b4d8-87b8005acba5 | OPEA Documentation | file://datasets/opea-docs/community/index.rst | unknown | 2aecc3a7-fdf7-490b-a58c-87d7371a5efc | 5 | opea-semantic-v1 | 221a7aa5ed23aab0 | * **Documentation**: Project technical documentation is developed along with the project's code, and can be found at https://opea-project.github.io.
* **Issue Reporting and Tracking**: Requirements and Issue tracking is done in
the Github issues system within each of the major repositories such as: https://github.com/... | ai_ref_knowledge | OPEA Documentation | * **Documentation**: Project technical documentation is developed along with the project's code, and can be found at https://opea-project.github.io.
* **Issue Reporting and Tracking**: Requirements and Issue tracking is done in
the Github issues system within each of the major repositories such as: https://github.com/... | * **Documentation**: Project technical documentation is developed along with the project's code, and can be found at https://opea-project.github.io.
* **Issue Reporting and Tracking**: Requirements and Issue tracking is done in
the Github issues system within each of the major repositories such as: https://github.com/... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
48539255-ba0f-45dc-a712-e5bd5c8d0dad | OPEA Documentation | file://datasets/opea-docs/deploy/index.rst | unknown | eb3d9760-1fc4-449a-a13d-2d9cbea59302 | 1 | opea-semantic-v1 | 846570fdf8b32ab7 | and cloud native suite for OPEA, including artifacts to deploy :ref:`GenAIExamples` in a cloud native way so enterprise users can deploy to their own cloud.
We're building this documentation from content in the | ai_ref_knowledge | OPEA Documentation | and cloud native suite for OPEA, including artifacts to deploy :ref:`GenAIExamples` in a cloud native way so enterprise users can deploy to their own cloud.
We're building this documentation from content in the | and cloud native suite for OPEA, including artifacts to deploy :ref:`GenAIExamples` in a cloud native way so enterprise users can deploy to their own cloud.
We're building this documentation from content in the | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f0b63652-fcf0-4286-97f3-1ad87ee28694 | OPEA Documentation | file://datasets/opea-docs/deploy/index.rst | unknown | eb3d9760-1fc4-449a-a13d-2d9cbea59302 | 0 | opea-semantic-v1 | 16448a729fab4f8b | Deploying GenAI ###############
GenAIInfra is the containerization and cloud native suite for OPEA, including
artifacts to deploy :ref:`GenAIExamples` in a cloud native way so enterprise users
can deploy to their own cloud. | ai_ref_knowledge | OPEA Documentation | Deploying GenAI ###############
GenAIInfra is the containerization and cloud native suite for OPEA, including
artifacts to deploy :ref:`GenAIExamples` in a cloud native way so enterprise users
can deploy to their own cloud. | Deploying GenAI ###############
GenAIInfra is the containerization and cloud native suite for OPEA, including
artifacts to deploy :ref:`GenAIExamples` in a cloud native way so enterprise users
can deploy to their own cloud. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0059330a-0695-470b-a5b1-17ceb918d5e2 | OPEA Documentation | file://datasets/opea-docs/developer-guides/OPEA_API.rst | unknown | 314c66a7-1201-4174-8031-55d7de224e64 | 25 | opea-semantic-v1 | e8ed7d5abf694fea | "choices": [{ "index": 0, "object": "embedding", "message": { "role": "assistant", "content": "\n\nHello there, how may I assist you today?", }, "logprobs": null, "finish_reason": "stop", }],
"usage": {
"prompt_tokens": 9,
"completion_tokens": 12,
"total_tokens": 21
},
} | ai_ref_knowledge | OPEA Documentation | "choices": [{ "index": 0, "object": "embedding", "message": { "role": "assistant", "content": "\n\nHello there, how may I assist you today?", }, "logprobs": null, "finish_reason": "stop", }],
"usage": {
"prompt_tokens": 9,
"completion_tokens": 12,
"total_tokens": 21
},
} | "choices": [{ "index": 0, "object": "embedding", "message": { "role": "assistant", "content": "\n\nHello there, how may I assist you today?", }, "logprobs": null, "finish_reason": "stop", }],
"usage": {
"prompt_tokens": 9,
"completion_tokens": 12,
"total_tokens": 21
},
} | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
12dd6689-a7fa-47f4-ad18-b6d0c08545d6 | OPEA Documentation | file://datasets/opea-docs/developer-guides/OPEA_API.rst | unknown | 314c66a7-1201-4174-8031-55d7de224e64 | 5 | opea-semantic-v1 | 76222941b0483d65 | Note some keywords such as ``/v1/audio/speech``, ``/v1/audio/transcriptions``, ``/v1/embeddings``, ``/v1/chat/completions`` are reserved for openAI compatible Mega Service.
``service_description (string)``
The detail usage description user used to access the specified
endpoints or urls OPEA mega service is serving, i... | ai_ref_knowledge | OPEA Documentation | Note some keywords such as ``/v1/audio/speech``, ``/v1/audio/transcriptions``, ``/v1/embeddings``, ``/v1/chat/completions`` are reserved for openAI compatible Mega Service.
``service_description (string)``
The detail usage description user used to access the specified
endpoints or urls OPEA mega service is serving, i... | Note some keywords such as ``/v1/audio/speech``, ``/v1/audio/transcriptions``, ``/v1/embeddings``, ``/v1/chat/completions`` are reserved for openAI compatible Mega Service.
``service_description (string)``
The detail usage description user used to access the specified
endpoints or urls OPEA mega service is serving, i... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
132ed547-d24e-4299-b36e-c502f03970df | OPEA Documentation | file://datasets/opea-docs/developer-guides/OPEA_API.rst | unknown | 314c66a7-1201-4174-8031-55d7de224e64 | 62 | opea-semantic-v1 | 742ad8bbefcc0a73 | RAG Reranking Micro Service
The micro service is used to provide RAG reranking service. It’s usually after
RAG retrieval and before LLM generation micro service. | ai_ref_knowledge | OPEA Documentation | RAG Reranking Micro Service
The micro service is used to provide RAG reranking service. It’s usually after
RAG retrieval and before LLM generation micro service. | RAG Reranking Micro Service
The micro service is used to provide RAG reranking service. It’s usually after
RAG retrieval and before LLM generation micro service. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1b4664e0-5688-4e82-b758-48fe3e3f33b0 | OPEA Documentation | file://datasets/opea-docs/developer-guides/OPEA_API.rst | unknown | 314c66a7-1201-4174-8031-55d7de224e64 | 10 | opea-semantic-v1 | ed0158d309810709 | ``data_type (string)`` The supported data type: ``"string"`` or ``"integer"``.
For example: ``{"/v1/llm_generate": {"max_tokens": "integer"}}``
* - **405**
- ``{"error": "Retrieve configurable parameter wrongly."}`` | ai_ref_knowledge | OPEA Documentation | ``data_type (string)`` The supported data type: ``"string"`` or ``"integer"``.
For example: ``{"/v1/llm_generate": {"max_tokens": "integer"}}``
* - **405**
- ``{"error": "Retrieve configurable parameter wrongly."}`` | ``data_type (string)`` The supported data type: ``"string"`` or ``"integer"``.
For example: ``{"/v1/llm_generate": {"max_tokens": "integer"}}``
* - **405**
- ``{"error": "Retrieve configurable parameter wrongly."}`` | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1bcd91cc-2e56-45af-b002-560e4d9e8459 | OPEA Documentation | file://datasets/opea-docs/developer-guides/OPEA_API.rst | unknown | 314c66a7-1201-4174-8031-55d7de224e64 | 57 | opea-semantic-v1 | cbbb652bfbde4c88 | is used to provide RAG retrieval service. It’s usually after embedding micro sevice and before RAG reranking micro service to build a RAG Mega service.
Request | ai_ref_knowledge | OPEA Documentation | is used to provide RAG retrieval service. It’s usually after embedding micro sevice and before RAG reranking micro service to build a RAG Mega service.
Request | is used to provide RAG retrieval service. It’s usually after embedding micro sevice and before RAG reranking micro service to build a RAG Mega service.
Request | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1c25483b-1013-41e9-8b81-e6b12396d022 | OPEA Documentation | file://datasets/opea-docs/developer-guides/OPEA_API.rst | unknown | 314c66a7-1201-4174-8031-55d7de224e64 | 2 | opea-semantic-v1 | dd9ef777af27cea5 | OPEA Mega Service API *********************
OPEA Mega Service is the main entry user can access for a prebuilt GenAI
application. Such GenAI application consists of single or several OPEA
Micro Services chained as a DAG (Directed Acyclic Graph) and built as an
execution workflow for developer to create complex applicat... | ai_ref_knowledge | OPEA Documentation | OPEA Mega Service API *********************
OPEA Mega Service is the main entry user can access for a prebuilt GenAI
application. Such GenAI application consists of single or several OPEA
Micro Services chained as a DAG (Directed Acyclic Graph) and built as an
execution workflow for developer to create complex applicat... | OPEA Mega Service API *********************
OPEA Mega Service is the main entry user can access for a prebuilt GenAI
application. Such GenAI application consists of single or several OPEA
Micro Services chained as a DAG (Directed Acyclic Graph) and built as an
execution workflow for developer to create complex applicat... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1ecb27e2-8861-4968-b582-137298601aae | OPEA Documentation | file://datasets/opea-docs/developer-guides/OPEA_API.rst | unknown | 314c66a7-1201-4174-8031-55d7de224e64 | 14 | opea-semantic-v1 | e51ae47d81855ab4 | * - Status - Response * - **200** - .. code-block::
{
"object": "list",
"data": [{
"object": "embedding",
"embedding": [
0.0023064255,
... ],
"index": 0
}],
"model": "text-embedding-ada-002",
"usage": {
"prompt_tokens": 8,
"total_tokens": 8
},
} | ai_ref_knowledge | OPEA Documentation | * - Status - Response * - **200** - .. code-block::
{
"object": "list",
"data": [{
"object": "embedding",
"embedding": [
0.0023064255,
... ],
"index": 0
}],
"model": "text-embedding-ada-002",
"usage": {
"prompt_tokens": 8,
"total_tokens": 8
},
} | * - Status - Response * - **200** - .. code-block::
{
"object": "list",
"data": [{
"object": "embedding",
"embedding": [
0.0023064255,
... ],
"index": 0
}],
"model": "text-embedding-ada-002",
"usage": {
"prompt_tokens": 8,
"total_tokens": 8
},
} | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
23c16627-ba94-4cba-987a-d35e59a6c4a3 | OPEA Documentation | file://datasets/opea-docs/developer-guides/OPEA_API.rst | unknown | 314c66a7-1201-4174-8031-55d7de224e64 | 51 | opea-semantic-v1 | e72c438dc747456f | "choices": [{ "index": 0, "object": "embedding", "message": { "role": "assistant", "content": "\n\nHello there, how may I assist you today?", }, "logprobs": null, "finish_reason": "stop", }],
"usage": {
"prompt_tokens": 9,
"completion_tokens": 12,
"total_tokens": 21
},
}
* - **405**
- ``{"error": "The request of... | ai_ref_knowledge | OPEA Documentation | "choices": [{ "index": 0, "object": "embedding", "message": { "role": "assistant", "content": "\n\nHello there, how may I assist you today?", }, "logprobs": null, "finish_reason": "stop", }],
"usage": {
"prompt_tokens": 9,
"completion_tokens": 12,
"total_tokens": 21
},
}
* - **405**
- ``{"error": "The request of... | "choices": [{ "index": 0, "object": "embedding", "message": { "role": "assistant", "content": "\n\nHello there, how may I assist you today?", }, "logprobs": null, "finish_reason": "stop", }],
"usage": {
"prompt_tokens": 9,
"completion_tokens": 12,
"total_tokens": 21
},
}
* - **405**
- ``{"error": "The request of... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
25798d68-3eae-469b-8913-a92a0a22a9bb | OPEA Documentation | file://datasets/opea-docs/developer-guides/OPEA_API.rst | unknown | 314c66a7-1201-4174-8031-55d7de224e64 | 28 | opea-semantic-v1 | 72f5c89932576500 | ``created (integer)`` The Unix timestamp (in seconds) of when the chat completion was created.
``model (string)``
The model used for the chat completion. | ai_ref_knowledge | OPEA Documentation | ``created (integer)`` The Unix timestamp (in seconds) of when the chat completion was created.
``model (string)``
The model used for the chat completion. | ``created (integer)`` The Unix timestamp (in seconds) of when the chat completion was created.
``model (string)``
The model used for the chat completion. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
27337f1a-5533-4751-b3c1-7dde708d5e30 | OPEA Documentation | file://datasets/opea-docs/developer-guides/OPEA_API.rst | unknown | 314c66a7-1201-4174-8031-55d7de224e64 | 18 | opea-semantic-v1 | 5039aba020a7aa17 | ``data_type (string)`` The supported data type, ``"string"`` or ``"integer"``.
For example: ``{"llm": {"max_tokens": "integer"}}``
* - **405**
- ``{"error": "Retrieve configurable parameter wrongly."}`` | ai_ref_knowledge | OPEA Documentation | ``data_type (string)`` The supported data type, ``"string"`` or ``"integer"``.
For example: ``{"llm": {"max_tokens": "integer"}}``
* - **405**
- ``{"error": "Retrieve configurable parameter wrongly."}`` | ``data_type (string)`` The supported data type, ``"string"`` or ``"integer"``.
For example: ``{"llm": {"max_tokens": "integer"}}``
* - **405**
- ``{"error": "Retrieve configurable parameter wrongly."}`` | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
3240070e-a70a-41c1-a69c-d43cee06f803 | OPEA Documentation | file://datasets/opea-docs/developer-guides/OPEA_API.rst | unknown | 314c66a7-1201-4174-8031-55d7de224e64 | 47 | opea-semantic-v1 | be1119495a962d74 | this when you set ``"stream": "true"``. * - **POST** - ``temperature`` - ``float`` - optional - What sampling temperature to use, between 0 and 2.
Higher values like 0.8
will make the output more random, while lower values like 0.2 will make
it more focused and deterministic. We generally recommend altering this or
... | ai_ref_knowledge | OPEA Documentation | this when you set ``"stream": "true"``. * - **POST** - ``temperature`` - ``float`` - optional - What sampling temperature to use, between 0 and 2.
Higher values like 0.8
will make the output more random, while lower values like 0.2 will make
it more focused and deterministic. We generally recommend altering this or
... | this when you set ``"stream": "true"``. * - **POST** - ``temperature`` - ``float`` - optional - What sampling temperature to use, between 0 and 2.
Higher values like 0.8
will make the output more random, while lower values like 0.2 will make
it more focused and deterministic. We generally recommend altering this or
... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
33b9a0f1-13a6-4681-be5f-88b8a790d01c | OPEA Documentation | file://datasets/opea-docs/developer-guides/OPEA_API.rst | unknown | 314c66a7-1201-4174-8031-55d7de224e64 | 64 | opea-semantic-v1 | 37267883fe10ea3b | .. list-table::
* - Type
- Parameters
- Values
- Required
- Description
* - **POST**
- ``retrieved_docs``
- ``list of string``
- required
- The docs to be retreived. * - **POST**
- ``initial_query``
- ``string``
- required
- The string to query. * - **POST**
- ``json_encoders``
- ``list of float``
- req... | ai_ref_knowledge | OPEA Documentation | .. list-table::
* - Type
- Parameters
- Values
- Required
- Description
* - **POST**
- ``retrieved_docs``
- ``list of string``
- required
- The docs to be retreived. * - **POST**
- ``initial_query``
- ``string``
- required
- The string to query. * - **POST**
- ``json_encoders``
- ``list of float``
- req... | .. list-table::
* - Type
- Parameters
- Values
- Required
- Description
* - **POST**
- ``retrieved_docs``
- ``list of string``
- required
- The docs to be retreived. * - **POST**
- ``initial_query``
- ``string``
- required
- The string to query. * - **POST**
- ``json_encoders``
- ``list of float``
- req... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
3aeba09b-4c6a-4cd8-9064-ab62200177b7 | OPEA Documentation | file://datasets/opea-docs/developer-guides/OPEA_API.rst | unknown | 314c66a7-1201-4174-8031-55d7de224e64 | 66 | opea-semantic-v1 | 28e34d42e321044c | * - Status - Response * - **200** - .. code-block::
{
"query": string,
"doc": [{
"text": "I am the agent of chatbot. What can I do for you?",
},
... ]
}
* - **405**
- ``{"error": "The request of ASR fails."}`` | ai_ref_knowledge | OPEA Documentation | * - Status - Response * - **200** - .. code-block::
{
"query": string,
"doc": [{
"text": "I am the agent of chatbot. What can I do for you?",
},
... ]
}
* - **405**
- ``{"error": "The request of ASR fails."}`` | * - Status - Response * - **200** - .. code-block::
{
"query": string,
"doc": [{
"text": "I am the agent of chatbot. What can I do for you?",
},
... ]
}
* - **405**
- ``{"error": "The request of ASR fails."}`` | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
3e499102-f270-439b-8289-ecaf436ee43e | OPEA Documentation | file://datasets/opea-docs/developer-guides/OPEA_API.rst | unknown | 314c66a7-1201-4174-8031-55d7de224e64 | 63 | opea-semantic-v1 | a522c809c18e1f94 | The micro service is used to provide RAG reranking service. It’s usually after RAG retrieval and before LLM generation micro service.
Request | ai_ref_knowledge | OPEA Documentation | The micro service is used to provide RAG reranking service. It’s usually after RAG retrieval and before LLM generation micro service.
Request | The micro service is used to provide RAG reranking service. It’s usually after RAG retrieval and before LLM generation micro service.
Request | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
3e79d70c-e0e7-4014-abd0-ff86f03cae49 | OPEA Documentation | file://datasets/opea-docs/developer-guides/OPEA_API.rst | unknown | 314c66a7-1201-4174-8031-55d7de224e64 | 37 | opea-semantic-v1 | f8fcfc10e591b39a | * - **POST** - ``user`` - ``string`` - optional - A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.
Response | ai_ref_knowledge | OPEA Documentation | * - **POST** - ``user`` - ``string`` - optional - A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.
Response | * - **POST** - ``user`` - ``string`` - optional - A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.
Response | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
40f6a533-6cb9-4e51-b81e-702baa859251 | OPEA Documentation | file://datasets/opea-docs/developer-guides/OPEA_API.rst | unknown | 314c66a7-1201-4174-8031-55d7de224e64 | 24 | opea-semantic-v1 | fba6b10856926fd5 | { "id": "chatcmpl-123", "object": "chat.completion", "created": 1677652288, "model": "gpt-3.5-turbo-0125", "system_fingerprint": "fp_44709d6fcb",
"choices": [{
"index": 0,
"object": "embedding",
"message": {
"role": "assistant",
"content": "\n\nHello there, how may I assist you today?",
},
"logprobs": null,
"... | ai_ref_knowledge | OPEA Documentation | { "id": "chatcmpl-123", "object": "chat.completion", "created": 1677652288, "model": "gpt-3.5-turbo-0125", "system_fingerprint": "fp_44709d6fcb",
"choices": [{
"index": 0,
"object": "embedding",
"message": {
"role": "assistant",
"content": "\n\nHello there, how may I assist you today?",
},
"logprobs": null,
"... | { "id": "chatcmpl-123", "object": "chat.completion", "created": 1677652288, "model": "gpt-3.5-turbo-0125", "system_fingerprint": "fp_44709d6fcb",
"choices": [{
"index": 0,
"object": "embedding",
"message": {
"role": "assistant",
"content": "\n\nHello there, how may I assist you today?",
},
"logprobs": null,
"... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
418667f6-4a7a-42f4-9c38-e00e0bb6d977 | OPEA Documentation | file://datasets/opea-docs/developer-guides/OPEA_API.rst | unknown | 314c66a7-1201-4174-8031-55d7de224e64 | 17 | opea-semantic-v1 | 00eae5902bb87034 | ``index (integer)`` The index of the embedding in the list of embeddings.
``parameter_name (string)``
The configurable parameter name in OPEA mega service. | ai_ref_knowledge | OPEA Documentation | ``index (integer)`` The index of the embedding in the list of embeddings.
``parameter_name (string)``
The configurable parameter name in OPEA mega service. | ``index (integer)`` The index of the embedding in the list of embeddings.
``parameter_name (string)``
The configurable parameter name in OPEA mega service. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4192e5fc-ae76-46db-9a76-8c5cf0a616f3 | OPEA Documentation | file://datasets/opea-docs/developer-guides/OPEA_API.rst | unknown | 314c66a7-1201-4174-8031-55d7de224e64 | 27 | opea-semantic-v1 | 04471a0f99e70b6c | ``choices (array)`` A list of chat completion choices. Can be more than one if ``n`` is greater than 1.
``created (integer)``
The Unix timestamp (in seconds) of when the chat completion was created. | ai_ref_knowledge | OPEA Documentation | ``choices (array)`` A list of chat completion choices. Can be more than one if ``n`` is greater than 1.
``created (integer)``
The Unix timestamp (in seconds) of when the chat completion was created. | ``choices (array)`` A list of chat completion choices. Can be more than one if ``n`` is greater than 1.
``created (integer)``
The Unix timestamp (in seconds) of when the chat completion was created. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
50d7a2b6-5738-4a08-bd3a-342f38ae8563 | OPEA Documentation | file://datasets/opea-docs/developer-guides/OPEA_API.rst | unknown | 314c66a7-1201-4174-8031-55d7de224e64 | 36 | opea-semantic-v1 | 8dc8ea482817feca | to use. * - **POST** - ``encoding_format`` - ``string`` - optional - The format to return the embeddings in. Can be either ``"float"`` or ``"base64"``.
Devault to ``"float"``. * - **POST**
- ``dimensions``
- ``integer``
- optional
- The number of dimensions the resulting output embeddings should have. * - **POST**
... | ai_ref_knowledge | OPEA Documentation | to use. * - **POST** - ``encoding_format`` - ``string`` - optional - The format to return the embeddings in. Can be either ``"float"`` or ``"base64"``.
Devault to ``"float"``. * - **POST**
- ``dimensions``
- ``integer``
- optional
- The number of dimensions the resulting output embeddings should have. * - **POST**
... | to use. * - **POST** - ``encoding_format`` - ``string`` - optional - The format to return the embeddings in. Can be either ``"float"`` or ``"base64"``.
Devault to ``"float"``. * - **POST**
- ``dimensions``
- ``integer``
- optional
- The number of dimensions the resulting output embeddings should have. * - **POST**
... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
516dfc97-b9d2-4782-b0a4-3e2170c2119b | OPEA Documentation | file://datasets/opea-docs/developer-guides/OPEA_API.rst | unknown | 314c66a7-1201-4174-8031-55d7de224e64 | 13 | opea-semantic-v1 | e69b5ecd7fd26488 | **POST** - ``dimensions`` - ``integer`` - optional - The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models.
Response | ai_ref_knowledge | OPEA Documentation | **POST** - ``dimensions`` - ``integer`` - optional - The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models.
Response | **POST** - ``dimensions`` - ``integer`` - optional - The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models.
Response | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
581a57c7-3615-40cb-a5aa-dc685950a564 | OPEA Documentation | file://datasets/opea-docs/developer-guides/OPEA_API.rst | unknown | 314c66a7-1201-4174-8031-55d7de224e64 | 42 | opea-semantic-v1 | 4e34dbb4e430173e | Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.
* - **POST**
- ``logit_bias``
- ``map``
- optional
- Modify the likelihood of specified tokens appearing in the
completion.Accepts a JSON object that maps to... | ai_ref_knowledge | OPEA Documentation | Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.
* - **POST**
- ``logit_bias``
- ``map``
- optional
- Modify the likelihood of specified tokens appearing in the
completion.Accepts a JSON object that maps to... | Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.
* - **POST**
- ``logit_bias``
- ``map``
- optional
- Modify the likelihood of specified tokens appearing in the
completion.Accepts a JSON object that maps to... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5dda9e39-e341-498c-ab7c-366f25229732 | OPEA Documentation | file://datasets/opea-docs/developer-guides/OPEA_API.rst | unknown | 314c66a7-1201-4174-8031-55d7de224e64 | 34 | opea-semantic-v1 | fa2f2cd19de0ec5e | The API in OPEA micro service is used by developers to construct OPEA Mega Service like a DAG chain and is invisible for end user.
Embedding Micro Service | ai_ref_knowledge | OPEA Documentation | The API in OPEA micro service is used by developers to construct OPEA Mega Service like a DAG chain and is invisible for end user.
Embedding Micro Service | The API in OPEA micro service is used by developers to construct OPEA Mega Service like a DAG chain and is invisible for end user.
Embedding Micro Service | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5e9c938c-73ff-4a55-85a0-210b24d83533 | OPEA Documentation | file://datasets/opea-docs/developer-guides/OPEA_API.rst | unknown | 314c66a7-1201-4174-8031-55d7de224e64 | 21 | opea-semantic-v1 | 48abec06ba18cbcf | 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token.
* - **POST**
- ``logprobs``
- ``bool``
- optional
-
* - **POST**
- ``top_logprobs``
- ``integer``
- optional
-
* - **POST**
- ``max_tokens``
- ``integer``
- op... | ai_ref_knowledge | OPEA Documentation | 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token.
* - **POST**
- ``logprobs``
- ``bool``
- optional
-
* - **POST**
- ``top_logprobs``
- ``integer``
- optional
-
* - **POST**
- ``max_tokens``
- ``integer``
- op... | 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token.
* - **POST**
- ``logprobs``
- ``bool``
- optional
-
* - **POST**
- ``top_logprobs``
- ``integer``
- optional
-
* - **POST**
- ``max_tokens``
- ``integer``
- op... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6075ca19-d3e0-4644-b84b-53feb72c1485 | OPEA Documentation | file://datasets/opea-docs/developer-guides/OPEA_API.rst | unknown | 314c66a7-1201-4174-8031-55d7de224e64 | 3 | opea-semantic-v1 | 2d32898bf42fcc0f | single or several OPEA Micro Services chained as a DAG (Directed Acyclic Graph) and built as an execution workflow for developer to create complex applications.
.. _list_services: | ai_ref_knowledge | OPEA Documentation | single or several OPEA Micro Services chained as a DAG (Directed Acyclic Graph) and built as an execution workflow for developer to create complex applications.
.. _list_services: | single or several OPEA Micro Services chained as a DAG (Directed Acyclic Graph) and built as an execution workflow for developer to create complex applications.
.. _list_services: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6127794b-0526-45a6-a7ff-7611063bcd39 | OPEA Documentation | file://datasets/opea-docs/developer-guides/OPEA_API.rst | unknown | 314c66a7-1201-4174-8031-55d7de224e64 | 30 | opea-semantic-v1 | 884cc24e070f71f9 | model runs with. Can be used in conjunction with the seed request parameter to understand when backend changes have been made that might impact determinism.
``object (string)``
The object type, which is always ``"chat.completion"``. | ai_ref_knowledge | OPEA Documentation | model runs with. Can be used in conjunction with the seed request parameter to understand when backend changes have been made that might impact determinism.
``object (string)``
The object type, which is always ``"chat.completion"``. | model runs with. Can be used in conjunction with the seed request parameter to understand when backend changes have been made that might impact determinism.
``object (string)``
The object type, which is always ``"chat.completion"``. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6c742dff-8dae-44de-9c39-ca8bcefc3616 | OPEA Documentation | file://datasets/opea-docs/developer-guides/OPEA_API.rst | unknown | 314c66a7-1201-4174-8031-55d7de224e64 | 31 | opea-semantic-v1 | 51e900be610998df | Other Operations
Check the usage description returned in :ref:`list_services` to know what other
operations are supported by this OPEA Mega Service. | ai_ref_knowledge | OPEA Documentation | Other Operations
Check the usage description returned in :ref:`list_services` to know what other
operations are supported by this OPEA Mega Service. | Other Operations
Check the usage description returned in :ref:`list_services` to know what other
operations are supported by this OPEA Mega Service. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
73027850-d622-443e-8d3a-7525e21a49a8 | OPEA Documentation | file://datasets/opea-docs/developer-guides/OPEA_API.rst | unknown | 314c66a7-1201-4174-8031-55d7de224e64 | 1 | opea-semantic-v1 | 0024e28e24f1ddc8 | Service for users to access, as long as the input and output definition of all OPEA Micro Services for developer to build OPEA Mega service.
incomplete and contain errors. | ai_ref_knowledge | OPEA Documentation | Service for users to access, as long as the input and output definition of all OPEA Micro Services for developer to build OPEA Mega service.
incomplete and contain errors. | Service for users to access, as long as the input and output definition of all OPEA Micro Services for developer to build OPEA Mega service.
incomplete and contain errors. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7d91a0b6-1ea6-4677-a40f-21a7eac355e6 | OPEA Documentation | file://datasets/opea-docs/developer-guides/OPEA_API.rst | unknown | 314c66a7-1201-4174-8031-55d7de224e64 | 53 | opea-semantic-v1 | 519428ee93a6eae8 | .. list-table::
* - Type
- Parameters
- Values
- Required
- Description
* - **POST**
- ``url``
- ``docarray.AudioUrl``
- optional
- The link to the audio. * - **POST**
- ``model_name_or_path``
- ``string``
- optional
- The model used to do audio-to-text translation. * - **POST**
- ``Language``
- ``string... | ai_ref_knowledge | OPEA Documentation | .. list-table::
* - Type
- Parameters
- Values
- Required
- Description
* - **POST**
- ``url``
- ``docarray.AudioUrl``
- optional
- The link to the audio. * - **POST**
- ``model_name_or_path``
- ``string``
- optional
- The model used to do audio-to-text translation. * - **POST**
- ``Language``
- ``string... | .. list-table::
* - Type
- Parameters
- Values
- Required
- Description
* - **POST**
- ``url``
- ``docarray.AudioUrl``
- optional
- The link to the audio. * - **POST**
- ``model_name_or_path``
- ``string``
- optional
- The model used to do audio-to-text translation. * - **POST**
- ``Language``
- ``string... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8172d294-cb2a-42f0-86ae-567f1b423840 | OPEA Documentation | file://datasets/opea-docs/developer-guides/OPEA_API.rst | unknown | 314c66a7-1201-4174-8031-55d7de224e64 | 58 | opea-semantic-v1 | cc7ca6adb3df60bb | .. list-table::
* - Type
- Parameters
- Values
- Required
- Description
* - **POST**
- ``text``
- ``string``
- required
- The input string to query. * - **POST**
- ``embedding``
- ``list of float``
- required
- The list of float for text as vector representation. | ai_ref_knowledge | OPEA Documentation | .. list-table::
* - Type
- Parameters
- Values
- Required
- Description
* - **POST**
- ``text``
- ``string``
- required
- The input string to query. * - **POST**
- ``embedding``
- ``list of float``
- required
- The list of float for text as vector representation. | .. list-table::
* - Type
- Parameters
- Values
- Required
- Description
* - **POST**
- ``text``
- ``string``
- required
- The input string to query. * - **POST**
- ``embedding``
- ``list of float``
- required
- The list of float for text as vector representation. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
81d96a80-e473-4281-b75f-2ab85b54ef95 | OPEA Documentation | file://datasets/opea-docs/developer-guides/OPEA_API.rst | unknown | 314c66a7-1201-4174-8031-55d7de224e64 | 4 | opea-semantic-v1 | 68888aa6c56ab5bf | ``service_name (string)`` The endpoints or URLs OPEA mega service is serving. For example, ``/v1/RAG``.
Note some keywords such as ``/v1/audio/speech``,
``/v1/audio/transcriptions``, ``/v1/embeddings``,
``/v1/chat/completions`` are reserved for openAI compatible Mega
Service. | ai_ref_knowledge | OPEA Documentation | ``service_name (string)`` The endpoints or URLs OPEA mega service is serving. For example, ``/v1/RAG``.
Note some keywords such as ``/v1/audio/speech``,
``/v1/audio/transcriptions``, ``/v1/embeddings``,
``/v1/chat/completions`` are reserved for openAI compatible Mega
Service. | ``service_name (string)`` The endpoints or URLs OPEA mega service is serving. For example, ``/v1/RAG``.
Note some keywords such as ``/v1/audio/speech``,
``/v1/audio/transcriptions``, ``/v1/embeddings``,
``/v1/chat/completions`` are reserved for openAI compatible Mega
Service. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
81e7a6b5-67b7-4b66-b8c9-d19f12fa8792 | OPEA Documentation | file://datasets/opea-docs/developer-guides/OPEA_API.rst | unknown | 314c66a7-1201-4174-8031-55d7de224e64 | 11 | opea-semantic-v1 | e902f44da99f90ec | .. list-table::
* - Type
- Parameters
- Values
- Required
- Description
* - **POST**
- ``input``
- ``string``
- required
- Input text to embed, encoded as a string or array of tokens. To embed
multiple inputs in a single request, pass an array of strings or array of
token arrays. The input must not exceed th... | ai_ref_knowledge | OPEA Documentation | .. list-table::
* - Type
- Parameters
- Values
- Required
- Description
* - **POST**
- ``input``
- ``string``
- required
- Input text to embed, encoded as a string or array of tokens. To embed
multiple inputs in a single request, pass an array of strings or array of
token arrays. The input must not exceed th... | .. list-table::
* - Type
- Parameters
- Values
- Required
- Description
* - **POST**
- ``input``
- ``string``
- required
- Input text to embed, encoded as a string or array of tokens. To embed
multiple inputs in a single request, pass an array of strings or array of
token arrays. The input must not exceed th... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
86038db7-ce33-4c71-a24a-b8b875065267 | OPEA Documentation | file://datasets/opea-docs/developer-guides/OPEA_API.rst | unknown | 314c66a7-1201-4174-8031-55d7de224e64 | 41 | opea-semantic-v1 | 97cd6ac3870f3e09 | .. list-table::
* - Type
- Parameters
- Values
- Required
- Description
* - **POST**
- ``message``
- ``array``
- required
- A list of messages comprising the conversation so far. Example Python code. * - **POST**
- ``model``
- ``string``
- required
- The ID of the model to use. See the model endpoint compa... | ai_ref_knowledge | OPEA Documentation | .. list-table::
* - Type
- Parameters
- Values
- Required
- Description
* - **POST**
- ``message``
- ``array``
- required
- A list of messages comprising the conversation so far. Example Python code. * - **POST**
- ``model``
- ``string``
- required
- The ID of the model to use. See the model endpoint compa... | .. list-table::
* - Type
- Parameters
- Values
- Required
- Description
* - **POST**
- ``message``
- ``array``
- required
- A list of messages comprising the conversation so far. Example Python code. * - **POST**
- ``model``
- ``string``
- required
- The ID of the model to use. See the model endpoint compa... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
86c88cb9-dc09-487c-9c2b-e1b365693bc0 | OPEA Documentation | file://datasets/opea-docs/developer-guides/OPEA_API.rst | unknown | 314c66a7-1201-4174-8031-55d7de224e64 | 22 | opea-semantic-v1 | b1c14867b8d6ef7c | - ``array`` - optional - * - **POST** - ``tool_choice`` - ``string`` - optional - * - **POST** - ``user`` - ``string`` - optional -
Response | ai_ref_knowledge | OPEA Documentation | - ``array`` - optional - * - **POST** - ``tool_choice`` - ``string`` - optional - * - **POST** - ``user`` - ``string`` - optional -
Response | - ``array`` - optional - * - **POST** - ``tool_choice`` - ``string`` - optional - * - **POST** - ``user`` - ``string`` - optional -
Response | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
88b9e002-3aa0-4947-b08f-fbe778a091a6 | OPEA Documentation | file://datasets/opea-docs/developer-guides/OPEA_API.rst | unknown | 314c66a7-1201-4174-8031-55d7de224e64 | 43 | opea-semantic-v1 | b85b1649115f1656 | of the relevant token. * - **POST** - ``logprobs`` - ``bool`` - optional - Whether to return log probabilities of the output tokens or not.
If true,
returns the log probabilities of each output token returned in the
content of message. * - **POST**
- ``top_logprobs``
- ``integer``
- optional
- An integer between ... | ai_ref_knowledge | OPEA Documentation | of the relevant token. * - **POST** - ``logprobs`` - ``bool`` - optional - Whether to return log probabilities of the output tokens or not.
If true,
returns the log probabilities of each output token returned in the
content of message. * - **POST**
- ``top_logprobs``
- ``integer``
- optional
- An integer between ... | of the relevant token. * - **POST** - ``logprobs`` - ``bool`` - optional - Whether to return log probabilities of the output tokens or not.
If true,
returns the log probabilities of each output token returned in the
content of message. * - **POST**
- ``top_logprobs``
- ``integer``
- optional
- An integer between ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8a8cb43d-7b14-4330-ac14-70e0dd799a81 | OPEA Documentation | file://datasets/opea-docs/developer-guides/OPEA_API.rst | unknown | 314c66a7-1201-4174-8031-55d7de224e64 | 45 | opea-semantic-v1 | 7aab79daeb40c27d | our system will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result.
Determinism is not
guaranteed, and you should refer to the ``system_fingerprint`` response
parameter to monitor changes in the backend. * - **POST**
- ``service... | ai_ref_knowledge | OPEA Documentation | our system will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result.
Determinism is not
guaranteed, and you should refer to the ``system_fingerprint`` response
parameter to monitor changes in the backend. * - **POST**
- ``service... | our system will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result.
Determinism is not
guaranteed, and you should refer to the ``system_fingerprint`` response
parameter to monitor changes in the backend. * - **POST**
- ``service... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation |
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