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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
21eeaa60-081c-4a76-8b71-403350b49d79 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 98 | opea-semantic-v1 | c03f8c3c7f94dfa9 | Test the service:
curl http://localhost:9009/generate \
-X POST \
-d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \
-H 'Content-Type: application/json' | ai_ref_knowledge | OPEA Documentation | Test the service:
curl http://localhost:9009/generate \
-X POST \
-d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \
-H 'Content-Type: application/json' | Test the service:
curl http://localhost:9009/generate \
-X POST \
-d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \
-H 'Content-Type: application/json' | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
22db2a81-02ba-418c-a315-3ec9a2325577 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 11 | opea-semantic-v1 | fde491a514fbcbf5 | next step is to clone the GenAIInfra which is the containerization and cloud-native suite for OPEA, including artifacts to deploy ChatQnA in a cloud-native way.
```bash
git clone https://github.com/opea-project/GenAIInfra.git | ai_ref_knowledge | OPEA Documentation | next step is to clone the GenAIInfra which is the containerization and cloud-native suite for OPEA, including artifacts to deploy ChatQnA in a cloud-native way.
```bash
git clone https://github.com/opea-project/GenAIInfra.git | next step is to clone the GenAIInfra which is the containerization and cloud-native suite for OPEA, including artifacts to deploy ChatQnA in a cloud-native way.
```bash
git clone https://github.com/opea-project/GenAIInfra.git | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
2551be3b-06f6-4aba-a6c1-1a5b2e70cfb2 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 64 | opea-semantic-v1 | 860854e1005a4bb4 | ### Accessing ChatQnA application after custom data upload
Use the following command to forward traffic from your local machine to the service running in the Kubernetes cluster:
```bash
kubectl port-forward svc/chatqna 8888:8888 & | ai_ref_knowledge | OPEA Documentation | ### Accessing ChatQnA application after custom data upload
Use the following command to forward traffic from your local machine to the service running in the Kubernetes cluster:
```bash
kubectl port-forward svc/chatqna 8888:8888 & | ### Accessing ChatQnA application after custom data upload
Use the following command to forward traffic from your local machine to the service running in the Kubernetes cluster:
```bash
kubectl port-forward svc/chatqna 8888:8888 & | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
2601dab2-c811-4f06-80f5-b811181c1b13 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 18 | opea-semantic-v1 | dcd0d891d48d10d7 | | |Reranking | TEI | BAAI/bge-reranker-base | OPEA Microservice | |LLM | TGI |Intel/neural-chat-7b-v3-3 |OPEA Microservice | |UI | | NA | Gateway Service |
Tools and models mentioned in the table are configurable either through the
environment variable or `values.yaml` | ai_ref_knowledge | OPEA Documentation | | |Reranking | TEI | BAAI/bge-reranker-base | OPEA Microservice | |LLM | TGI |Intel/neural-chat-7b-v3-3 |OPEA Microservice | |UI | | NA | Gateway Service |
Tools and models mentioned in the table are configurable either through the
environment variable or `values.yaml` | | |Reranking | TEI | BAAI/bge-reranker-base | OPEA Microservice | |LLM | TGI |Intel/neural-chat-7b-v3-3 |OPEA Microservice | |UI | | NA | Gateway Service |
Tools and models mentioned in the table are configurable either through the
environment variable or `values.yaml` | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
263365ce-9b75-4eaf-842c-ff0bbf11896a | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 22 | opea-semantic-v1 | 0c0491da2af86c12 | charts in the specified directory and ensure the `chatqna` Helm chart is ready for deployment by updating its dependencies as defined in the `Chart.yaml` file.
```bash
# All Helm charts in the specified directory have their
# dependencies up-to-date, facilitating consistent deployments. scripts/update_dependency.sh | ai_ref_knowledge | OPEA Documentation | charts in the specified directory and ensure the `chatqna` Helm chart is ready for deployment by updating its dependencies as defined in the `Chart.yaml` file.
```bash
# All Helm charts in the specified directory have their
# dependencies up-to-date, facilitating consistent deployments. scripts/update_dependency.sh | charts in the specified directory and ensure the `chatqna` Helm chart is ready for deployment by updating its dependencies as defined in the `Chart.yaml` file.
```bash
# All Helm charts in the specified directory have their
# dependencies up-to-date, facilitating consistent deployments. scripts/update_dependency.sh | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
27147798-5128-47f6-af59-460cd29a2f4d | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 4 | opea-semantic-v1 | 79b5d6740396b2bd | this ChatQnA tutorial, we will walk through how to enable the below list of microservices from OPEA GenAIComps to deploy a multi-node TGI-based service solution.
1. Data Prep
2. Embedding
3. Retriever
4. Reranking
5. LLM with TGI | ai_ref_knowledge | OPEA Documentation | this ChatQnA tutorial, we will walk through how to enable the below list of microservices from OPEA GenAIComps to deploy a multi-node TGI-based service solution.
1. Data Prep
2. Embedding
3. Retriever
4. Reranking
5. LLM with TGI | this ChatQnA tutorial, we will walk through how to enable the below list of microservices from OPEA GenAIComps to deploy a multi-node TGI-based service solution.
1. Data Prep
2. Embedding
3. Retriever
4. Reranking
5. LLM with TGI | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
2807f6b9-287d-4529-9f9c-9f29e9355dbb | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 107 | opea-semantic-v1 | 9ca72566f1843b1f | curl -X POST "http://localhost:6007/v1/dataprep" \ -H "Content-Type: multipart/form-data" \ -F 'link_list=["https://opea.dev"]'
This command updates a knowledge base by submitting a list of HTTP links for processing. | ai_ref_knowledge | OPEA Documentation | curl -X POST "http://localhost:6007/v1/dataprep" \ -H "Content-Type: multipart/form-data" \ -F 'link_list=["https://opea.dev"]'
This command updates a knowledge base by submitting a list of HTTP links for processing. | curl -X POST "http://localhost:6007/v1/dataprep" \ -H "Content-Type: multipart/form-data" \ -F 'link_list=["https://opea.dev"]'
This command updates a knowledge base by submitting a list of HTTP links for processing. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
289720d6-2706-4191-99f0-47dbc901c521 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 115 | opea-semantic-v1 | d91cb4ec03c28e4a | The next step is to get the `<k8s-node-ip-address>` by running: ```bash kubectl get nodes -o wide
The command shows internal IPs for all the nodes in the cluster: | ai_ref_knowledge | OPEA Documentation | The next step is to get the `<k8s-node-ip-address>` by running: ```bash kubectl get nodes -o wide
The command shows internal IPs for all the nodes in the cluster: | The next step is to get the `<k8s-node-ip-address>` by running: ```bash kubectl get nodes -o wide
The command shows internal IPs for all the nodes in the cluster: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
29911639-a1ad-4502-b333-77d2dcf2f3d7 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 66 | opea-semantic-v1 | 56288c35c06bbcae | curl http://localhost:8888/v1/chatqna -H "Content-Type: application/json" -d '{ "model": "Intel/neural-chat-7b-v3-3", "messages": "What is OPEA?" }'
After uploading the pdf with information about OPEA, we can see that the pdf is being used as a context to answer the question correctly: | ai_ref_knowledge | OPEA Documentation | curl http://localhost:8888/v1/chatqna -H "Content-Type: application/json" -d '{ "model": "Intel/neural-chat-7b-v3-3", "messages": "What is OPEA?" }'
After uploading the pdf with information about OPEA, we can see that the pdf is being used as a context to answer the question correctly: | curl http://localhost:8888/v1/chatqna -H "Content-Type: application/json" -d '{ "model": "Intel/neural-chat-7b-v3-3", "messages": "What is OPEA?" }'
After uploading the pdf with information about OPEA, we can see that the pdf is being used as a context to answer the question correctly: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
2a90c06b-c304-48b6-acc6-785ba95ca71b | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 122 | opea-semantic-v1 | 898e39562a1a4937 | And open a browser to access `http://localhost:8080` Visit this [link](https://opea-project.github.io/latest/getting-started/README.html#interact-with-chatqna) to see how to interact with the UI.
### Stop the services
Once you are done with the entire pipeline and wish to stop and remove all the resources, use the comm... | ai_ref_knowledge | OPEA Documentation | And open a browser to access `http://localhost:8080` Visit this [link](https://opea-project.github.io/latest/getting-started/README.html#interact-with-chatqna) to see how to interact with the UI.
### Stop the services
Once you are done with the entire pipeline and wish to stop and remove all the resources, use the comm... | And open a browser to access `http://localhost:8080` Visit this [link](https://opea-project.github.io/latest/getting-started/README.html#interact-with-chatqna) to see how to interact with the UI.
### Stop the services
Once you are done with the entire pipeline and wish to stop and remove all the resources, use the comm... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
2f09f4c6-08da-4943-865e-abab6c6919ee | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 121 | opea-semantic-v1 | c02be09f728db6fe | Alternatively, You can also choose to use port forwarding as shown previously using: ```bash kubectl port-forward service/chatqna-nginx 8080:80 &
And open a browser to access `http://localhost:8080`
Visit this [link](https://opea-project.github.io/latest/getting-started/README.html#interact-with-chatqna) to see how ... | ai_ref_knowledge | OPEA Documentation | Alternatively, You can also choose to use port forwarding as shown previously using: ```bash kubectl port-forward service/chatqna-nginx 8080:80 &
And open a browser to access `http://localhost:8080`
Visit this [link](https://opea-project.github.io/latest/getting-started/README.html#interact-with-chatqna) to see how ... | Alternatively, You can also choose to use port forwarding as shown previously using: ```bash kubectl port-forward service/chatqna-nginx 8080:80 &
And open a browser to access `http://localhost:8080`
Visit this [link](https://opea-project.github.io/latest/getting-started/README.html#interact-with-chatqna) to see how ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
30d5f46f-422f-41f5-a1de-ef17a8563405 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 105 | opea-semantic-v1 | 57cfcfa1b7abbf6f | it to a known supported commit. 2024-06-05T05:45:27.852525522Z 2024-06-05T05:45:27.852437Z INFO text_generation_router: router/src/main.rs:379: Serving revision bdd31cf498d13782cc7497cba5896996ce429f91 of model Intel/neural-chat-7b-v3-3 2024-06-05T05:45:27.867833811Z 2024-06-05T05:45:27.867759Z INFO text_generation_rou... | ai_ref_knowledge | OPEA Documentation | it to a known supported commit. 2024-06-05T05:45:27.852525522Z 2024-06-05T05:45:27.852437Z INFO text_generation_router: router/src/main.rs:379: Serving revision bdd31cf498d13782cc7497cba5896996ce429f91 of model Intel/neural-chat-7b-v3-3 2024-06-05T05:45:27.867833811Z 2024-06-05T05:45:27.867759Z INFO text_generation_rou... | it to a known supported commit. 2024-06-05T05:45:27.852525522Z 2024-06-05T05:45:27.852437Z INFO text_generation_router: router/src/main.rs:379: Serving revision bdd31cf498d13782cc7497cba5896996ce429f91 of model Intel/neural-chat-7b-v3-3 2024-06-05T05:45:27.867833811Z 2024-06-05T05:45:27.867759Z INFO text_generation_rou... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
32d0ce87-302b-49cc-bff8-6a976d7033a3 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 86 | opea-semantic-v1 | af43ef6618e311cd | within the megaservice architecture.\nGateways support API definition, API versioning, rate limiting, and request transformation,\nallowing for fine-grained control over how users interact with the underlying Microservices.
By\nabstracting the complexity of the underlying infrastructure, Gateways provide a seamless and... | ai_ref_knowledge | OPEA Documentation | within the megaservice architecture.\nGateways support API definition, API versioning, rate limiting, and request transformation,\nallowing for fine-grained control over how users interact with the underlying Microservices.
By\nabstracting the complexity of the underlying infrastructure, Gateways provide a seamless and... | within the megaservice architecture.\nGateways support API definition, API versioning, rate limiting, and request transformation,\nallowing for fine-grained control over how users interact with the underlying Microservices.
By\nabstracting the complexity of the underlying infrastructure, Gateways provide a seamless and... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
3464981d-d044-4dd8-9e30-b9cc1532bde3 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 97 | opea-semantic-v1 | 38678a15d0b7f563 | Use the following command to forward traffic from your local machine to the service running in your Kubernetes cluster: ```bash kubectl port-forward svc/chatqna-tgi 9009:80 &
Test the service: | ai_ref_knowledge | OPEA Documentation | Use the following command to forward traffic from your local machine to the service running in your Kubernetes cluster: ```bash kubectl port-forward svc/chatqna-tgi 9009:80 &
Test the service: | Use the following command to forward traffic from your local machine to the service running in your Kubernetes cluster: ```bash kubectl port-forward svc/chatqna-tgi 9009:80 &
Test the service: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
349ff6d7-83a0-4f61-9ebc-388e824a95cf | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 42 | opea-semantic-v1 | 210170b730f24622 | Or check logs using: ```bash kubectl logs chatqna-tgi-778bb6598f-cv5cg
## Interacting with ChatQnA deployment
This section will walk you through what are the different ways to interact with
the microservices deployed | ai_ref_knowledge | OPEA Documentation | Or check logs using: ```bash kubectl logs chatqna-tgi-778bb6598f-cv5cg
## Interacting with ChatQnA deployment
This section will walk you through what are the different ways to interact with
the microservices deployed | Or check logs using: ```bash kubectl logs chatqna-tgi-778bb6598f-cv5cg
## Interacting with ChatQnA deployment
This section will walk you through what are the different ways to interact with
the microservices deployed | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
36a450b2-ae52-4119-90f7-cbc210137a06 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 95 | opea-semantic-v1 | fa85641ad8921c78 | curl http://localhost:8808/rerank \ -X POST \ -d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \ -H 'Content-Type: application/json'
Output is: `[{"index":1,"score":0.9988041},{"index":0,"score":0.022948774}]` | ai_ref_knowledge | OPEA Documentation | curl http://localhost:8808/rerank \ -X POST \ -d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \ -H 'Content-Type: application/json'
Output is: `[{"index":1,"score":0.9988041},{"index":0,"score":0.022948774}]` | curl http://localhost:8808/rerank \ -X POST \ -d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \ -H 'Content-Type: application/json'
Output is: `[{"index":1,"score":0.9988041},{"index":0,"score":0.022948774}]` | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
37703d65-6d1b-43e0-9ee9-b8184e32b5af | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 76 | opea-semantic-v1 | b1fc5d239490dd1d | the following command to forward traffic from your local machine to the Retriever service running in your Kubernetes cluster: ```bash kubectl port-forward svc/chatqna-retriever-usvc 7000:7000 &
Test the service: | ai_ref_knowledge | OPEA Documentation | the following command to forward traffic from your local machine to the Retriever service running in your Kubernetes cluster: ```bash kubectl port-forward svc/chatqna-retriever-usvc 7000:7000 &
Test the service: | the following command to forward traffic from your local machine to the Retriever service running in your Kubernetes cluster: ```bash kubectl port-forward svc/chatqna-retriever-usvc 7000:7000 &
Test the service: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
38366563-e88f-4ec7-a377-90c077f240ef | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 0 | opea-semantic-v1 | 85d28403f1b56e47 | # Multi-node on-prem deployment with TGI on Xeon Scalable processors on a K8s cluster using Helm
This deployment section covers multi-node on-prem deployment of the ChatQnA example with OPEA components using the TGI service. While one may customize the RAG application with a choice of vector database, the LLM model use... | ai_ref_knowledge | OPEA Documentation | # Multi-node on-prem deployment with TGI on Xeon Scalable processors on a K8s cluster using Helm
This deployment section covers multi-node on-prem deployment of the ChatQnA example with OPEA components using the TGI service. While one may customize the RAG application with a choice of vector database, the LLM model use... | # Multi-node on-prem deployment with TGI on Xeon Scalable processors on a K8s cluster using Helm
This deployment section covers multi-node on-prem deployment of the ChatQnA example with OPEA components using the TGI service. While one may customize the RAG application with a choice of vector database, the LLM model use... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
39f65b0e-dc8d-44f9-82f9-35a63be6b8a2 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 118 | opea-semantic-v1 | 733e8f020544ad9d | cluster will be listening at the specified port, which is `30304` in this example. The `<k8s-node-ip-address>` can be found under INTERNAL-IP. Here it is `190.128.49.1`.
Open a browser to access `http://<k8s-node-ip-address>:${port}`. From the configuration shown above, it would be `http://190.128.49.1:30304` | ai_ref_knowledge | OPEA Documentation | cluster will be listening at the specified port, which is `30304` in this example. The `<k8s-node-ip-address>` can be found under INTERNAL-IP. Here it is `190.128.49.1`.
Open a browser to access `http://<k8s-node-ip-address>:${port}`. From the configuration shown above, it would be `http://190.128.49.1:30304` | cluster will be listening at the specified port, which is `30304` in this example. The `<k8s-node-ip-address>` can be found under INTERNAL-IP. Here it is `190.128.49.1`.
Open a browser to access `http://<k8s-node-ip-address>:${port}`. From the configuration shown above, it would be `http://190.128.49.1:30304` | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
3a6d7b6b-be26-48ff-a5cf-434861c957fb | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 20 | opea-semantic-v1 | eb6981f48cec7304 | Set a new [namespace](k8s_getting_started.md#create-and-set-namespace) and switch to it if needed
To enable UI, uncomment the following lines in `GenAIInfra/helm-charts/chatqna/values.yaml`:
```bash
chatqna-ui:
image:
repository: "opea/chatqna-ui"
tag: "latest"
containerPort: "5173" | ai_ref_knowledge | OPEA Documentation | Set a new [namespace](k8s_getting_started.md#create-and-set-namespace) and switch to it if needed
To enable UI, uncomment the following lines in `GenAIInfra/helm-charts/chatqna/values.yaml`:
```bash
chatqna-ui:
image:
repository: "opea/chatqna-ui"
tag: "latest"
containerPort: "5173" | Set a new [namespace](k8s_getting_started.md#create-and-set-namespace) and switch to it if needed
To enable UI, uncomment the following lines in `GenAIInfra/helm-charts/chatqna/values.yaml`:
```bash
chatqna-ui:
image:
repository: "opea/chatqna-ui"
tag: "latest"
containerPort: "5173" | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
3bb2c73c-ca51-47ed-a489-2f6bf7b34f88 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 27 | opea-semantic-v1 | 85bca40207acc9fd | performance and reduced memory usage. Bfloat16 operations are accelerated using Intel® AMX, the built-in AI accelerator on 4th Gen Intel® Xeon® Scalable processors and later.
Set the necessary environment variables to set up the use case
```bash
export MODELDIR="" #export MODELDIR="/mnt/opea-models" if you want to cach... | ai_ref_knowledge | OPEA Documentation | performance and reduced memory usage. Bfloat16 operations are accelerated using Intel® AMX, the built-in AI accelerator on 4th Gen Intel® Xeon® Scalable processors and later.
Set the necessary environment variables to set up the use case
```bash
export MODELDIR="" #export MODELDIR="/mnt/opea-models" if you want to cach... | performance and reduced memory usage. Bfloat16 operations are accelerated using Intel® AMX, the built-in AI accelerator on 4th Gen Intel® Xeon® Scalable processors and later.
Set the necessary environment variables to set up the use case
```bash
export MODELDIR="" #export MODELDIR="/mnt/opea-models" if you want to cach... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
3d7a38d1-7abf-4502-8b59-c5b3ead8d1f0 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 50 | opea-semantic-v1 | 234c453b7df2f320 | Use the following command to forward traffic from your local machine to the service running in the Kubernetes cluster: ```bash kubectl port-forward svc/chatqna 8888:8888 &
Test the service: | ai_ref_knowledge | OPEA Documentation | Use the following command to forward traffic from your local machine to the service running in the Kubernetes cluster: ```bash kubectl port-forward svc/chatqna 8888:8888 &
Test the service: | Use the following command to forward traffic from your local machine to the service running in the Kubernetes cluster: ```bash kubectl port-forward svc/chatqna 8888:8888 &
Test the service: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
445105c5-c580-4000-9148-5f3c0b8abfb2 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 93 | opea-semantic-v1 | 969f9a580bd44a8c | Test the service:
The TEI Reranking Service reranks the documents returned by the retrieval
service. It consumes the query and list of documents and returns the document
indices based on the decreasing order of the similarity score. The document
corresponding to the returned index with the highest score is the most rel... | ai_ref_knowledge | OPEA Documentation | Test the service:
The TEI Reranking Service reranks the documents returned by the retrieval
service. It consumes the query and list of documents and returns the document
indices based on the decreasing order of the similarity score. The document
corresponding to the returned index with the highest score is the most rel... | Test the service:
The TEI Reranking Service reranks the documents returned by the retrieval
service. It consumes the query and list of documents and returns the document
indices based on the decreasing order of the similarity score. The document
corresponding to the returned index with the highest score is the most rel... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
474931b9-e47c-4aa3-b1af-ca9a5ffde54e | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 99 | opea-semantic-v1 | 20668bddb56db85a | curl http://localhost:9009/generate \ -X POST \ -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \ -H 'Content-Type: application/json'
TGI service generates text for the input prompt. Here is the expected result from TGI: | ai_ref_knowledge | OPEA Documentation | curl http://localhost:9009/generate \ -X POST \ -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \ -H 'Content-Type: application/json'
TGI service generates text for the input prompt. Here is the expected result from TGI: | curl http://localhost:9009/generate \ -X POST \ -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \ -H 'Content-Type: application/json'
TGI service generates text for the input prompt. Here is the expected result from TGI: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
49300f31-44fa-4c05-aa09-71ea682bd6e2 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 6 | opea-semantic-v1 | c79003b805a2ca8b | using Kubernetes provided there are adequate resources for all the associated pods, namely CPU and memory and, no constraints such as affinity, anti-affinity, or taints.
## Prerequisites | ai_ref_knowledge | OPEA Documentation | using Kubernetes provided there are adequate resources for all the associated pods, namely CPU and memory and, no constraints such as affinity, anti-affinity, or taints.
## Prerequisites | using Kubernetes provided there are adequate resources for all the associated pods, namely CPU and memory and, no constraints such as affinity, anti-affinity, or taints.
## Prerequisites | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4998a0dd-1a11-48b0-a7eb-0a51b11e8f3e | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 16 | opea-semantic-v1 | f72622f5021d2fbc | ## Use Case Setup
The `GenAIInfra` repository utilizes a structured Helm chart approach, comprising a primary `Charts.yaml` and individual sub-charts for components like the LLM Service, Embedding Service, and Reranking Service. Each sub-chart includes its own `values.yaml` file, enabling specific configurations such a... | ai_ref_knowledge | OPEA Documentation | ## Use Case Setup
The `GenAIInfra` repository utilizes a structured Helm chart approach, comprising a primary `Charts.yaml` and individual sub-charts for components like the LLM Service, Embedding Service, and Reranking Service. Each sub-chart includes its own `values.yaml` file, enabling specific configurations such a... | ## Use Case Setup
The `GenAIInfra` repository utilizes a structured Helm chart approach, comprising a primary `Charts.yaml` and individual sub-charts for components like the LLM Service, Embedding Service, and Reranking Service. Each sub-chart includes its own `values.yaml` file, enabling specific configurations such a... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4a3d0f14-625a-4704-bbb1-69a9ea17f413 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 119 | opea-semantic-v1 | 41b74945f84944d2 | Open a browser to access `http://<k8s-node-ip-address>:${port}`. From the configuration shown above, it would be `http://190.128.49.1:30304`
### Basic UI via Port Forwarding | ai_ref_knowledge | OPEA Documentation | Open a browser to access `http://<k8s-node-ip-address>:${port}`. From the configuration shown above, it would be `http://190.128.49.1:30304`
### Basic UI via Port Forwarding | Open a browser to access `http://<k8s-node-ip-address>:${port}`. From the configuration shown above, it would be `http://190.128.49.1:30304`
### Basic UI via Port Forwarding | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4b2dc525-0da9-4244-8ab0-a91354891c95 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 39 | opea-semantic-v1 | 6d8e953cc2035e0e | a specific pod, which can provide insight into what the application is doing and any errors it might be encountering use: ```bash kubectl logs <pod-name>
2. **Describing Pods**: For a detailed view of the pod's current state, its configuration, and its operational events, run:
```bash
kubectl describe pod <pod-name> | ai_ref_knowledge | OPEA Documentation | a specific pod, which can provide insight into what the application is doing and any errors it might be encountering use: ```bash kubectl logs <pod-name>
2. **Describing Pods**: For a detailed view of the pod's current state, its configuration, and its operational events, run:
```bash
kubectl describe pod <pod-name> | a specific pod, which can provide insight into what the application is doing and any errors it might be encountering use: ```bash kubectl logs <pod-name>
2. **Describing Pods**: For a detailed view of the pod's current state, its configuration, and its operational events, run:
```bash
kubectl describe pod <pod-name> | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4e193cc5-acf3-44b3-a1c8-ff972d564d99 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 56 | opea-semantic-v1 | 42adf2158fde066b | of Public Employees of Alabama. It is a labor union representing public employees in the state of Alabama, working to protect their rights and interests.
In the upcoming sections, we will see how this answer can be improved with RAG. | ai_ref_knowledge | OPEA Documentation | of Public Employees of Alabama. It is a labor union representing public employees in the state of Alabama, working to protect their rights and interests.
In the upcoming sections, we will see how this answer can be improved with RAG. | of Public Employees of Alabama. It is a labor union representing public employees in the state of Alabama, working to protect their rights and interests.
In the upcoming sections, we will see how this answer can be improved with RAG. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4e2d4f22-ce5f-47ba-8f66-ffd79423117a | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 74 | opea-semantic-v1 | b76f61aa111e8255 | curl http://localhost:6006/embed \ -X POST \ -d '{"inputs":"What is Deep Learning?"}' \ -H 'Content-Type: application/json'
In this example, the embedding model used is "BAAI/bge-base-en-v1.5", which has a vector size of 768. So the output of the `curl` command is an embedded vector of
length 768. | ai_ref_knowledge | OPEA Documentation | curl http://localhost:6006/embed \ -X POST \ -d '{"inputs":"What is Deep Learning?"}' \ -H 'Content-Type: application/json'
In this example, the embedding model used is "BAAI/bge-base-en-v1.5", which has a vector size of 768. So the output of the `curl` command is an embedded vector of
length 768. | curl http://localhost:6006/embed \ -X POST \ -d '{"inputs":"What is Deep Learning?"}' \ -H 'Content-Type: application/json'
In this example, the embedding model used is "BAAI/bge-base-en-v1.5", which has a vector size of 768. So the output of the `curl` command is an embedded vector of
length 768. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
50c39dd2-4f28-4854-a364-32917ae62353 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 91 | opea-semantic-v1 | 7c4a0b3e9b7ba158 | ### TEI Reranking Service
Use the following command to forward traffic from your local machine to the Reranking service running in the Kubernetes cluster:
```bash
kubectl port-forward svc/chatqna-teirerank 8808:80 & | ai_ref_knowledge | OPEA Documentation | ### TEI Reranking Service
Use the following command to forward traffic from your local machine to the Reranking service running in the Kubernetes cluster:
```bash
kubectl port-forward svc/chatqna-teirerank 8808:80 & | ### TEI Reranking Service
Use the following command to forward traffic from your local machine to the Reranking service running in the Kubernetes cluster:
```bash
kubectl port-forward svc/chatqna-teirerank 8808:80 & | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
510c2aa5-74ca-4ea1-bb1e-15039ebb0c24 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 46 | opea-semantic-v1 | f68861b71b392f44 | 8m16s chatqna-tei ClusterIP 10.105.204.12 <none> 80/TCP 8m16s chatqna-teirerank ClusterIP 10.115.146.21 <none> 80/TCP 8m16s chatqna-tgi ClusterIP 10.108.195.244 <none> 80/TCP 8m16s kubernetes ClusterIP 10.92.0.100 <none> 443/TCP 11d
To access the services running in your Kubernetes cluster from your local machine, you ... | ai_ref_knowledge | OPEA Documentation | 8m16s chatqna-tei ClusterIP 10.105.204.12 <none> 80/TCP 8m16s chatqna-teirerank ClusterIP 10.115.146.21 <none> 80/TCP 8m16s chatqna-tgi ClusterIP 10.108.195.244 <none> 80/TCP 8m16s kubernetes ClusterIP 10.92.0.100 <none> 443/TCP 11d
To access the services running in your Kubernetes cluster from your local machine, you ... | 8m16s chatqna-tei ClusterIP 10.105.204.12 <none> 80/TCP 8m16s chatqna-teirerank ClusterIP 10.115.146.21 <none> 80/TCP 8m16s chatqna-tgi ClusterIP 10.108.195.244 <none> 80/TCP 8m16s kubernetes ClusterIP 10.92.0.100 <none> 443/TCP 11d
To access the services running in your Kubernetes cluster from your local machine, you ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
511c474c-49d0-46a9-9a0c-2be0bbb32a46 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 114 | opea-semantic-v1 | 5b906c1effdda09a | We can see that it is serving at port `30304` based on this configuration via a NodePort.
The next step is to get the `<k8s-node-ip-address>` by running:
```bash
kubectl get nodes -o wide | ai_ref_knowledge | OPEA Documentation | We can see that it is serving at port `30304` based on this configuration via a NodePort.
The next step is to get the `<k8s-node-ip-address>` by running:
```bash
kubectl get nodes -o wide | We can see that it is serving at port `30304` based on this configuration via a NodePort.
The next step is to get the `<k8s-node-ip-address>` by running:
```bash
kubectl get nodes -o wide | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
541fb88c-1693-486b-9267-30264b56e594 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 51 | opea-semantic-v1 | 5cb2e509d1117507 | curl http://localhost:8888/v1/chatqna -H "Content-Type: application/json" -d '{ "model": "Intel/neural-chat-7b-v3-3", "messages": "What is OPEA?" }'
>**NOTE:** In the curl command, in addition to our prompt, we are specifying the LLM model to use. | ai_ref_knowledge | OPEA Documentation | curl http://localhost:8888/v1/chatqna -H "Content-Type: application/json" -d '{ "model": "Intel/neural-chat-7b-v3-3", "messages": "What is OPEA?" }'
>**NOTE:** In the curl command, in addition to our prompt, we are specifying the LLM model to use. | curl http://localhost:8888/v1/chatqna -H "Content-Type: application/json" -d '{ "model": "Intel/neural-chat-7b-v3-3", "messages": "What is OPEA?" }'
>**NOTE:** In the curl command, in addition to our prompt, we are specifying the LLM model to use. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
56dcfaf7-d296-4575-bf69-b478a2b47b6a | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 61 | opea-semantic-v1 | af3f5a977165b35a | This example leverages the OPEA document for its RAG-based content. You can download the [OPEA document](https://opea-project.github.io/latest/_downloads/41c91aec1d47f20ca22350daa8c2cadc/what_is_opea.pdf) and upload it using the UI.
Local File `what_is_opea.pdf` Upload: | ai_ref_knowledge | OPEA Documentation | This example leverages the OPEA document for its RAG-based content. You can download the [OPEA document](https://opea-project.github.io/latest/_downloads/41c91aec1d47f20ca22350daa8c2cadc/what_is_opea.pdf) and upload it using the UI.
Local File `what_is_opea.pdf` Upload: | This example leverages the OPEA document for its RAG-based content. You can download the [OPEA document](https://opea-project.github.io/latest/_downloads/41c91aec1d47f20ca22350daa8c2cadc/what_is_opea.pdf) and upload it using the UI.
Local File `what_is_opea.pdf` Upload: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
58f6f739-19dd-43f8-aa1e-887d0560fb1f | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 101 | opea-semantic-v1 | 925542a502c049bb | Learning, and it’s the cornerstone of today’s Machine Learning breakthroughs.\n\nDeep Learning makes machines act more like humans through their ability to generalize from very large"}
**NOTE**: After TGI service is started, it takes a few minutes to load the LLM model and warm up, before it reaches the `Ready` state. | ai_ref_knowledge | OPEA Documentation | Learning, and it’s the cornerstone of today’s Machine Learning breakthroughs.\n\nDeep Learning makes machines act more like humans through their ability to generalize from very large"}
**NOTE**: After TGI service is started, it takes a few minutes to load the LLM model and warm up, before it reaches the `Ready` state. | Learning, and it’s the cornerstone of today’s Machine Learning breakthroughs.\n\nDeep Learning makes machines act more like humans through their ability to generalize from very large"}
**NOTE**: After TGI service is started, it takes a few minutes to load the LLM model and warm up, before it reaches the `Ready` state. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5ca25875-e8d5-4da3-b6ba-59616e9d0a41 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 65 | opea-semantic-v1 | b556fc4d9afc6c17 | Use the following command to forward traffic from your local machine to the service running in the Kubernetes cluster: ```bash kubectl port-forward svc/chatqna 8888:8888 &
Similarly, Test the service: | ai_ref_knowledge | OPEA Documentation | Use the following command to forward traffic from your local machine to the service running in the Kubernetes cluster: ```bash kubectl port-forward svc/chatqna 8888:8888 &
Similarly, Test the service: | Use the following command to forward traffic from your local machine to the service running in the Kubernetes cluster: ```bash kubectl port-forward svc/chatqna 8888:8888 &
Similarly, Test the service: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5d3e6e67-60a0-4586-8928-bd1534f0c09e | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 78 | opea-semantic-v1 | 3410a38727e1e795 | The length of the embedding vector is determined by the embedding model. Here we use the model EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5", which creates a vector of size 768.
Check the vector dimension of your embedding model and set
`your_embedding` dimension equal to it. | ai_ref_knowledge | OPEA Documentation | The length of the embedding vector is determined by the embedding model. Here we use the model EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5", which creates a vector of size 768.
Check the vector dimension of your embedding model and set
`your_embedding` dimension equal to it. | The length of the embedding vector is determined by the embedding model. Here we use the model EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5", which creates a vector of size 768.
Check the vector dimension of your embedding model and set
`your_embedding` dimension equal to it. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5ddc9a6b-e765-4bfb-9bb9-d867cb30acf3 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 82 | opea-semantic-v1 | 45216a379b4b4b42 | a list of top `n` retrieved documents relevant to the input query, and top_n where n refers to the number of documents to be returned.
The output is retrieved text that is relevant to the input data: | ai_ref_knowledge | OPEA Documentation | a list of top `n` retrieved documents relevant to the input query, and top_n where n refers to the number of documents to be returned.
The output is retrieved text that is relevant to the input data: | a list of top `n` retrieved documents relevant to the input query, and top_n where n refers to the number of documents to be returned.
The output is retrieved text that is relevant to the input data: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
60c60e73-317d-4aca-9676-e989c6ffd438 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 112 | opea-semantic-v1 | 6a073447068a861d | the frontend, open the following URL in your browser: `http://{k8s-node-ip-address}:${port}` You can find the NGINX port using the following command: ```bash kubectl get service chatqna-nginx
Which shows the Nginx port as follows: | ai_ref_knowledge | OPEA Documentation | the frontend, open the following URL in your browser: `http://{k8s-node-ip-address}:${port}` You can find the NGINX port using the following command: ```bash kubectl get service chatqna-nginx
Which shows the Nginx port as follows: | the frontend, open the following URL in your browser: `http://{k8s-node-ip-address}:${port}` You can find the NGINX port using the following command: ```bash kubectl get service chatqna-nginx
Which shows the Nginx port as follows: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
63162a92-8c8e-46e6-8621-29e7d0a5bc2b | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 1 | opea-semantic-v1 | 99bd1d23e57075b3 | guide will show how to build an e2e chatQnA application using the Redis VectorDB and the neural-chat-7b-v3-3 model, deployed on a Kubernetes cluster using Helm.
For more information on how to setup a Xeon-based Kubernetes cluster along with the development pre-requisites, refer to [Kubernetes Cluster and Development En... | ai_ref_knowledge | OPEA Documentation | guide will show how to build an e2e chatQnA application using the Redis VectorDB and the neural-chat-7b-v3-3 model, deployed on a Kubernetes cluster using Helm.
For more information on how to setup a Xeon-based Kubernetes cluster along with the development pre-requisites, refer to [Kubernetes Cluster and Development En... | guide will show how to build an e2e chatQnA application using the Redis VectorDB and the neural-chat-7b-v3-3 model, deployed on a Kubernetes cluster using Helm.
For more information on how to setup a Xeon-based Kubernetes cluster along with the development pre-requisites, refer to [Kubernetes Cluster and Development En... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
63a29857-4f91-4605-b469-6aedae4f5601 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 110 | opea-semantic-v1 | 13f3bfb32e077540 | # The dataprep service will add a .txt postfix for link file
curl -X POST "http://localhost:6007/v1/dataprep/delete_file" \
-d '{"file_path": "https://opea.dev.txt"}' \
-H "Content-Type: application/json" | ai_ref_knowledge | OPEA Documentation | # The dataprep service will add a .txt postfix for link file
curl -X POST "http://localhost:6007/v1/dataprep/delete_file" \
-d '{"file_path": "https://opea.dev.txt"}' \
-H "Content-Type: application/json" | # The dataprep service will add a .txt postfix for link file
curl -X POST "http://localhost:6007/v1/dataprep/delete_file" \
-d '{"file_path": "https://opea.dev.txt"}' \
-H "Content-Type: application/json" | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
64b591e3-4b42-4627-ac06-e3d87aaee8bb | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 54 | opea-semantic-v1 | 80c070360e287a7f | data: b' Public' data: b' Em' data: b'ploy' data: b'ees' data: b' of' data: b' Alabama' . . . data: b'' data: b'' data: [DONE]
Which is essentially the following sentence: | ai_ref_knowledge | OPEA Documentation | data: b' Public' data: b' Em' data: b'ploy' data: b'ees' data: b' of' data: b' Alabama' . . . data: b'' data: b'' data: [DONE]
Which is essentially the following sentence: | data: b' Public' data: b' Em' data: b'ploy' data: b'ees' data: b' of' data: b' Alabama' . . . data: b'' data: b'' data: [DONE]
Which is essentially the following sentence: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
64df281c-223f-47c3-a0e9-4106b355c798 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 49 | opea-semantic-v1 | 9a82f24628c79f1b | ### Accessing the ChatQnA application
Use the following command to forward traffic from your local machine to the service running in the Kubernetes cluster:
```bash
kubectl port-forward svc/chatqna 8888:8888 & | ai_ref_knowledge | OPEA Documentation | ### Accessing the ChatQnA application
Use the following command to forward traffic from your local machine to the service running in the Kubernetes cluster:
```bash
kubectl port-forward svc/chatqna 8888:8888 & | ### Accessing the ChatQnA application
Use the following command to forward traffic from your local machine to the service running in the Kubernetes cluster:
```bash
kubectl port-forward svc/chatqna 8888:8888 & | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
64f060e4-092b-4753-a8f9-abf7d84ab0e3 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 108 | opea-semantic-v1 | 0034f8ae4c2f13b8 | This command updates a knowledge base by submitting a list of HTTP links for processing.
To get a list of uploaded files: | ai_ref_knowledge | OPEA Documentation | This command updates a knowledge base by submitting a list of HTTP links for processing.
To get a list of uploaded files: | This command updates a knowledge base by submitting a list of HTTP links for processing.
To get a list of uploaded files: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
66859904-1dfc-4a72-a76e-8191e25405e4 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 25 | opea-semantic-v1 | ff7c814074da8913 | To use the bfloat16 data type for the LLM in TGI, modify the `values.yaml` file located in `GenAIInfra/helm-charts/common/tgi/`. Uncomment or add the following line:
```yaml
extraCmdArgs: ["--dtype","bfloat16"] | ai_ref_knowledge | OPEA Documentation | To use the bfloat16 data type for the LLM in TGI, modify the `values.yaml` file located in `GenAIInfra/helm-charts/common/tgi/`. Uncomment or add the following line:
```yaml
extraCmdArgs: ["--dtype","bfloat16"] | To use the bfloat16 data type for the LLM in TGI, modify the `values.yaml` file located in `GenAIInfra/helm-charts/common/tgi/`. Uncomment or add the following line:
```yaml
extraCmdArgs: ["--dtype","bfloat16"] | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
71925c12-d785-4a9d-bc8e-46c58216ff25 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 21 | opea-semantic-v1 | 0aaac2040a3812a8 | To enable UI, uncomment the following lines in `GenAIInfra/helm-charts/chatqna/values.yaml`: ```bash chatqna-ui: image: repository: "opea/chatqna-ui" tag: "latest" containerPort: "5173"
Next, we will update the dependencies for all Helm charts in the specified directory and ensure the `chatqna` Helm chart is ready for ... | ai_ref_knowledge | OPEA Documentation | To enable UI, uncomment the following lines in `GenAIInfra/helm-charts/chatqna/values.yaml`: ```bash chatqna-ui: image: repository: "opea/chatqna-ui" tag: "latest" containerPort: "5173"
Next, we will update the dependencies for all Helm charts in the specified directory and ensure the `chatqna` Helm chart is ready for ... | To enable UI, uncomment the following lines in `GenAIInfra/helm-charts/chatqna/values.yaml`: ```bash chatqna-ui: image: repository: "opea/chatqna-ui" tag: "latest" containerPort: "5173"
Next, we will update the dependencies for all Helm charts in the specified directory and ensure the `chatqna` Helm chart is ready for ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
71ea9d05-fc11-4fea-a76d-189a90754e4f | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 15 | opea-semantic-v1 | 6cd220b2d9649e31 | Update the following section and save file: ```yaml # chatqna/values.yaml global: http_proxy: "http://your-proxy-address:port" https_proxy: "http://your-proxy-address:port" no_proxy: "localhost,127.0.0.1,localaddress,.localdomain.com"
## Use Case Setup | ai_ref_knowledge | OPEA Documentation | Update the following section and save file: ```yaml # chatqna/values.yaml global: http_proxy: "http://your-proxy-address:port" https_proxy: "http://your-proxy-address:port" no_proxy: "localhost,127.0.0.1,localaddress,.localdomain.com"
## Use Case Setup | Update the following section and save file: ```yaml # chatqna/values.yaml global: http_proxy: "http://your-proxy-address:port" https_proxy: "http://your-proxy-address:port" no_proxy: "localhost,127.0.0.1,localaddress,.localdomain.com"
## Use Case Setup | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
71f92119-4274-49ab-831b-7be33e450483 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 44 | opea-semantic-v1 | 114cec4d08b6cee0 | Before starting the validation of microservices, check the network configuration of services using: ```bash kubectl get svc
This command will display a list of services along with their network-related details such as cluster IP and ports. | ai_ref_knowledge | OPEA Documentation | Before starting the validation of microservices, check the network configuration of services using: ```bash kubectl get svc
This command will display a list of services along with their network-related details such as cluster IP and ports. | Before starting the validation of microservices, check the network configuration of services using: ```bash kubectl get svc
This command will display a list of services along with their network-related details such as cluster IP and ports. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
78a87892-392b-4fd2-b37f-ae6caed73ae4 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 33 | opea-semantic-v1 | 1fb5e255ab9e834b | for all the microservices to get up and running. Go to the next section which is [Validate Microservices](#validate-microservices) to verify that the deployment is successful.
### Validate microservice
#### Check the pod status
To check if all the pods have started, run: | ai_ref_knowledge | OPEA Documentation | for all the microservices to get up and running. Go to the next section which is [Validate Microservices](#validate-microservices) to verify that the deployment is successful.
### Validate microservice
#### Check the pod status
To check if all the pods have started, run: | for all the microservices to get up and running. Go to the next section which is [Validate Microservices](#validate-microservices) to verify that the deployment is successful.
### Validate microservice
#### Check the pod status
To check if all the pods have started, run: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
797ccf1f-1cb9-45b9-95ae-56e5f3cf4789 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 75 | opea-semantic-v1 | 47fdec937a108985 | model used is "BAAI/bge-base-en-v1.5", which has a vector size of 768. So the output of the `curl` command is an embedded vector of length 768.
### Retriever Microservice
Use the following command to forward traffic from your local machine to the Retriever service running in your Kubernetes cluster:
```bash
kubectl por... | ai_ref_knowledge | OPEA Documentation | model used is "BAAI/bge-base-en-v1.5", which has a vector size of 768. So the output of the `curl` command is an embedded vector of length 768.
### Retriever Microservice
Use the following command to forward traffic from your local machine to the Retriever service running in your Kubernetes cluster:
```bash
kubectl por... | model used is "BAAI/bge-base-en-v1.5", which has a vector size of 768. So the output of the `curl` command is an embedded vector of length 768.
### Retriever Microservice
Use the following command to forward traffic from your local machine to the Retriever service running in your Kubernetes cluster:
```bash
kubectl por... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7a715c8d-9e84-488e-83df-41047d42a7e5 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 69 | opea-semantic-v1 | 87c5d03a9251a6fc | parsed into the below sentence which shows how the LLM has picked up the right context to answer the question correctly after the document upload:
Open Platform for Enterprise AI (Open Platform for Enterprise AI) is a framework that focuses on creating and evaluating open, multi-provider, robust, and composable generat... | ai_ref_knowledge | OPEA Documentation | parsed into the below sentence which shows how the LLM has picked up the right context to answer the question correctly after the document upload:
Open Platform for Enterprise AI (Open Platform for Enterprise AI) is a framework that focuses on creating and evaluating open, multi-provider, robust, and composable generat... | parsed into the below sentence which shows how the LLM has picked up the right context to answer the question correctly after the document upload:
Open Platform for Enterprise AI (Open Platform for Enterprise AI) is a framework that focuses on creating and evaluating open, multi-provider, robust, and composable generat... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8262a984-3af7-4b58-b356-acbc3ce4ee83 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 9 | opea-semantic-v1 | 1a38ca0d12e617b7 | from the default fp32, the memory requirement can be further relaxed. Instructions to switch to bf16 are provided in the [Use Case Setup](#use-case-setup) section below.
### Install Helm
First, ensure that Helm (version >= 3.15) is installed on your system. Helm is an essential tool for managing Kubernetes applications... | ai_ref_knowledge | OPEA Documentation | from the default fp32, the memory requirement can be further relaxed. Instructions to switch to bf16 are provided in the [Use Case Setup](#use-case-setup) section below.
### Install Helm
First, ensure that Helm (version >= 3.15) is installed on your system. Helm is an essential tool for managing Kubernetes applications... | from the default fp32, the memory requirement can be further relaxed. Instructions to switch to bf16 are provided in the [Use Case Setup](#use-case-setup) section below.
### Install Helm
First, ensure that Helm (version >= 3.15) is installed on your system. Helm is an essential tool for managing Kubernetes applications... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
874cfa6c-89f3-4694-9fa6-3fc4169116de | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 17 | opea-semantic-v1 | d1d47eb09d9f3029 | scalable deployment and easy management of the GenAI application suite within Kubernetes environments. For detailed configurations and common components, visit the [GenAIInfra common components directory](https://github.com/opea-project/GenAIInfra/tree/main/helm-charts/common).
This use case employs a tailored combinat... | ai_ref_knowledge | OPEA Documentation | scalable deployment and easy management of the GenAI application suite within Kubernetes environments. For detailed configurations and common components, visit the [GenAIInfra common components directory](https://github.com/opea-project/GenAIInfra/tree/main/helm-charts/common).
This use case employs a tailored combinat... | scalable deployment and easy management of the GenAI application suite within Kubernetes environments. For detailed configurations and common components, visit the [GenAIInfra common components directory](https://github.com/opea-project/GenAIInfra/tree/main/helm-charts/common).
This use case employs a tailored combinat... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8946cfa2-090b-4fb8-aa3e-02ac2e57d636 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 59 | opea-semantic-v1 | 35dc7f66a66a02e7 | Test the service:
If you want to add to or update the default knowledge base, you can use the following
commands. The dataprep microservice extracts the text from the provided data
source (multiple data source types are supported such as PDF, Word, and URLs), chunks the data, embeds each chunk using the embedding micro... | ai_ref_knowledge | OPEA Documentation | Test the service:
If you want to add to or update the default knowledge base, you can use the following
commands. The dataprep microservice extracts the text from the provided data
source (multiple data source types are supported such as PDF, Word, and URLs), chunks the data, embeds each chunk using the embedding micro... | Test the service:
If you want to add to or update the default knowledge base, you can use the following
commands. The dataprep microservice extracts the text from the provided data
source (multiple data source types are supported such as PDF, Word, and URLs), chunks the data, embeds each chunk using the embedding micro... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8c63cfc8-c501-4c6c-8293-80a45e809edd | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 77 | opea-semantic-v1 | 9f560b30dbd16d0f | Test the service:
To consume the retriever microservice, you need to generate a mock embedding
vector by Python script. The length of the embedding vector is determined by the
embedding model. Here we use the
model EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5", which creates a vector of size 768. | ai_ref_knowledge | OPEA Documentation | Test the service:
To consume the retriever microservice, you need to generate a mock embedding
vector by Python script. The length of the embedding vector is determined by the
embedding model. Here we use the
model EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5", which creates a vector of size 768. | Test the service:
To consume the retriever microservice, you need to generate a mock embedding
vector by Python script. The length of the embedding vector is determined by the
embedding model. Here we use the
model EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5", which creates a vector of size 768. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8ed28d35-7408-4ea4-a748-24b7577d2c61 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 62 | opea-semantic-v1 | ccc3a251e807a7cc | curl -X POST "http://localhost:6007/v1/dataprep" \ -H "Content-Type: multipart/form-data" \ -F "files=@./what_is_opea.pdf"
This command updates a knowledge base by uploading a local file for processing. Update the file path according to your environment. | ai_ref_knowledge | OPEA Documentation | curl -X POST "http://localhost:6007/v1/dataprep" \ -H "Content-Type: multipart/form-data" \ -F "files=@./what_is_opea.pdf"
This command updates a knowledge base by uploading a local file for processing. Update the file path according to your environment. | curl -X POST "http://localhost:6007/v1/dataprep" \ -H "Content-Type: multipart/form-data" \ -F "files=@./what_is_opea.pdf"
This command updates a knowledge base by uploading a local file for processing. Update the file path according to your environment. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
92c59779-ecf7-4640-85f7-fe06ea6fb441 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 28 | opea-semantic-v1 | 237d8ebeca0c9ff8 | variables to set up the use case ```bash export MODELDIR="" #export MODELDIR="/mnt/opea-models" if you want to cache the model. export MODELNAME="Intel/neural-chat-7b-v3-3" export EMBEDDING_MODELNAME="BAAI/bge-base-en-v1.5" export RERANKER_MODELNAME="BAAI/bge-reranker-base"
> **Note:**
>
> Setting `MODELDIR` to an emp... | ai_ref_knowledge | OPEA Documentation | variables to set up the use case ```bash export MODELDIR="" #export MODELDIR="/mnt/opea-models" if you want to cache the model. export MODELNAME="Intel/neural-chat-7b-v3-3" export EMBEDDING_MODELNAME="BAAI/bge-base-en-v1.5" export RERANKER_MODELNAME="BAAI/bge-reranker-base"
> **Note:**
>
> Setting `MODELDIR` to an emp... | variables to set up the use case ```bash export MODELDIR="" #export MODELDIR="/mnt/opea-models" if you want to cache the model. export MODELNAME="Intel/neural-chat-7b-v3-3" export EMBEDDING_MODELNAME="BAAI/bge-base-en-v1.5" export RERANKER_MODELNAME="BAAI/bge-reranker-base"
> **Note:**
>
> Setting `MODELDIR` to an emp... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
973c68c7-3e16-49da-9c5d-7984606fbb06 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 8 | opea-semantic-v1 | 95ef3a539bba04ec | uses only `~24 GiB` of memory, similar instance types with at least 32 vCPUs and 32 GiB of memory are recommended to ensure smooth performance.
By switching to bf16 from the default fp32, the memory requirement can be further relaxed. Instructions to switch to bf16 are provided in the [Use Case Setup](#use-case-setup) ... | ai_ref_knowledge | OPEA Documentation | uses only `~24 GiB` of memory, similar instance types with at least 32 vCPUs and 32 GiB of memory are recommended to ensure smooth performance.
By switching to bf16 from the default fp32, the memory requirement can be further relaxed. Instructions to switch to bf16 are provided in the [Use Case Setup](#use-case-setup) ... | uses only `~24 GiB` of memory, similar instance types with at least 32 vCPUs and 32 GiB of memory are recommended to ensure smooth performance.
By switching to bf16 from the default fp32, the memory requirement can be further relaxed. Instructions to switch to bf16 are provided in the [Use Case Setup](#use-case-setup) ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
97db1afd-a0b3-407f-aca2-7d96dbea2c61 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 92 | opea-semantic-v1 | 73ff31a9c7a326c2 | the following command to forward traffic from your local machine to the Reranking service running in the Kubernetes cluster: ```bash kubectl port-forward svc/chatqna-teirerank 8808:80 &
Test the service: | ai_ref_knowledge | OPEA Documentation | the following command to forward traffic from your local machine to the Reranking service running in the Kubernetes cluster: ```bash kubectl port-forward svc/chatqna-teirerank 8808:80 &
Test the service: | the following command to forward traffic from your local machine to the Reranking service running in the Kubernetes cluster: ```bash kubectl port-forward svc/chatqna-teirerank 8808:80 &
Test the service: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
9cd0f8e9-b8d6-4ec5-94db-d32009cc9e59 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 96 | opea-semantic-v1 | f0cf3663fa434e4a | ### TGI Service
Use the following command to forward traffic from your local machine to the service running in your Kubernetes cluster:
```bash
kubectl port-forward svc/chatqna-tgi 9009:80 & | ai_ref_knowledge | OPEA Documentation | ### TGI Service
Use the following command to forward traffic from your local machine to the service running in your Kubernetes cluster:
```bash
kubectl port-forward svc/chatqna-tgi 9009:80 & | ### TGI Service
Use the following command to forward traffic from your local machine to the service running in your Kubernetes cluster:
```bash
kubectl port-forward svc/chatqna-tgi 9009:80 & | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
9d9d976c-d4fa-42f8-8ac7-df835bd0a917 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 19 | opea-semantic-v1 | b9ec892c373b173e | Tools and models mentioned in the table are configurable either through the environment variable or `values.yaml`
Set a new [namespace](k8s_getting_started.md#create-and-set-namespace) and switch to it if needed | ai_ref_knowledge | OPEA Documentation | Tools and models mentioned in the table are configurable either through the environment variable or `values.yaml`
Set a new [namespace](k8s_getting_started.md#create-and-set-namespace) and switch to it if needed | Tools and models mentioned in the table are configurable either through the environment variable or `values.yaml`
Set a new [namespace](k8s_getting_started.md#create-and-set-namespace) and switch to it if needed | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
9eca2a45-a3ff-4070-a85b-b048f2909457 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 31 | opea-semantic-v1 | f680904d02faa410 | ```bash helm install chatqna chatqna \ --set global.HUGGINGFACEHUB_API_TOKEN=${HF_TOKEN} \ --set global.modelUseHostPath=${MODELDIR} \ --set tgi.LLM_MODEL_ID=${MODELNAME} \ --set tei.EMBEDDING_MODEL_ID=${EMBEDDING_MODELNAME} \ --set teirerank.RERANK_MODEL_ID=${RERANKER_MODELNAME}
**OUTPUT:**
```bash
NAME: chatqna
LAST ... | ai_ref_knowledge | OPEA Documentation | ```bash helm install chatqna chatqna \ --set global.HUGGINGFACEHUB_API_TOKEN=${HF_TOKEN} \ --set global.modelUseHostPath=${MODELDIR} \ --set tgi.LLM_MODEL_ID=${MODELNAME} \ --set tei.EMBEDDING_MODEL_ID=${EMBEDDING_MODELNAME} \ --set teirerank.RERANK_MODEL_ID=${RERANKER_MODELNAME}
**OUTPUT:**
```bash
NAME: chatqna
LAST ... | ```bash helm install chatqna chatqna \ --set global.HUGGINGFACEHUB_API_TOKEN=${HF_TOKEN} \ --set global.modelUseHostPath=${MODELDIR} \ --set tgi.LLM_MODEL_ID=${MODELNAME} \ --set tei.EMBEDDING_MODEL_ID=${EMBEDDING_MODELNAME} \ --set teirerank.RERANK_MODEL_ID=${RERANKER_MODELNAME}
**OUTPUT:**
```bash
NAME: chatqna
LAST ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
9ff952ab-0704-4ba1-b44a-b6e05c557843 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 57 | opea-semantic-v1 | 47be88b2ea157993 | In the upcoming sections, we will see how this answer can be improved with RAG.
### Dataprep Microservice
Use the following command to forward traffic from your local machine to the data-prep service running in the Kubernetes cluster, which allows uploading documents to provide a more domain-specific context:
```bash
k... | ai_ref_knowledge | OPEA Documentation | In the upcoming sections, we will see how this answer can be improved with RAG.
### Dataprep Microservice
Use the following command to forward traffic from your local machine to the data-prep service running in the Kubernetes cluster, which allows uploading documents to provide a more domain-specific context:
```bash
k... | In the upcoming sections, we will see how this answer can be improved with RAG.
### Dataprep Microservice
Use the following command to forward traffic from your local machine to the data-prep service running in the Kubernetes cluster, which allows uploading documents to provide a more domain-specific context:
```bash
k... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a441ab30-35b2-439f-927e-44bdb74a29b1 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 100 | opea-semantic-v1 | 43ab6cf94ae312e4 | TGI service generates text for the input prompt. Here is the expected result from TGI:
{"generated_text":"We have all heard the buzzword, but our understanding of it is still growing. It’s a sub-field of Machine Learning, and it’s the cornerstone of today’s Machine Learning breakthroughs.\n\nDeep Learning makes machine... | ai_ref_knowledge | OPEA Documentation | TGI service generates text for the input prompt. Here is the expected result from TGI:
{"generated_text":"We have all heard the buzzword, but our understanding of it is still growing. It’s a sub-field of Machine Learning, and it’s the cornerstone of today’s Machine Learning breakthroughs.\n\nDeep Learning makes machine... | TGI service generates text for the input prompt. Here is the expected result from TGI:
{"generated_text":"We have all heard the buzzword, but our understanding of it is still growing. It’s a sub-field of Machine Learning, and it’s the cornerstone of today’s Machine Learning breakthroughs.\n\nDeep Learning makes machine... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a5e56ac7-459e-464c-b95f-57bd2c235e77 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 10 | opea-semantic-v1 | 4d39f134ee5d21ee | managing Kubernetes applications. It simplifies the deployment and management of Kubernetes applications using Helm charts. For detailed installation instructions, refer to the [Helm Installation Guide](https://helm.sh/docs/intro/install/)
### Clone Repository
The next step is to clone the GenAIInfra which is the cont... | ai_ref_knowledge | OPEA Documentation | managing Kubernetes applications. It simplifies the deployment and management of Kubernetes applications using Helm charts. For detailed installation instructions, refer to the [Helm Installation Guide](https://helm.sh/docs/intro/install/)
### Clone Repository
The next step is to clone the GenAIInfra which is the cont... | managing Kubernetes applications. It simplifies the deployment and management of Kubernetes applications using Helm charts. For detailed installation instructions, refer to the [Helm Installation Guide](https://helm.sh/docs/intro/install/)
### Clone Repository
The next step is to clone the GenAIInfra which is the cont... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a638bace-7aba-452b-a645-9dfa2ffcbe9c | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 120 | opea-semantic-v1 | fde7f8c84a8311ff | ### Basic UI via Port Forwarding
Alternatively, You can also choose to use port forwarding as shown previously using:
```bash
kubectl port-forward service/chatqna-nginx 8080:80 & | ai_ref_knowledge | OPEA Documentation | ### Basic UI via Port Forwarding
Alternatively, You can also choose to use port forwarding as shown previously using:
```bash
kubectl port-forward service/chatqna-nginx 8080:80 & | ### Basic UI via Port Forwarding
Alternatively, You can also choose to use port forwarding as shown previously using:
```bash
kubectl port-forward service/chatqna-nginx 8080:80 & | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a72ce6b8-d9f5-4c5f-9d51-3822deafa249 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 13 | opea-semantic-v1 | 7b1001faf94d94d9 | ### Proxy Settings
If you are behind a corporate VPN, proxy settings must be added for services requiring internet access, such as the LLM microservice, embedding service, reranking service, and other backend services. Proxy can be set in the `values.yaml`. Open the `values.yaml` file using an editor
```bash
vi chatqna... | ai_ref_knowledge | OPEA Documentation | ### Proxy Settings
If you are behind a corporate VPN, proxy settings must be added for services requiring internet access, such as the LLM microservice, embedding service, reranking service, and other backend services. Proxy can be set in the `values.yaml`. Open the `values.yaml` file using an editor
```bash
vi chatqna... | ### Proxy Settings
If you are behind a corporate VPN, proxy settings must be added for services requiring internet access, such as the LLM microservice, embedding service, reranking service, and other backend services. Proxy can be set in the `values.yaml`. Open the `values.yaml` file using an editor
```bash
vi chatqna... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a8377bc0-e63a-4777-ac8d-c718d2bc561f | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 88 | opea-semantic-v1 | a927e730b5614e2e | the ecosystem while keeping enterprise-level needs front and\ncenter.\nOPEA simplifies the implementation of enterprise-grade composite GenAI solutions, starting\nwith a focus on Retrieval Augmented Generative AI (RAG).
The platform is designed to facilitate\nefficient integration of secure, performant, and cost-effect... | ai_ref_knowledge | OPEA Documentation | the ecosystem while keeping enterprise-level needs front and\ncenter.\nOPEA simplifies the implementation of enterprise-grade composite GenAI solutions, starting\nwith a focus on Retrieval Augmented Generative AI (RAG).
The platform is designed to facilitate\nefficient integration of secure, performant, and cost-effect... | the ecosystem while keeping enterprise-level needs front and\ncenter.\nOPEA simplifies the implementation of enterprise-grade composite GenAI solutions, starting\nwith a focus on Retrieval Augmented Generative AI (RAG).
The platform is designed to facilitate\nefficient integration of secure, performant, and cost-effect... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
aa8f7eed-8e55-47bb-b3a9-bae3305c8650 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 34 | opea-semantic-v1 | b51263486b8cc0e4 | ### Validate microservice #### Check the pod status To check if all the pods have started, run:
```bash
kubectl get pods | ai_ref_knowledge | OPEA Documentation | ### Validate microservice #### Check the pod status To check if all the pods have started, run:
```bash
kubectl get pods | ### Validate microservice #### Check the pod status To check if all the pods have started, run:
```bash
kubectl get pods | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ac4481d8-f120-4ba6-969c-dedfb507f45e | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 35 | opea-semantic-v1 | 26eab24bacb05045 | You should expect a similar output as below:
NAME READY STATUS RESTARTS AGE
chatqna-chatqna-ui-77dbdfc949-6dtms 1/1 Running 0 5m7s
chatqna-data-prep-798f59f447-4frqt 1/1 Running 0 5m7s
chatqna-df57cc766-t6lkg 1/1 Running 0 5m7s
chatqna-nginx-5dd47bfc7d-54x96 1/1 Running 0 5m7s
chatqna-redis-vector-db-7f489b6bb6-mvzbw 1... | ai_ref_knowledge | OPEA Documentation | You should expect a similar output as below:
NAME READY STATUS RESTARTS AGE
chatqna-chatqna-ui-77dbdfc949-6dtms 1/1 Running 0 5m7s
chatqna-data-prep-798f59f447-4frqt 1/1 Running 0 5m7s
chatqna-df57cc766-t6lkg 1/1 Running 0 5m7s
chatqna-nginx-5dd47bfc7d-54x96 1/1 Running 0 5m7s
chatqna-redis-vector-db-7f489b6bb6-mvzbw 1... | You should expect a similar output as below:
NAME READY STATUS RESTARTS AGE
chatqna-chatqna-ui-77dbdfc949-6dtms 1/1 Running 0 5m7s
chatqna-data-prep-798f59f447-4frqt 1/1 Running 0 5m7s
chatqna-df57cc766-t6lkg 1/1 Running 0 5m7s
chatqna-nginx-5dd47bfc7d-54x96 1/1 Running 0 5m7s
chatqna-redis-vector-db-7f489b6bb6-mvzbw 1... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
af51bc74-474f-4026-8922-2e6a97936895 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 104 | opea-semantic-v1 | 0c9d437ae509579e | And the log shows the model warm-up, please wait for a while and retry.
2024-06-05T05:45:27.707509646Z 2024-06-05T05:45:27.707361Z WARN text_generation_router: router/src/main.rs:357: `--revision` is not set
2024-06-05T05:45:27.707539740Z 2024-06-05T05:45:27.707379Z WARN text_generation_router: router/src/main.rs:358: ... | ai_ref_knowledge | OPEA Documentation | And the log shows the model warm-up, please wait for a while and retry.
2024-06-05T05:45:27.707509646Z 2024-06-05T05:45:27.707361Z WARN text_generation_router: router/src/main.rs:357: `--revision` is not set
2024-06-05T05:45:27.707539740Z 2024-06-05T05:45:27.707379Z WARN text_generation_router: router/src/main.rs:358: ... | And the log shows the model warm-up, please wait for a while and retry.
2024-06-05T05:45:27.707509646Z 2024-06-05T05:45:27.707361Z WARN text_generation_router: router/src/main.rs:357: `--revision` is not set
2024-06-05T05:45:27.707539740Z 2024-06-05T05:45:27.707379Z WARN text_generation_router: router/src/main.rs:358: ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
afaccec3-0977-4f27-9426-22167dc94c60 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 72 | opea-semantic-v1 | b21f26bdfa3b717d | Test the service:
The TEI embedding service takes in a string as input, embeds the string into a
vector of a specific length determined by the embedding model and returns this
embedded vector. | ai_ref_knowledge | OPEA Documentation | Test the service:
The TEI embedding service takes in a string as input, embeds the string into a
vector of a specific length determined by the embedding model and returns this
embedded vector. | Test the service:
The TEI embedding service takes in a string as input, embeds the string into a
vector of a specific length determined by the embedding model and returns this
embedded vector. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b21da066-d71d-424c-8ba7-22e082eed8a2 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 63 | opea-semantic-v1 | 7884eef488840050 | This command updates a knowledge base by uploading a local file for processing. Update the file path according to your environment.
You should see the following output after successful execution: | ai_ref_knowledge | OPEA Documentation | This command updates a knowledge base by uploading a local file for processing. Update the file path according to your environment.
You should see the following output after successful execution: | This command updates a knowledge base by uploading a local file for processing. Update the file path according to your environment.
You should see the following output after successful execution: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b2337a67-f1b9-4c5f-b1dc-036be2429423 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 7 | opea-semantic-v1 | e88232ccaca9dc74 | ## Prerequisites
### Hardware Prerequisites
For cloud deployments, the ChatQnA pipeline in this guide has been tested on an AWS `m7i.8xlarge` single node instance, which provides `32 vCPUs`, `128 GiB` memory and upgraded to `100 GB` of disk space. While the default deployment uses only `~24 GiB` of memory, similar inst... | ai_ref_knowledge | OPEA Documentation | ## Prerequisites
### Hardware Prerequisites
For cloud deployments, the ChatQnA pipeline in this guide has been tested on an AWS `m7i.8xlarge` single node instance, which provides `32 vCPUs`, `128 GiB` memory and upgraded to `100 GB` of disk space. While the default deployment uses only `~24 GiB` of memory, similar inst... | ## Prerequisites
### Hardware Prerequisites
For cloud deployments, the ChatQnA pipeline in this guide has been tested on an AWS `m7i.8xlarge` single node instance, which provides `32 vCPUs`, `128 GiB` memory and upgraded to `100 GB` of disk space. While the default deployment uses only `~24 GiB` of memory, similar inst... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b30631e1-434d-4cc3-92a6-fe60b2dfb5dc | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 48 | opea-semantic-v1 | ac75d9edf8ec8954 | the local machine. In another terminal, use `curl` commands to test the functionality and response of the service. `&` runs the process in the background.
Use `ctrl+c` to end the port-forwarding to test other services. | ai_ref_knowledge | OPEA Documentation | the local machine. In another terminal, use `curl` commands to test the functionality and response of the service. `&` runs the process in the background.
Use `ctrl+c` to end the port-forwarding to test other services. | the local machine. In another terminal, use `curl` commands to test the functionality and response of the service. `&` runs the process in the background.
Use `ctrl+c` to end the port-forwarding to test other services. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b5281273-4fdb-4e66-b82e-72a7fc9e2cde | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 52 | opea-semantic-v1 | f2bdcc59bad9f358 | >**NOTE:** In the curl command, in addition to our prompt, we are specifying the LLM model to use.
Here is the output for your reference: | ai_ref_knowledge | OPEA Documentation | >**NOTE:** In the curl command, in addition to our prompt, we are specifying the LLM model to use.
Here is the output for your reference: | >**NOTE:** In the curl command, in addition to our prompt, we are specifying the LLM model to use.
Here is the output for your reference: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b5f26b2f-dd7c-4f1f-8f3c-7420f27bc2df | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 53 | opea-semantic-v1 | 1a3d639c8a984776 | Here is the output for your reference:
```bash
data: b' O'
data: b'PE'
data: b'A'
data: b' stands'
data: b' Organization'
data: b' of'
data: b' Public'
data: b' Em'
data: b'ploy'
data: b'ees'
data: b' of'
data: b' Alabama'
. . . data: b''
data: b''
data: [DONE] | ai_ref_knowledge | OPEA Documentation | Here is the output for your reference:
```bash
data: b' O'
data: b'PE'
data: b'A'
data: b' stands'
data: b' Organization'
data: b' of'
data: b' Public'
data: b' Em'
data: b'ploy'
data: b'ees'
data: b' of'
data: b' Alabama'
. . . data: b''
data: b''
data: [DONE] | Here is the output for your reference:
```bash
data: b' O'
data: b'PE'
data: b'A'
data: b' stands'
data: b' Organization'
data: b' of'
data: b' Public'
data: b' Em'
data: b'ploy'
data: b'ees'
data: b' of'
data: b' Alabama'
. . . data: b''
data: b''
data: [DONE] | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b8f867ec-5238-4fed-b674-4f9152719ba7 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 102 | opea-semantic-v1 | ed6beebd34121b6d | **NOTE**: After TGI service is started, it takes a few minutes to load the LLM model and warm up, before it reaches the `Ready` state.
If you get | ai_ref_knowledge | OPEA Documentation | **NOTE**: After TGI service is started, it takes a few minutes to load the LLM model and warm up, before it reaches the `Ready` state.
If you get | **NOTE**: After TGI service is started, it takes a few minutes to load the LLM model and warm up, before it reaches the `Ready` state.
If you get | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ba87900c-f53d-4ae4-95ba-3bfd7aae92c8 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 14 | opea-semantic-v1 | 66c9ce5522071e46 | embedding service, reranking service, and other backend services. Proxy can be set in the `values.yaml`. Open the `values.yaml` file using an editor ```bash vi chatqna/values.yaml
Update the following section and save file:
```yaml
# chatqna/values.yaml
global:
http_proxy: "http://your-proxy-address:port"
https_proxy... | ai_ref_knowledge | OPEA Documentation | embedding service, reranking service, and other backend services. Proxy can be set in the `values.yaml`. Open the `values.yaml` file using an editor ```bash vi chatqna/values.yaml
Update the following section and save file:
```yaml
# chatqna/values.yaml
global:
http_proxy: "http://your-proxy-address:port"
https_proxy... | embedding service, reranking service, and other backend services. Proxy can be set in the `values.yaml`. Open the `values.yaml` file using an editor ```bash vi chatqna/values.yaml
Update the following section and save file:
```yaml
# chatqna/values.yaml
global:
http_proxy: "http://your-proxy-address:port"
https_proxy... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
bcb318ab-ccb7-43ea-97ec-afa728cc1841 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 32 | opea-semantic-v1 | 3c426c4a592575a6 | **OUTPUT:** ```bash NAME: chatqna LAST DEPLOYED: Thu Sep 5 13:40:20 2024 NAMESPACE: chatqa STATUS: deployed REVISION: 1
It takes a few minutes for all the microservices to get up and running. Go to the next section which is [Validate Microservices](#validate-microservices) to verify that the deployment is successful. | ai_ref_knowledge | OPEA Documentation | **OUTPUT:** ```bash NAME: chatqna LAST DEPLOYED: Thu Sep 5 13:40:20 2024 NAMESPACE: chatqa STATUS: deployed REVISION: 1
It takes a few minutes for all the microservices to get up and running. Go to the next section which is [Validate Microservices](#validate-microservices) to verify that the deployment is successful. | **OUTPUT:** ```bash NAME: chatqna LAST DEPLOYED: Thu Sep 5 13:40:20 2024 NAMESPACE: chatqa STATUS: deployed REVISION: 1
It takes a few minutes for all the microservices to get up and running. Go to the next section which is [Validate Microservices](#validate-microservices) to verify that the deployment is successful. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
bdf373b4-845b-4ed4-ab9b-5490903e9898 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 113 | opea-semantic-v1 | 5cf2c60a5c63a40e | NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE chatqna-nginx NodePort 10.201.220.120 <none> 80:30304/TCP 16h
We can see that it is serving at port `30304` based on this configuration via a NodePort. | ai_ref_knowledge | OPEA Documentation | NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE chatqna-nginx NodePort 10.201.220.120 <none> 80:30304/TCP 16h
We can see that it is serving at port `30304` based on this configuration via a NodePort. | NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE chatqna-nginx NodePort 10.201.220.120 <none> 80:30304/TCP 16h
We can see that it is serving at port `30304` based on this configuration via a NodePort. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c0062bd3-b5ec-4cc8-849d-911eb15ae181 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 109 | opea-semantic-v1 | ea0031f60bcea4fd | curl -X POST "http://localhost:6007/v1/dataprep/get_file" \ -H "Content-Type: application/json"
To delete the file/link you uploaded you can use the following commands: | ai_ref_knowledge | OPEA Documentation | curl -X POST "http://localhost:6007/v1/dataprep/get_file" \ -H "Content-Type: application/json"
To delete the file/link you uploaded you can use the following commands: | curl -X POST "http://localhost:6007/v1/dataprep/get_file" \ -H "Content-Type: application/json"
To delete the file/link you uploaded you can use the following commands: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c06a2a0e-e51d-4b86-8264-bfc1adcea22d | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 58 | opea-semantic-v1 | f523fd75483ca2c4 | to the data-prep service running in the Kubernetes cluster, which allows uploading documents to provide a more domain-specific context: ```bash kubectl port-forward svc/chatqna-data-prep 6007:6007 &
Test the service: | ai_ref_knowledge | OPEA Documentation | to the data-prep service running in the Kubernetes cluster, which allows uploading documents to provide a more domain-specific context: ```bash kubectl port-forward svc/chatqna-data-prep 6007:6007 &
Test the service: | to the data-prep service running in the Kubernetes cluster, which allows uploading documents to provide a more domain-specific context: ```bash kubectl port-forward svc/chatqna-data-prep 6007:6007 &
Test the service: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c25b9e6d-6046-41ce-8c06-bbabb36f7480 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 37 | opea-semantic-v1 | 08875b937470bb45 | >**Note:** Use `kubectl get pods -o wide` to check the nodes that the respective pods are running on
For example, the ChatQnA deployment starts 9 Kubernetes services. Ensure that all associated pods are running, i.e., all the pods' statuses are 'Running'. To perform a quick sanity check, use the command `kubectl get po... | ai_ref_knowledge | OPEA Documentation | >**Note:** Use `kubectl get pods -o wide` to check the nodes that the respective pods are running on
For example, the ChatQnA deployment starts 9 Kubernetes services. Ensure that all associated pods are running, i.e., all the pods' statuses are 'Running'. To perform a quick sanity check, use the command `kubectl get po... | >**Note:** Use `kubectl get pods -o wide` to check the nodes that the respective pods are running on
For example, the ChatQnA deployment starts 9 Kubernetes services. Ensure that all associated pods are running, i.e., all the pods' statuses are 'Running'. To perform a quick sanity check, use the command `kubectl get po... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c26a6019-d392-4277-9dcb-121f7b064083 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 94 | opea-semantic-v1 | 9268a693c08f1e26 | order of the similarity score. The document corresponding to the returned index with the highest score is the most relevant document for the input query.
curl http://localhost:8808/rerank \
-X POST \
-d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \
-H 'Content-Ty... | ai_ref_knowledge | OPEA Documentation | order of the similarity score. The document corresponding to the returned index with the highest score is the most relevant document for the input query.
curl http://localhost:8808/rerank \
-X POST \
-d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \
-H 'Content-Ty... | order of the similarity score. The document corresponding to the returned index with the highest score is the most relevant document for the input query.
curl http://localhost:8808/rerank \
-X POST \
-d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \
-H 'Content-Ty... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c7069a21-e9d3-4263-b276-6f3b510c0fc1 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 70 | opea-semantic-v1 | e16597aaea455bba | of enterprise-grade composite GenAI solutions, particularly Retrieval Augmented Generative AI (RAG), by simplifying the integration of secure, performant, and cost-effective GenAI workflows into business systems.
### TEI Embedding Service
Use the following command to forward traffic from your local machine to the TEI s... | ai_ref_knowledge | OPEA Documentation | of enterprise-grade composite GenAI solutions, particularly Retrieval Augmented Generative AI (RAG), by simplifying the integration of secure, performant, and cost-effective GenAI workflows into business systems.
### TEI Embedding Service
Use the following command to forward traffic from your local machine to the TEI s... | of enterprise-grade composite GenAI solutions, particularly Retrieval Augmented Generative AI (RAG), by simplifying the integration of secure, performant, and cost-effective GenAI workflows into business systems.
### TEI Embedding Service
Use the following command to forward traffic from your local machine to the TEI s... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
cab282a6-5014-486f-a864-11d907f2e5fc | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 123 | opea-semantic-v1 | f124386b58da70ac | ### Stop the services Once you are done with the entire pipeline and wish to stop and remove all the resources, use the command below:
helm uninstall chatqna | ai_ref_knowledge | OPEA Documentation | ### Stop the services Once you are done with the entire pipeline and wish to stop and remove all the resources, use the command below:
helm uninstall chatqna | ### Stop the services Once you are done with the entire pipeline and wish to stop and remove all the resources, use the command below:
helm uninstall chatqna | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
cb873173-1555-4736-89e3-fd895a2fbc41 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 3 | opea-semantic-v1 | b88c599e950a5394 | ## Overview
In this ChatQnA tutorial, we
will walk through how to enable the below list of microservices from OPEA
GenAIComps to deploy a multi-node TGI-based service solution. | ai_ref_knowledge | OPEA Documentation | ## Overview
In this ChatQnA tutorial, we
will walk through how to enable the below list of microservices from OPEA
GenAIComps to deploy a multi-node TGI-based service solution. | ## Overview
In this ChatQnA tutorial, we
will walk through how to enable the below list of microservices from OPEA
GenAIComps to deploy a multi-node TGI-based service solution. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ce1e27dd-19b8-4847-9df7-28a4c993dcb2 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 43 | opea-semantic-v1 | 6e7d9358b1452e6d | ## Interacting with ChatQnA deployment This section will walk you through what are the different ways to interact with the microservices deployed
Before starting the validation of microservices, check the network configuration of services using:
```bash
kubectl get svc | ai_ref_knowledge | OPEA Documentation | ## Interacting with ChatQnA deployment This section will walk you through what are the different ways to interact with the microservices deployed
Before starting the validation of microservices, check the network configuration of services using:
```bash
kubectl get svc | ## Interacting with ChatQnA deployment This section will walk you through what are the different ways to interact with the microservices deployed
Before starting the validation of microservices, check the network configuration of services using:
```bash
kubectl get svc | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d00bf88d-dfd5-476d-94ef-5999490febff | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 23 | opea-semantic-v1 | 4e6ef7c20c3e25c1 | ```bash # All Helm charts in the specified directory have their # dependencies up-to-date, facilitating consistent deployments. scripts/update_dependency.sh
# "chatqna" here refers to the directory name that contains the Helm
# chart for the ChatQnA application
helm dependency update chatqna | ai_ref_knowledge | OPEA Documentation | ```bash # All Helm charts in the specified directory have their # dependencies up-to-date, facilitating consistent deployments. scripts/update_dependency.sh
# "chatqna" here refers to the directory name that contains the Helm
# chart for the ChatQnA application
helm dependency update chatqna | ```bash # All Helm charts in the specified directory have their # dependencies up-to-date, facilitating consistent deployments. scripts/update_dependency.sh
# "chatqna" here refers to the directory name that contains the Helm
# chart for the ChatQnA application
helm dependency update chatqna | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d1877516-c3be-4ed9-b566-1ae6ff61fbe1 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 40 | opea-semantic-v1 | b75f0d0c6a2cd61e | 2. **Describing Pods**: For a detailed view of the pod's current state, its configuration, and its operational events, run: ```bash kubectl describe pod <pod-name>
For example, if the status of the TGI service does not show 'Running', describe the pod using the name from the above table. In our example the pod name is ... | ai_ref_knowledge | OPEA Documentation | 2. **Describing Pods**: For a detailed view of the pod's current state, its configuration, and its operational events, run: ```bash kubectl describe pod <pod-name>
For example, if the status of the TGI service does not show 'Running', describe the pod using the name from the above table. In our example the pod name is ... | 2. **Describing Pods**: For a detailed view of the pod's current state, its configuration, and its operational events, run: ```bash kubectl describe pod <pod-name>
For example, if the status of the TGI service does not show 'Running', describe the pod using the name from the above table. In our example the pod name is ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d21b1050-fa34-46d2-b712-93ea911d77a8 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 67 | opea-semantic-v1 | aacca77cf317452d | After uploading the pdf with information about OPEA, we can see that the pdf is being used as a context to answer the question correctly:
```bash
data: b' O'
data: b'PE'
data: b'A'
data: b' ('
data: b'Open'
data: b' Platform'
data: b' for'
data: b' Enterprise'
data: b' AI'
data: b')',
. . . data: b' systems'
data: b'.'... | ai_ref_knowledge | OPEA Documentation | After uploading the pdf with information about OPEA, we can see that the pdf is being used as a context to answer the question correctly:
```bash
data: b' O'
data: b'PE'
data: b'A'
data: b' ('
data: b'Open'
data: b' Platform'
data: b' for'
data: b' Enterprise'
data: b' AI'
data: b')',
. . . data: b' systems'
data: b'.'... | After uploading the pdf with information about OPEA, we can see that the pdf is being used as a context to answer the question correctly:
```bash
data: b' O'
data: b'PE'
data: b'A'
data: b' ('
data: b'Open'
data: b' Platform'
data: b' for'
data: b' Enterprise'
data: b' AI'
data: b')',
. . . data: b' systems'
data: b'.'... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d2fcaac1-72d6-4881-b5b2-6c1b85ccb685 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 2 | opea-semantic-v1 | d914a6486667fcdb | setup a Xeon-based Kubernetes cluster along with the development pre-requisites, refer to [Kubernetes Cluster and Development Environment](k8s_getting_started.md#kubernetes-cluster-and-development-environment) and for a [quick introduction to Helm Charts](k8s_getting_started.md#using-helm-charts-to-deploy).
## Overview | ai_ref_knowledge | OPEA Documentation | setup a Xeon-based Kubernetes cluster along with the development pre-requisites, refer to [Kubernetes Cluster and Development Environment](k8s_getting_started.md#kubernetes-cluster-and-development-environment) and for a [quick introduction to Helm Charts](k8s_getting_started.md#using-helm-charts-to-deploy).
## Overview | setup a Xeon-based Kubernetes cluster along with the development pre-requisites, refer to [Kubernetes Cluster and Development Environment](k8s_getting_started.md#kubernetes-cluster-and-development-environment) and for a [quick introduction to Helm Charts](k8s_getting_started.md#using-helm-charts-to-deploy).
## Overview | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d3bf5b51-195f-4baa-8202-dabedecb6487 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 90 | opea-semantic-v1 | aeb4e0161e703307 | These microservices leverage a service\ncomposer that assembles them into a megaservice thereby creating real-world Enterprise AI\napplications."}],"initial_query":"test","top_n":1}
### TEI Reranking Service | ai_ref_knowledge | OPEA Documentation | These microservices leverage a service\ncomposer that assembles them into a megaservice thereby creating real-world Enterprise AI\napplications."}],"initial_query":"test","top_n":1}
### TEI Reranking Service | These microservices leverage a service\ncomposer that assembles them into a megaservice thereby creating real-world Enterprise AI\napplications."}],"initial_query":"test","top_n":1}
### TEI Reranking Service | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d414bf14-dc95-4d4f-925b-c09174d00a10 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 5 | opea-semantic-v1 | 1745841022154684 | 1. Data Prep 2. Embedding 3. Retriever 4. Reranking 5. LLM with TGI
> **Note:** ChatQnA can also be deployed on a single node using Kubernetes provided there are adequate resources for all the associated pods, namely CPU and memory and, no constraints such as affinity, anti-affinity, or taints. | ai_ref_knowledge | OPEA Documentation | 1. Data Prep 2. Embedding 3. Retriever 4. Reranking 5. LLM with TGI
> **Note:** ChatQnA can also be deployed on a single node using Kubernetes provided there are adequate resources for all the associated pods, namely CPU and memory and, no constraints such as affinity, anti-affinity, or taints. | 1. Data Prep 2. Embedding 3. Retriever 4. Reranking 5. LLM with TGI
> **Note:** ChatQnA can also be deployed on a single node using Kubernetes provided there are adequate resources for all the associated pods, namely CPU and memory and, no constraints such as affinity, anti-affinity, or taints. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d6808bbe-09db-4784-ae4c-612d82824a95 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 83 | opea-semantic-v1 | 5a73d11799fa2d15 | The output is retrieved text that is relevant to the input data:
{"id":"13617fc8ac716a9ca5df036fd297b9ad","retrieved_docs":[{"downstream_black_list":[],"id":"7e6f2e6584947f293d6d40cccb7ef58d","text":"applications.\nMicroservices: Flexible and Scalable Architecture\nThe GenAI Microservices documentation describes a suit... | ai_ref_knowledge | OPEA Documentation | The output is retrieved text that is relevant to the input data:
{"id":"13617fc8ac716a9ca5df036fd297b9ad","retrieved_docs":[{"downstream_black_list":[],"id":"7e6f2e6584947f293d6d40cccb7ef58d","text":"applications.\nMicroservices: Flexible and Scalable Architecture\nThe GenAI Microservices documentation describes a suit... | The output is retrieved text that is relevant to the input data:
{"id":"13617fc8ac716a9ca5df036fd297b9ad","retrieved_docs":[{"downstream_black_list":[],"id":"7e6f2e6584947f293d6d40cccb7ef58d","text":"applications.\nMicroservices: Flexible and Scalable Architecture\nThe GenAI Microservices documentation describes a suit... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
dc36916d-be67-42bb-8d67-272bd8e8fe8f | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 30 | opea-semantic-v1 | 5a55bc6d34b48cb7 | ## Deploy the use case The `helm install` command will initiate all the aforementioned services such as Kubernetes pods.
```bash
helm install chatqna chatqna \
--set global.HUGGINGFACEHUB_API_TOKEN=${HF_TOKEN} \
--set global.modelUseHostPath=${MODELDIR} \
--set tgi.LLM_MODEL_ID=${MODELNAME} \
--set tei.EMBEDDING_MO... | ai_ref_knowledge | OPEA Documentation | ## Deploy the use case The `helm install` command will initiate all the aforementioned services such as Kubernetes pods.
```bash
helm install chatqna chatqna \
--set global.HUGGINGFACEHUB_API_TOKEN=${HF_TOKEN} \
--set global.modelUseHostPath=${MODELDIR} \
--set tgi.LLM_MODEL_ID=${MODELNAME} \
--set tei.EMBEDDING_MO... | ## Deploy the use case The `helm install` command will initiate all the aforementioned services such as Kubernetes pods.
```bash
helm install chatqna chatqna \
--set global.HUGGINGFACEHUB_API_TOKEN=${HF_TOKEN} \
--set global.modelUseHostPath=${MODELDIR} \
--set tgi.LLM_MODEL_ID=${MODELNAME} \
--set tei.EMBEDDING_MO... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e80a8142-136a-4bac-ac26-6170a272d797 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 111 | opea-semantic-v1 | 0c78cde1b095eb25 | curl -X POST "http://localhost:6007/v1/dataprep/delete_file" \ -d '{"file_path": "all"}' \ -H "Content-Type: application/json"
## Launch UI
### Basic UI via NodePort
To access the frontend, open the following URL in your browser:
`http://{k8s-node-ip-address}:${port}`
You can find the NGINX port using the following co... | ai_ref_knowledge | OPEA Documentation | curl -X POST "http://localhost:6007/v1/dataprep/delete_file" \ -d '{"file_path": "all"}' \ -H "Content-Type: application/json"
## Launch UI
### Basic UI via NodePort
To access the frontend, open the following URL in your browser:
`http://{k8s-node-ip-address}:${port}`
You can find the NGINX port using the following co... | curl -X POST "http://localhost:6007/v1/dataprep/delete_file" \ -d '{"file_path": "all"}' \ -H "Content-Type: application/json"
## Launch UI
### Basic UI via NodePort
To access the frontend, open the following URL in your browser:
`http://{k8s-node-ip-address}:${port}`
You can find the NGINX port using the following co... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ea15e6d4-9064-4f40-9083-d144220e7b01 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/k8s_helm.md | unknown | f8424b32-9e7e-493f-9934-cad335b62634 | 85 | opea-semantic-v1 | 77465bc2503adb06 | complex business logic and workflow orchestration, coordinating the interactions\nbetween various microservices to fulfill specific application requirements. This approach enables\nthe creation of modular yet integrated applications.
You can find a collection of use case-based\napplications in the GenAI Examples docume... | ai_ref_knowledge | OPEA Documentation | complex business logic and workflow orchestration, coordinating the interactions\nbetween various microservices to fulfill specific application requirements. This approach enables\nthe creation of modular yet integrated applications.
You can find a collection of use case-based\napplications in the GenAI Examples docume... | complex business logic and workflow orchestration, coordinating the interactions\nbetween various microservices to fulfill specific application requirements. This approach enables\nthe creation of modular yet integrated applications.
You can find a collection of use case-based\napplications in the GenAI Examples docume... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation |
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