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54596110-bc60-4544-bec7-c9fa011e9388 | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/gaudi.md | unknown | 2d6727be-f702-4d84-a699-49be5fb4af52 | 35 | opea-semantic-v1 | 36e9a97b5b165272 | #### Check Env Variables
Check the startup log by `docker compose -f ./compose.yaml logs`. The warning messages print out the variables if they are **NOT** set. | ai_ref_knowledge | OPEA Documentation | #### Check Env Variables
Check the startup log by `docker compose -f ./compose.yaml logs`. The warning messages print out the variables if they are **NOT** set. | #### Check Env Variables
Check the startup log by `docker compose -f ./compose.yaml logs`. The warning messages print out the variables if they are **NOT** set. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5e73e8c2-545c-4ce9-b01a-c10e17103fd0 | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/gaudi.md | unknown | 2d6727be-f702-4d84-a699-49be5fb4af52 | 21 | opea-semantic-v1 | 3b17da8118d93332 | Gaudi AI Accelerator optimized image hosted in huggingface repo will be used for TGI service: [ghcr.io/huggingface/text-generation-inference:2.4.0-intel-gaudi](https://github.com/huggingface/tgi-gaudi)
### Build TTS Image | ai_ref_knowledge | OPEA Documentation | Gaudi AI Accelerator optimized image hosted in huggingface repo will be used for TGI service: [ghcr.io/huggingface/text-generation-inference:2.4.0-intel-gaudi](https://github.com/huggingface/tgi-gaudi)
### Build TTS Image | Gaudi AI Accelerator optimized image hosted in huggingface repo will be used for TGI service: [ghcr.io/huggingface/text-generation-inference:2.4.0-intel-gaudi](https://github.com/huggingface/tgi-gaudi)
### Build TTS Image | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
627a2382-eca0-468d-8bd7-edd02d16a36a | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/gaudi.md | unknown | 2d6727be-f702-4d84-a699-49be5fb4af52 | 37 | opea-semantic-v1 | e74f2c1bbb24181c | string. WARN[0000] The "no_proxy" variable is not set. Defaulting to a blank string. WARN[0000] The "no_proxy" variable is not set. Defaulting to a blank string.
#### Check the container status | ai_ref_knowledge | OPEA Documentation | string. WARN[0000] The "no_proxy" variable is not set. Defaulting to a blank string. WARN[0000] The "no_proxy" variable is not set. Defaulting to a blank string.
#### Check the container status | string. WARN[0000] The "no_proxy" variable is not set. Defaulting to a blank string. WARN[0000] The "no_proxy" variable is not set. Defaulting to a blank string.
#### Check the container status | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
655322c0-60af-4958-814f-c26359362ca1 | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/gaudi.md | unknown | 2d6727be-f702-4d84-a699-49be5fb4af52 | 13 | opea-semantic-v1 | 4ba5f3bd6d4bc714 | Make sure to setup Proxies if you are behind a firewall ```bash export no_proxy=${your_no_proxy},$host_ip export http_proxy=${your_http_proxy} export https_proxy=${your_http_proxy}
## Prepare (Building / Pulling) Docker images | ai_ref_knowledge | OPEA Documentation | Make sure to setup Proxies if you are behind a firewall ```bash export no_proxy=${your_no_proxy},$host_ip export http_proxy=${your_http_proxy} export https_proxy=${your_http_proxy}
## Prepare (Building / Pulling) Docker images | Make sure to setup Proxies if you are behind a firewall ```bash export no_proxy=${your_no_proxy},$host_ip export http_proxy=${your_http_proxy} export https_proxy=${your_http_proxy}
## Prepare (Building / Pulling) Docker images | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6798a3bd-4a3b-4a94-90de-36d8831bbaa3 | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/gaudi.md | unknown | 2d6727be-f702-4d84-a699-49be5fb4af52 | 30 | opea-semantic-v1 | 868ae25c9515e755 | script. Here is where the environment variable `LLM_MODEL_ID` is set, and you can change it to another model by specifying the HuggingFace model card ID.
Run the `set_env.sh` script. ```bash
cd $WORKSPACE/GenAIExamples/AudioQnA/docker_compose
source ./set_env.sh | ai_ref_knowledge | OPEA Documentation | script. Here is where the environment variable `LLM_MODEL_ID` is set, and you can change it to another model by specifying the HuggingFace model card ID.
Run the `set_env.sh` script. ```bash
cd $WORKSPACE/GenAIExamples/AudioQnA/docker_compose
source ./set_env.sh | script. Here is where the environment variable `LLM_MODEL_ID` is set, and you can change it to another model by specifying the HuggingFace model card ID.
Run the `set_env.sh` script. ```bash
cd $WORKSPACE/GenAIExamples/AudioQnA/docker_compose
source ./set_env.sh | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6b19bb3f-0df2-490f-8df7-5e6222584ad1 | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/gaudi.md | unknown | 2d6727be-f702-4d84-a699-49be5fb4af52 | 49 | opea-semantic-v1 | fe69d3ae999c59a3 | ### Speecht5 Service
```bash
# speecht5 service
curl http://${host_ip}:7055/v1/audio/speech -XPOST -d '{"input": "Who are you?"}' -H 'Content-Type: application/json' --output speech.mp3 | ai_ref_knowledge | OPEA Documentation | ### Speecht5 Service
```bash
# speecht5 service
curl http://${host_ip}:7055/v1/audio/speech -XPOST -d '{"input": "Who are you?"}' -H 'Content-Type: application/json' --output speech.mp3 | ### Speecht5 Service
```bash
# speecht5 service
curl http://${host_ip}:7055/v1/audio/speech -XPOST -d '{"input": "Who are you?"}' -H 'Content-Type: application/json' --output speech.mp3 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7890dc11-2fdc-4ad9-bc01-e8c3df3a87c0 | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/gaudi.md | unknown | 2d6727be-f702-4d84-a699-49be5fb4af52 | 5 | opea-semantic-v1 | 6abdb640ea9cef11 | to setup docker container to start a microservices and megaservice . The solution will then utilize a sample audio file which is in waw format.
## Prerequisites | ai_ref_knowledge | OPEA Documentation | to setup docker container to start a microservices and megaservice . The solution will then utilize a sample audio file which is in waw format.
## Prerequisites | to setup docker container to start a microservices and megaservice . The solution will then utilize a sample audio file which is in waw format.
## Prerequisites | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7f836ec8-eab6-4b89-8f0c-b1704a65489e | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/gaudi.md | unknown | 2d6727be-f702-4d84-a699-49be5fb4af52 | 12 | opea-semantic-v1 | f46ef7b55396bb62 | export BACKEND_SERVICE_ENDPOINT=http://${host_ip}:3008/v1/audioqna
Make sure to setup Proxies if you are behind a firewall
```bash
export no_proxy=${your_no_proxy},$host_ip
export http_proxy=${your_http_proxy}
export https_proxy=${your_http_proxy} | ai_ref_knowledge | OPEA Documentation | export BACKEND_SERVICE_ENDPOINT=http://${host_ip}:3008/v1/audioqna
Make sure to setup Proxies if you are behind a firewall
```bash
export no_proxy=${your_no_proxy},$host_ip
export http_proxy=${your_http_proxy}
export https_proxy=${your_http_proxy} | export BACKEND_SERVICE_ENDPOINT=http://${host_ip}:3008/v1/audioqna
Make sure to setup Proxies if you are behind a firewall
```bash
export no_proxy=${your_no_proxy},$host_ip
export http_proxy=${your_http_proxy}
export https_proxy=${your_http_proxy} | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
832ebe2c-3a00-46d7-a455-26661f42c639 | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/gaudi.md | unknown | 2d6727be-f702-4d84-a699-49be5fb4af52 | 26 | opea-semantic-v1 | fcd4855199e5fbc7 | Before proceeding, verify that you have all required Docker images by running `docker images`. You should see the following images:
* opea/whisper:${RELEASE_VERSION}
* opea/speecht5:${RELEASE_VERSION}
* opea/audioqna:${RELEASE_VERSION} | ai_ref_knowledge | OPEA Documentation | Before proceeding, verify that you have all required Docker images by running `docker images`. You should see the following images:
* opea/whisper:${RELEASE_VERSION}
* opea/speecht5:${RELEASE_VERSION}
* opea/audioqna:${RELEASE_VERSION} | Before proceeding, verify that you have all required Docker images by running `docker images`. You should see the following images:
* opea/whisper:${RELEASE_VERSION}
* opea/speecht5:${RELEASE_VERSION}
* opea/audioqna:${RELEASE_VERSION} | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
83fcac2b-582d-4c45-adb6-87aa5be3a554 | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/gaudi.md | unknown | 2d6727be-f702-4d84-a699-49be5fb4af52 | 43 | opea-semantic-v1 | c04658694323ac3b | ### Whisper Service
```bash
# whisper service
wget https://github.com/intel/intel-extension-for-transformers/raw/main/intel_extension_for_transformers/neural_chat/assets/audio/sample.wav
curl http://${host_ip}:7066/v1/audio/transcriptions \
-H "Content-Type: multipart/form-data" \
-F file="@./sample.wav" \
-F model=... | ai_ref_knowledge | OPEA Documentation | ### Whisper Service
```bash
# whisper service
wget https://github.com/intel/intel-extension-for-transformers/raw/main/intel_extension_for_transformers/neural_chat/assets/audio/sample.wav
curl http://${host_ip}:7066/v1/audio/transcriptions \
-H "Content-Type: multipart/form-data" \
-F file="@./sample.wav" \
-F model=... | ### Whisper Service
```bash
# whisper service
wget https://github.com/intel/intel-extension-for-transformers/raw/main/intel_extension_for_transformers/neural_chat/assets/audio/sample.wav
curl http://${host_ip}:7066/v1/audio/transcriptions \
-H "Content-Type: multipart/form-data" \
-F file="@./sample.wav" \
-F model=... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
856b8c93-16d9-407c-8cc6-037566a89537 | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/gaudi.md | unknown | 2d6727be-f702-4d84-a699-49be5fb4af52 | 38 | opea-semantic-v1 | 6ac06f965ca66f26 | #### Check the container status
Check if all the containers launched via docker compose have started. For example, the AudioQnA example starts 5 docker containers (services), check these docker containers are all running, i.e., all the containers `STATUS` are `Up`. | ai_ref_knowledge | OPEA Documentation | #### Check the container status
Check if all the containers launched via docker compose have started. For example, the AudioQnA example starts 5 docker containers (services), check these docker containers are all running, i.e., all the containers `STATUS` are `Up`. | #### Check the container status
Check if all the containers launched via docker compose have started. For example, the AudioQnA example starts 5 docker containers (services), check these docker containers are all running, i.e., all the containers `STATUS` are `Up`. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
86d61325-1b5d-47a2-bd03-f98d9d0cddca | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/gaudi.md | unknown | 2d6727be-f702-4d84-a699-49be5fb4af52 | 11 | opea-semantic-v1 | c61c65e868b5f2c9 | The example requires you to set the the following variables to deploy the microservices on endpoint enabled with ports.
```bash
export host_ip=$(hostname -I | awk '{print $1}')
export HUGGINGFACEHUB_API_TOKEN=<your HF token> | ai_ref_knowledge | OPEA Documentation | The example requires you to set the the following variables to deploy the microservices on endpoint enabled with ports.
```bash
export host_ip=$(hostname -I | awk '{print $1}')
export HUGGINGFACEHUB_API_TOKEN=<your HF token> | The example requires you to set the the following variables to deploy the microservices on endpoint enabled with ports.
```bash
export host_ip=$(hostname -I | awk '{print $1}')
export HUGGINGFACEHUB_API_TOKEN=<your HF token> | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8b03ee7b-9e4a-4e7b-9c61-0e5ec594b65d | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/gaudi.md | unknown | 2d6727be-f702-4d84-a699-49be5fb4af52 | 48 | opea-semantic-v1 | 91ff1a8edf69ab49 | TGI service handles the core language model operations. Here is the expected result from TGI:
```bash
{"generated_text":"\n\nDeep learning is a subset of machine learning and broadly defined as techniques to"} | ai_ref_knowledge | OPEA Documentation | TGI service handles the core language model operations. Here is the expected result from TGI:
```bash
{"generated_text":"\n\nDeep learning is a subset of machine learning and broadly defined as techniques to"} | TGI service handles the core language model operations. Here is the expected result from TGI:
```bash
{"generated_text":"\n\nDeep learning is a subset of machine learning and broadly defined as techniques to"} | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8c33eb61-3013-46a7-bb47-94290081d6ef | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/gaudi.md | unknown | 2d6727be-f702-4d84-a699-49be5fb4af52 | 27 | opea-semantic-v1 | 4f572b499e383ad5 | The use case will use the following combination of the GenAIComps with the tools.
| Use Case Components | Tools | Model | Service Type |
|---------------------|---------------|--------------------------------------|----------------------|
| LLM | TGI | Intel/neural-chat-7b-v3-3 | OPEA Microservice |
| ASR | | NA | OPEA... | ai_ref_knowledge | OPEA Documentation | The use case will use the following combination of the GenAIComps with the tools.
| Use Case Components | Tools | Model | Service Type |
|---------------------|---------------|--------------------------------------|----------------------|
| LLM | TGI | Intel/neural-chat-7b-v3-3 | OPEA Microservice |
| ASR | | NA | OPEA... | The use case will use the following combination of the GenAIComps with the tools.
| Use Case Components | Tools | Model | Service Type |
|---------------------|---------------|--------------------------------------|----------------------|
| LLM | TGI | Intel/neural-chat-7b-v3-3 | OPEA Microservice |
| ASR | | NA | OPEA... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8f52b1c2-673f-4640-b025-79ee10d54d83 | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/gaudi.md | unknown | 2d6727be-f702-4d84-a699-49be5fb4af52 | 24 | opea-semantic-v1 | 360f74a4d8cab69a | Build the megaservice image for this use case.
```bash
cd $WORKSPACE/GenAIExamples/AudioQnA/
docker build --no-cache -t opea/audioqna:${RELEASE_VERSION} --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile . | ai_ref_knowledge | OPEA Documentation | Build the megaservice image for this use case.
```bash
cd $WORKSPACE/GenAIExamples/AudioQnA/
docker build --no-cache -t opea/audioqna:${RELEASE_VERSION} --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile . | Build the megaservice image for this use case.
```bash
cd $WORKSPACE/GenAIExamples/AudioQnA/
docker build --no-cache -t opea/audioqna:${RELEASE_VERSION} --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile . | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
91a28fe3-8696-455c-9b03-23739bbb5fbf | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/gaudi.md | unknown | 2d6727be-f702-4d84-a699-49be5fb4af52 | 4 | opea-semantic-v1 | 6306bb9c6cbb3289 | 1. Automatic Speech Recognition (ASR) Service 2. Large Language Models (LLM) Service 3. Text-to-Speech (TTS) Service
The solution is aimed to show how to use ASR, TGI and TTS on Gaudi AI Accelerator. We will go through how to setup docker container to start a microservices and megaservice . The solution will then utili... | ai_ref_knowledge | OPEA Documentation | 1. Automatic Speech Recognition (ASR) Service 2. Large Language Models (LLM) Service 3. Text-to-Speech (TTS) Service
The solution is aimed to show how to use ASR, TGI and TTS on Gaudi AI Accelerator. We will go through how to setup docker container to start a microservices and megaservice . The solution will then utili... | 1. Automatic Speech Recognition (ASR) Service 2. Large Language Models (LLM) Service 3. Text-to-Speech (TTS) Service
The solution is aimed to show how to use ASR, TGI and TTS on Gaudi AI Accelerator. We will go through how to setup docker container to start a microservices and megaservice . The solution will then utili... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
9b24a6f7-cdaa-4c28-8685-0b0360893b0f | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/gaudi.md | unknown | 2d6727be-f702-4d84-a699-49be5fb4af52 | 20 | opea-semantic-v1 | 80860083cb399628 | ### Build LLM Image
Gaudi AI Accelerator optimized image hosted in huggingface repo will be used for TGI service: [ghcr.io/huggingface/text-generation-inference:2.4.0-intel-gaudi](https://github.com/huggingface/tgi-gaudi) | ai_ref_knowledge | OPEA Documentation | ### Build LLM Image
Gaudi AI Accelerator optimized image hosted in huggingface repo will be used for TGI service: [ghcr.io/huggingface/text-generation-inference:2.4.0-intel-gaudi](https://github.com/huggingface/tgi-gaudi) | ### Build LLM Image
Gaudi AI Accelerator optimized image hosted in huggingface repo will be used for TGI service: [ghcr.io/huggingface/text-generation-inference:2.4.0-intel-gaudi](https://github.com/huggingface/tgi-gaudi) | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a1b92037-e3a5-4399-8630-3f0903bd07ca | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/gaudi.md | unknown | 2d6727be-f702-4d84-a699-49be5fb4af52 | 55 | opea-semantic-v1 | 9b699d8e1a66c232 | Once you are done with the entire pipeline and wish to stop and remove all the containers, use the command below:
```bash
docker compose -f compose.yaml down | ai_ref_knowledge | OPEA Documentation | Once you are done with the entire pipeline and wish to stop and remove all the containers, use the command below:
```bash
docker compose -f compose.yaml down | Once you are done with the entire pipeline and wish to stop and remove all the containers, use the command below:
```bash
docker compose -f compose.yaml down | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a5fb605f-a911-49fe-b1ef-fb9c1c550ab8 | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/gaudi.md | unknown | 2d6727be-f702-4d84-a699-49be5fb4af52 | 7 | opea-semantic-v1 | 7ec97740ca498778 | Set an environment variable for the desired release version with the number only (i.e. 1.0, 1.1, etc) and checkout using the tag with that version.
```bash
# Set workspace
export WORKSPACE=<path>
cd $WORKSPACE | ai_ref_knowledge | OPEA Documentation | Set an environment variable for the desired release version with the number only (i.e. 1.0, 1.1, etc) and checkout using the tag with that version.
```bash
# Set workspace
export WORKSPACE=<path>
cd $WORKSPACE | Set an environment variable for the desired release version with the number only (i.e. 1.0, 1.1, etc) and checkout using the tag with that version.
```bash
# Set workspace
export WORKSPACE=<path>
cd $WORKSPACE | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
aa607730-ca11-4238-9968-7e18c702096d | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/gaudi.md | unknown | 2d6727be-f702-4d84-a699-49be5fb4af52 | 22 | opea-semantic-v1 | cc1985aeeff20ca1 | ### Build MegaService Image
The Megaservice is a pipeline that channels data through different microservices, each performing varied tasks. We define the different microservices and the flow of data between them in the `audioqna.py` file. | ai_ref_knowledge | OPEA Documentation | ### Build MegaService Image
The Megaservice is a pipeline that channels data through different microservices, each performing varied tasks. We define the different microservices and the flow of data between them in the `audioqna.py` file. | ### Build MegaService Image
The Megaservice is a pipeline that channels data through different microservices, each performing varied tasks. We define the different microservices and the flow of data between them in the `audioqna.py` file. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ae21222e-39b3-49dc-9e72-f029f217e02e | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/gaudi.md | unknown | 2d6727be-f702-4d84-a699-49be5fb4af52 | 10 | opea-semantic-v1 | 6af96c1f45cedcef | # GenAIExamples git clone https://github.com/opea-project/GenAIExamples.git cd GenAIExamples git checkout tags/v${RELEASE_VERSION} cd ..
The example requires you to set the the following variables to deploy the microservices on
endpoint enabled with ports. | ai_ref_knowledge | OPEA Documentation | # GenAIExamples git clone https://github.com/opea-project/GenAIExamples.git cd GenAIExamples git checkout tags/v${RELEASE_VERSION} cd ..
The example requires you to set the the following variables to deploy the microservices on
endpoint enabled with ports. | # GenAIExamples git clone https://github.com/opea-project/GenAIExamples.git cd GenAIExamples git checkout tags/v${RELEASE_VERSION} cd ..
The example requires you to set the the following variables to deploy the microservices on
endpoint enabled with ports. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
afde04fd-a466-44a0-b863-ccd7e9669900 | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/gaudi.md | unknown | 2d6727be-f702-4d84-a699-49be5fb4af52 | 8 | opea-semantic-v1 | b96df5e102af40ef | # Set desired release version - number only export RELEASE_VERSION=<insert-release-version>
# GenAIComps
git clone https://github.com/opea-project/GenAIComps.git
cd GenAIComps
git checkout tags/v${RELEASE_VERSION}
cd .. | ai_ref_knowledge | OPEA Documentation | # Set desired release version - number only export RELEASE_VERSION=<insert-release-version>
# GenAIComps
git clone https://github.com/opea-project/GenAIComps.git
cd GenAIComps
git checkout tags/v${RELEASE_VERSION}
cd .. | # Set desired release version - number only export RELEASE_VERSION=<insert-release-version>
# GenAIComps
git clone https://github.com/opea-project/GenAIComps.git
cd GenAIComps
git checkout tags/v${RELEASE_VERSION}
cd .. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b1caf913-03a6-4718-9334-b4b62dcd99f8 | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/gaudi.md | unknown | 2d6727be-f702-4d84-a699-49be5fb4af52 | 23 | opea-semantic-v1 | e1ebfb4c60b9ec91 | channels data through different microservices, each performing varied tasks. We define the different microservices and the flow of data between them in the `audioqna.py` file.
Build the megaservice image for this use case. | ai_ref_knowledge | OPEA Documentation | channels data through different microservices, each performing varied tasks. We define the different microservices and the flow of data between them in the `audioqna.py` file.
Build the megaservice image for this use case. | channels data through different microservices, each performing varied tasks. We define the different microservices and the flow of data between them in the `audioqna.py` file.
Build the megaservice image for this use case. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b6d38cd9-5531-4068-9e33-748a11b933c2 | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/gaudi.md | unknown | 2d6727be-f702-4d84-a699-49be5fb4af52 | 36 | opea-semantic-v1 | 4678e00baad4be23 | Check the startup log by `docker compose -f ./compose.yaml logs`. The warning messages print out the variables if they are **NOT** set.
```bash
GenAIExamples/AudioQnA/docker_compose/intel/hpu/gaudi/$ sudo -E docker compose -f ./compose.yaml logs
WARN[0000] The "no_proxy" variable is not set. Defaulting to a blank strin... | ai_ref_knowledge | OPEA Documentation | Check the startup log by `docker compose -f ./compose.yaml logs`. The warning messages print out the variables if they are **NOT** set.
```bash
GenAIExamples/AudioQnA/docker_compose/intel/hpu/gaudi/$ sudo -E docker compose -f ./compose.yaml logs
WARN[0000] The "no_proxy" variable is not set. Defaulting to a blank strin... | Check the startup log by `docker compose -f ./compose.yaml logs`. The warning messages print out the variables if they are **NOT** set.
```bash
GenAIExamples/AudioQnA/docker_compose/intel/hpu/gaudi/$ sudo -E docker compose -f ./compose.yaml logs
WARN[0000] The "no_proxy" variable is not set. Defaulting to a blank strin... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c50c8a14-5339-44ae-99de-5621f7e043b6 | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/gaudi.md | unknown | 2d6727be-f702-4d84-a699-49be5fb4af52 | 51 | opea-semantic-v1 | f085b70dc19ebb16 | Speecht5 service generates an audio file from the given sentense. The expected outputs is an audio file that says "Who are you?".
### MegaService | ai_ref_knowledge | OPEA Documentation | Speecht5 service generates an audio file from the given sentense. The expected outputs is an audio file that says "Who are you?".
### MegaService | Speecht5 service generates an audio file from the given sentense. The expected outputs is an audio file that says "Who are you?".
### MegaService | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c8922399-aab4-46a8-b95c-df24d6416b68 | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/gaudi.md | unknown | 2d6727be-f702-4d84-a699-49be5fb4af52 | 0 | opea-semantic-v1 | bad642706e9a2d93 | # Single node on-prem deployment with TGI on Gaudi AI Accelerator
This deployment section covers single-node on-prem deployment of the AudioQnA example with OPEA comps to deploy using TGI service. The solution demonstrates building an voice chat service using the TGI deployed on Intel® Gaudi® AI Accelerator. To quickly... | ai_ref_knowledge | OPEA Documentation | # Single node on-prem deployment with TGI on Gaudi AI Accelerator
This deployment section covers single-node on-prem deployment of the AudioQnA example with OPEA comps to deploy using TGI service. The solution demonstrates building an voice chat service using the TGI deployed on Intel® Gaudi® AI Accelerator. To quickly... | # Single node on-prem deployment with TGI on Gaudi AI Accelerator
This deployment section covers single-node on-prem deployment of the AudioQnA example with OPEA comps to deploy using TGI service. The solution demonstrates building an voice chat service using the TGI deployed on Intel® Gaudi® AI Accelerator. To quickly... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ce248b3a-5fc7-475b-a16a-108cfda1bbcb | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/gaudi.md | unknown | 2d6727be-f702-4d84-a699-49be5fb4af52 | 45 | opea-semantic-v1 | c06aa5505227b0d2 | Whisper service generates text for the input audio file. Here is the expected result from Whisper:
```bash
{"text":"who is pat gelsinger"} | ai_ref_knowledge | OPEA Documentation | Whisper service generates text for the input audio file. Here is the expected result from Whisper:
```bash
{"text":"who is pat gelsinger"} | Whisper service generates text for the input audio file. Here is the expected result from Whisper:
```bash
{"text":"who is pat gelsinger"} | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d3897b1c-97ba-4707-a137-92686dd98fdf | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/gaudi.md | unknown | 2d6727be-f702-4d84-a699-49be5fb4af52 | 50 | opea-semantic-v1 | e6eccc4facb804bc | ```bash # speecht5 service curl http://${host_ip}:7055/v1/audio/speech -XPOST -d '{"input": "Who are you?"}' -H 'Content-Type: application/json' --output speech.mp3
Speecht5 service generates an audio file from the given sentense. The expected outputs is an audio file that says "Who are you?". | ai_ref_knowledge | OPEA Documentation | ```bash # speecht5 service curl http://${host_ip}:7055/v1/audio/speech -XPOST -d '{"input": "Who are you?"}' -H 'Content-Type: application/json' --output speech.mp3
Speecht5 service generates an audio file from the given sentense. The expected outputs is an audio file that says "Who are you?". | ```bash # speecht5 service curl http://${host_ip}:7055/v1/audio/speech -XPOST -d '{"input": "Who are you?"}' -H 'Content-Type: application/json' --output speech.mp3
Speecht5 service generates an audio file from the given sentense. The expected outputs is an audio file that says "Who are you?". | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e397e9ec-c285-4a78-9a77-fabf2156f538 | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/gaudi.md | unknown | 2d6727be-f702-4d84-a699-49be5fb4af52 | 52 | opea-semantic-v1 | 1ff30e3ba846ebba | The AudioQnA megaservice orchestrates the entire conversation process. Test it with a empty audio:
```bash
# if you are using speecht5 as the tts service, voice can be "default" or "male"
# if you are using gpt-sovits for the tts service, you can set the reference audio following https://github.com/opea-project/GenAICo... | ai_ref_knowledge | OPEA Documentation | The AudioQnA megaservice orchestrates the entire conversation process. Test it with a empty audio:
```bash
# if you are using speecht5 as the tts service, voice can be "default" or "male"
# if you are using gpt-sovits for the tts service, you can set the reference audio following https://github.com/opea-project/GenAICo... | The AudioQnA megaservice orchestrates the entire conversation process. Test it with a empty audio:
```bash
# if you are using speecht5 as the tts service, voice can be "default" or "male"
# if you are using gpt-sovits for the tts service, you can set the reference audio following https://github.com/opea-project/GenAICo... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e7e8e8f0-7577-41c3-92e3-6f037d3887f3 | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/gaudi.md | unknown | 2d6727be-f702-4d84-a699-49be5fb4af52 | 14 | opea-semantic-v1 | 699d4aaf3b8a023b | ## Prepare (Building / Pulling) Docker images
This step involves either building or pulling three required Docker images. Each image serves a specific purpose in the AudioQnA architecture. | ai_ref_knowledge | OPEA Documentation | ## Prepare (Building / Pulling) Docker images
This step involves either building or pulling three required Docker images. Each image serves a specific purpose in the AudioQnA architecture. | ## Prepare (Building / Pulling) Docker images
This step involves either building or pulling three required Docker images. Each image serves a specific purpose in the AudioQnA architecture. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ef92ed02-b070-4d76-9125-1946a491963a | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/gaudi.md | unknown | 2d6727be-f702-4d84-a699-49be5fb4af52 | 2 | opea-semantic-v1 | 304e6438ff7aa919 | ## Overview
There are several ways to setup a AudioQnA use case. Here in this tutorial, we will walk through how to enable the below list of microservices from OPEA GenAIComps to deploy a single node TGI megaservice solution. | ai_ref_knowledge | OPEA Documentation | ## Overview
There are several ways to setup a AudioQnA use case. Here in this tutorial, we will walk through how to enable the below list of microservices from OPEA GenAIComps to deploy a single node TGI megaservice solution. | ## Overview
There are several ways to setup a AudioQnA use case. Here in this tutorial, we will walk through how to enable the below list of microservices from OPEA GenAIComps to deploy a single node TGI megaservice solution. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f3e58bc7-9ee0-40f1-93ef-366a18c84698 | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/gaudi.md | unknown | 2d6727be-f702-4d84-a699-49be5fb4af52 | 1 | opea-semantic-v1 | ecd7e5452020b03e | quickly learn about OPEA in just 5 minutes and set up the required hardware and software, please follow the instructions in the [Getting Started](../../../getting-started/README.md) section.
## Overview | ai_ref_knowledge | OPEA Documentation | quickly learn about OPEA in just 5 minutes and set up the required hardware and software, please follow the instructions in the [Getting Started](../../../getting-started/README.md) section.
## Overview | quickly learn about OPEA in just 5 minutes and set up the required hardware and software, please follow the instructions in the [Getting Started](../../../getting-started/README.md) section.
## Overview | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f6107a52-b393-45ae-a254-6e9fdf4c7415 | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/gaudi.md | unknown | 2d6727be-f702-4d84-a699-49be5fb4af52 | 25 | opea-semantic-v1 | fa16796f8d5b8adb | ### Sanity Check
Before proceeding, verify that you have all required Docker images by running `docker images`. You should see the following images: | ai_ref_knowledge | OPEA Documentation | ### Sanity Check
Before proceeding, verify that you have all required Docker images by running `docker images`. You should see the following images: | ### Sanity Check
Before proceeding, verify that you have all required Docker images by running `docker images`. You should see the following images: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f9864550-a9d5-4588-aaee-e3dd60472796 | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/gaudi.md | unknown | 2d6727be-f702-4d84-a699-49be5fb4af52 | 33 | opea-semantic-v1 | fee398dca2d7a764 | # multilang tts (optional) docker compose -f compose_multilang.yaml up -d
Note: add the following environment variables in compose yaml if meet issues for downloading models:
```bash
HF_ENDPOINT: https://hf-mirror.com
HF_HUB_ENABLE_HF_TRANSFER: false | ai_ref_knowledge | OPEA Documentation | # multilang tts (optional) docker compose -f compose_multilang.yaml up -d
Note: add the following environment variables in compose yaml if meet issues for downloading models:
```bash
HF_ENDPOINT: https://hf-mirror.com
HF_HUB_ENABLE_HF_TRANSFER: false | # multilang tts (optional) docker compose -f compose_multilang.yaml up -d
Note: add the following environment variables in compose yaml if meet issues for downloading models:
```bash
HF_ENDPOINT: https://hf-mirror.com
HF_HUB_ENABLE_HF_TRANSFER: false | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
01a8dfeb-5f32-44ee-8cc2-5bc26177638f | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/xeon.md | unknown | 1c8c5597-cc9f-4c0a-b57d-77dce0b68d41 | 2 | opea-semantic-v1 | d16dfa11c8c5117f | ## Prepare (Building / Pulling) Docker images
This step involves either building or pulling four required Docker images. Each image serves a specific purpose in the AudioQnA architecture. | ai_ref_knowledge | OPEA Documentation | ## Prepare (Building / Pulling) Docker images
This step involves either building or pulling four required Docker images. Each image serves a specific purpose in the AudioQnA architecture. | ## Prepare (Building / Pulling) Docker images
This step involves either building or pulling four required Docker images. Each image serves a specific purpose in the AudioQnA architecture. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
097f6425-effc-4d5f-93c2-cb7856be9fdc | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/xeon.md | unknown | 1c8c5597-cc9f-4c0a-b57d-77dce0b68d41 | 8 | opea-semantic-v1 | 1a176daf11671a7b | [::]:7066->7066/tcp | whisper-service | | 1290ccd09182 | opea/speecht5:${RELEASE_VERSION} | `"python speecht5_ser…"` | 37 minutes ago | Up 37 minutes | 0.0.0.0:7055->7055/tcp, [::]:7055->7055/tcp | speecht5-service |
## Interacting with AudioQnA deployment | ai_ref_knowledge | OPEA Documentation | [::]:7066->7066/tcp | whisper-service | | 1290ccd09182 | opea/speecht5:${RELEASE_VERSION} | `"python speecht5_ser…"` | 37 minutes ago | Up 37 minutes | 0.0.0.0:7055->7055/tcp, [::]:7055->7055/tcp | speecht5-service |
## Interacting with AudioQnA deployment | [::]:7066->7066/tcp | whisper-service | | 1290ccd09182 | opea/speecht5:${RELEASE_VERSION} | `"python speecht5_ser…"` | 37 minutes ago | Up 37 minutes | 0.0.0.0:7055->7055/tcp, [::]:7055->7055/tcp | speecht5-service |
## Interacting with AudioQnA deployment | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
120f5cc2-3ef4-4675-82e5-0fe9833e8428 | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/xeon.md | unknown | 1c8c5597-cc9f-4c0a-b57d-77dce0b68d41 | 3 | opea-semantic-v1 | fd8e4c66eafe6dec | This step involves either building or pulling four required Docker images. Each image serves a specific purpose in the AudioQnA architecture.
::::::{tab-set} | ai_ref_knowledge | OPEA Documentation | This step involves either building or pulling four required Docker images. Each image serves a specific purpose in the AudioQnA architecture.
::::::{tab-set} | This step involves either building or pulling four required Docker images. Each image serves a specific purpose in the AudioQnA architecture.
::::::{tab-set} | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
435238c1-ed3b-4f06-9d49-f447b48dcd1b | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/xeon.md | unknown | 1c8c5597-cc9f-4c0a-b57d-77dce0b68d41 | 7 | opea-semantic-v1 | 86370a62773b59e3 | To do a quick sanity check, try `docker ps -a` to see if all the containers are running.
```bash
| CONTAINER ID | IMAGE | COMMAND | CREATED | STATUS | PORTS | NAMES |
|--------------|-------------------------------------------------------------------|---------------------------|----------------|------------------------... | ai_ref_knowledge | OPEA Documentation | To do a quick sanity check, try `docker ps -a` to see if all the containers are running.
```bash
| CONTAINER ID | IMAGE | COMMAND | CREATED | STATUS | PORTS | NAMES |
|--------------|-------------------------------------------------------------------|---------------------------|----------------|------------------------... | To do a quick sanity check, try `docker ps -a` to see if all the containers are running.
```bash
| CONTAINER ID | IMAGE | COMMAND | CREATED | STATUS | PORTS | NAMES |
|--------------|-------------------------------------------------------------------|---------------------------|----------------|------------------------... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
58e283db-182b-46a1-81f7-ea1b6c996bc3 | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/xeon.md | unknown | 1c8c5597-cc9f-4c0a-b57d-77dce0b68d41 | 6 | opea-semantic-v1 | fc4731ecee4ad760 | Check the startup log by `docker compose -f ./compose.yaml logs`. The warning messages print out the variables if they are **NOT** set.
```bash
GenAIExamples/AudioQnA/docker_compose/intel/cpu/xeon$ sudo -E docker compose -f ./compose.yaml logs
WARN[0000] The "no_proxy" variable is not set. Defaulting to a blank string.... | ai_ref_knowledge | OPEA Documentation | Check the startup log by `docker compose -f ./compose.yaml logs`. The warning messages print out the variables if they are **NOT** set.
```bash
GenAIExamples/AudioQnA/docker_compose/intel/cpu/xeon$ sudo -E docker compose -f ./compose.yaml logs
WARN[0000] The "no_proxy" variable is not set. Defaulting to a blank string.... | Check the startup log by `docker compose -f ./compose.yaml logs`. The warning messages print out the variables if they are **NOT** set.
```bash
GenAIExamples/AudioQnA/docker_compose/intel/cpu/xeon$ sudo -E docker compose -f ./compose.yaml logs
WARN[0000] The "no_proxy" variable is not set. Defaulting to a blank string.... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
80c2b9b6-16df-495d-b9f1-4c39d7c9adab | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/xeon.md | unknown | 1c8c5597-cc9f-4c0a-b57d-77dce0b68d41 | 1 | opea-semantic-v1 | dbc26cc57001876e | 1. Automatic Speech Recognition (ASR) Service 2. Large Language Models (LLM) Service 3. Text-to-Speech (TTS) Service
The solution is aimed to show how to use ASR, TGI and TTS on Intel Xeon Scalable processors. We will go through how to setup docker container to start a microservices and megaservice . The solution will ... | ai_ref_knowledge | OPEA Documentation | 1. Automatic Speech Recognition (ASR) Service 2. Large Language Models (LLM) Service 3. Text-to-Speech (TTS) Service
The solution is aimed to show how to use ASR, TGI and TTS on Intel Xeon Scalable processors. We will go through how to setup docker container to start a microservices and megaservice . The solution will ... | 1. Automatic Speech Recognition (ASR) Service 2. Large Language Models (LLM) Service 3. Text-to-Speech (TTS) Service
The solution is aimed to show how to use ASR, TGI and TTS on Intel Xeon Scalable processors. We will go through how to setup docker container to start a microservices and megaservice . The solution will ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
cb961957-664f-4e29-86a0-ff1352754b0c | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/xeon.md | unknown | 1c8c5597-cc9f-4c0a-b57d-77dce0b68d41 | 5 | opea-semantic-v1 | 7d41389a085f02b5 | Before proceeding, verify that you have all required Docker images by running `docker images`. You should see the following images:
* opea/whisper:${RELEASE_VERSION}
* opea/speecht5:${RELEASE_VERSION}
* opea/audioqna:${RELEASE_VERSION}
* opea/gpt-sovits:${RELEASE_VERSION} (optional) | ai_ref_knowledge | OPEA Documentation | Before proceeding, verify that you have all required Docker images by running `docker images`. You should see the following images:
* opea/whisper:${RELEASE_VERSION}
* opea/speecht5:${RELEASE_VERSION}
* opea/audioqna:${RELEASE_VERSION}
* opea/gpt-sovits:${RELEASE_VERSION} (optional) | Before proceeding, verify that you have all required Docker images by running `docker images`. You should see the following images:
* opea/whisper:${RELEASE_VERSION}
* opea/speecht5:${RELEASE_VERSION}
* opea/audioqna:${RELEASE_VERSION}
* opea/gpt-sovits:${RELEASE_VERSION} (optional) | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d6856db2-5880-4477-bfff-7e0d06a03a36 | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/xeon.md | unknown | 1c8c5597-cc9f-4c0a-b57d-77dce0b68d41 | 0 | opea-semantic-v1 | 6810f925c296291a | # Single node on-prem deployment with TGI on Xeon Scalable processors
This deployment section covers single-node on-prem deployment of the AudioQnA example with OPEA comps to deploy using TGI service. The solution demonstrates building a voice chat service using the TGI deployed on Intel® Xeon® Scalable processors. To ... | ai_ref_knowledge | OPEA Documentation | # Single node on-prem deployment with TGI on Xeon Scalable processors
This deployment section covers single-node on-prem deployment of the AudioQnA example with OPEA comps to deploy using TGI service. The solution demonstrates building a voice chat service using the TGI deployed on Intel® Xeon® Scalable processors. To ... | # Single node on-prem deployment with TGI on Xeon Scalable processors
This deployment section covers single-node on-prem deployment of the AudioQnA example with OPEA comps to deploy using TGI service. The solution demonstrates building a voice chat service using the TGI deployed on Intel® Xeon® Scalable processors. To ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f0167cc7-ea63-4255-9f84-19d7a3e6fabb | OPEA Documentation | file://datasets/opea-docs/tutorial/AudioQnA/deploy/xeon.md | unknown | 1c8c5597-cc9f-4c0a-b57d-77dce0b68d41 | 4 | opea-semantic-v1 | 07233e575d3c74d9 | ```bash docker build -t opea/speecht5:${RELEASE_VERSION} --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/tts/src/integrations/dependency/speecht5/Dockerfile .
# multilang tts (optional)
docker build -t opea/gpt-sovits:${RELEASE_VERSION} --build-arg http_proxy=$http_proxy --build-arg htt... | ai_ref_knowledge | OPEA Documentation | ```bash docker build -t opea/speecht5:${RELEASE_VERSION} --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/tts/src/integrations/dependency/speecht5/Dockerfile .
# multilang tts (optional)
docker build -t opea/gpt-sovits:${RELEASE_VERSION} --build-arg http_proxy=$http_proxy --build-arg htt... | ```bash docker build -t opea/speecht5:${RELEASE_VERSION} --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/tts/src/integrations/dependency/speecht5/Dockerfile .
# multilang tts (optional)
docker build -t opea/gpt-sovits:${RELEASE_VERSION} --build-arg http_proxy=$http_proxy --build-arg htt... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
014a984c-409c-470d-8801-8701a21e0ac0 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 79 | opea-semantic-v1 | 9f09b591fc38d466 | TEI_RERANKING_ENDPOINT="http://${ip_address}:8808" export TGI_LLM_ENDPOINT="http://${ip_address}:8008" export MILVUS_HOST=${ip_address} export HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN} export MEGA_SERVICE_HOST_IP=${ip_address} export EMBEDDING_SERVICE_HOST_IP=${ip_address} export RETRIEVER_SERVICE_HOST_IP=${... | ai_ref_knowledge | OPEA Documentation | TEI_RERANKING_ENDPOINT="http://${ip_address}:8808" export TGI_LLM_ENDPOINT="http://${ip_address}:8008" export MILVUS_HOST=${ip_address} export HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN} export MEGA_SERVICE_HOST_IP=${ip_address} export EMBEDDING_SERVICE_HOST_IP=${ip_address} export RETRIEVER_SERVICE_HOST_IP=${... | TEI_RERANKING_ENDPOINT="http://${ip_address}:8808" export TGI_LLM_ENDPOINT="http://${ip_address}:8008" export MILVUS_HOST=${ip_address} export HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN} export MEGA_SERVICE_HOST_IP=${ip_address} export EMBEDDING_SERVICE_HOST_IP=${ip_address} export RETRIEVER_SERVICE_HOST_IP=${... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0207de23-6ca1-4683-8796-75a05ea239c1 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 58 | opea-semantic-v1 | a911fd365372f7ac | ```bash curl ${host_ip}:6006/embed \ -X POST \ -d '{"inputs":"What is Deep Learning?"}' \ -H 'Content-Type: application/json'
#### Retriever Microservice
To consume the retriever microservice, you need to generate a mock embedding vector by Python script. The length of embedding vector is determined by the embedding m... | ai_ref_knowledge | OPEA Documentation | ```bash curl ${host_ip}:6006/embed \ -X POST \ -d '{"inputs":"What is Deep Learning?"}' \ -H 'Content-Type: application/json'
#### Retriever Microservice
To consume the retriever microservice, you need to generate a mock embedding vector by Python script. The length of embedding vector is determined by the embedding m... | ```bash curl ${host_ip}:6006/embed \ -X POST \ -d '{"inputs":"What is Deep Learning?"}' \ -H 'Content-Type: application/json'
#### Retriever Microservice
To consume the retriever microservice, you need to generate a mock embedding vector by Python script. The length of embedding vector is determined by the embedding m... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
076a8360-2805-4ffd-aeab-8b4c44bb83cc | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 39 | opea-semantic-v1 | 576ae4df19cc91ef | ```bash cd GenAIExamples/ChatQnA/ui docker build --no-cache -t opea/chatqna-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile .
#### Build Conversational React UI Docker Image (Optional)
Build frontend Docker image that enables Conversational experience with ChatQn... | ai_ref_knowledge | OPEA Documentation | ```bash cd GenAIExamples/ChatQnA/ui docker build --no-cache -t opea/chatqna-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile .
#### Build Conversational React UI Docker Image (Optional)
Build frontend Docker image that enables Conversational experience with ChatQn... | ```bash cd GenAIExamples/ChatQnA/ui docker build --no-cache -t opea/chatqna-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile .
#### Build Conversational React UI Docker Image (Optional)
Build frontend Docker image that enables Conversational experience with ChatQn... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
07cb10fa-de56-4cca-be27-0538c2d52ca1 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 22 | opea-semantic-v1 | 5f1329162d1e12ce | the aplication, please make sure either you've requested and have been granted access to it on [HuggingFace](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) or you've downloaded the model locally from [ModelScope](https://www.modelscope.cn/models).
#### Quick Start: 1. Setup Environment Variable | ai_ref_knowledge | OPEA Documentation | the aplication, please make sure either you've requested and have been granted access to it on [HuggingFace](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) or you've downloaded the model locally from [ModelScope](https://www.modelscope.cn/models).
#### Quick Start: 1. Setup Environment Variable | the aplication, please make sure either you've requested and have been granted access to it on [HuggingFace](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) or you've downloaded the model locally from [ModelScope](https://www.modelscope.cn/models).
#### Quick Start: 1. Setup Environment Variable | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
08fde042-abe5-4e44-9e60-0124e531ed50 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 19 | opea-semantic-v1 | d7e59a2e36661fea | server. The steps include Docker image creation, container deployment via Docker Compose, and service execution to integrate microservices such as `embedding`, `retriever`, `rerank`, and `llm`.
Quick Start: | ai_ref_knowledge | OPEA Documentation | server. The steps include Docker image creation, container deployment via Docker Compose, and service execution to integrate microservices such as `embedding`, `retriever`, `rerank`, and `llm`.
Quick Start: | server. The steps include Docker image creation, container deployment via Docker Compose, and service execution to integrate microservices such as `embedding`, `retriever`, `rerank`, and `llm`.
Quick Start: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0ea54a63-7380-4054-bffd-1fe29febe5e3 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 6 | opea-semantic-v1 | 3cae7ff068d2fcb3 | 1. Data Prep 2. Embedding 3. Retriever 4. Reranking 5. LLM with Ollama
To add a new VectorDB to OPEA involves minimal changes to OPEA sub-project [GenAIComps](https://github.com/opea-project/GenAIComps) that covers installation, launch, usage, and tests. The necessary customizations are covered in detail [here] | ai_ref_knowledge | OPEA Documentation | 1. Data Prep 2. Embedding 3. Retriever 4. Reranking 5. LLM with Ollama
To add a new VectorDB to OPEA involves minimal changes to OPEA sub-project [GenAIComps](https://github.com/opea-project/GenAIComps) that covers installation, launch, usage, and tests. The necessary customizations are covered in detail [here] | 1. Data Prep 2. Embedding 3. Retriever 4. Reranking 5. LLM with Ollama
To add a new VectorDB to OPEA involves minimal changes to OPEA sub-project [GenAIComps](https://github.com/opea-project/GenAIComps) that covers installation, launch, usage, and tests. The necessary customizations are covered in detail [here] | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0f98c0ae-bb6c-4c88-96f2-24b6e0526e0c | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 7 | opea-semantic-v1 | 79a50d5f13cb3931 | VectorDB to OPEA involves minimal changes to OPEA sub-project [GenAIComps](https://github.com/opea-project/GenAIComps) that covers installation, launch, usage, and tests. The necessary customizations are covered in detail [here]
### Prerequisites | ai_ref_knowledge | OPEA Documentation | VectorDB to OPEA involves minimal changes to OPEA sub-project [GenAIComps](https://github.com/opea-project/GenAIComps) that covers installation, launch, usage, and tests. The necessary customizations are covered in detail [here]
### Prerequisites | VectorDB to OPEA involves minimal changes to OPEA sub-project [GenAIComps](https://github.com/opea-project/GenAIComps) that covers installation, launch, usage, and tests. The necessary customizations are covered in detail [here]
### Prerequisites | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1049b128-5404-41cf-b972-a282f16c73f6 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 84 | opea-semantic-v1 | 667f308c362526ac | ChatQnA" docker compose -f build.yaml build ${service_list} --no-cache docker pull ghcr.io/huggingface/text-embeddings-inference:hpu-1.5 docker pull <Vector_DB> specific images docker images && sleep 1s echo "Docker images built!"
start_services()
echo "Starting Docker Services...."
export EMBEDDING_MODEL_ID="BAAI/... | ai_ref_knowledge | OPEA Documentation | ChatQnA" docker compose -f build.yaml build ${service_list} --no-cache docker pull ghcr.io/huggingface/text-embeddings-inference:hpu-1.5 docker pull <Vector_DB> specific images docker images && sleep 1s echo "Docker images built!"
start_services()
echo "Starting Docker Services...."
export EMBEDDING_MODEL_ID="BAAI/... | ChatQnA" docker compose -f build.yaml build ${service_list} --no-cache docker pull ghcr.io/huggingface/text-embeddings-inference:hpu-1.5 docker pull <Vector_DB> specific images docker images && sleep 1s echo "Docker images built!"
start_services()
echo "Starting Docker Services...."
export EMBEDDING_MODEL_ID="BAAI/... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
11a1a242-425b-42a0-bff6-9a89b0113766 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 2 | opea-semantic-v1 | 622fa09e8a8306ad | ## Add new VectorDB to ChatQnA Example
This deployment section covers how to add a new Vector DB to ChatQnA example with OPEA comps. Here we will be showcasing how to build an (end-to-end) e2e ChatQnA with a new VectorDB. | ai_ref_knowledge | OPEA Documentation | ## Add new VectorDB to ChatQnA Example
This deployment section covers how to add a new Vector DB to ChatQnA example with OPEA comps. Here we will be showcasing how to build an (end-to-end) e2e ChatQnA with a new VectorDB. | ## Add new VectorDB to ChatQnA Example
This deployment section covers how to add a new Vector DB to ChatQnA example with OPEA comps. Here we will be showcasing how to build an (end-to-end) e2e ChatQnA with a new VectorDB. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
14a150e5-878f-4aa6-b2d8-99d56828bc4d | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 26 | opea-semantic-v1 | aca9107f8c1926c6 | #### Quick Start: 2.Run Docker Compose ```bash docker compose -f compose_<Vector_DB>.yaml up -d
It will automatically download the docker image on `docker hub`: | ai_ref_knowledge | OPEA Documentation | #### Quick Start: 2.Run Docker Compose ```bash docker compose -f compose_<Vector_DB>.yaml up -d
It will automatically download the docker image on `docker hub`: | #### Quick Start: 2.Run Docker Compose ```bash docker compose -f compose_<Vector_DB>.yaml up -d
It will automatically download the docker image on `docker hub`: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
14b97f73-8b34-45b9-88e5-4d30728a8132 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 65 | opea-semantic-v1 | a785d310a9723ca6 | If the service is ready, you will get the response like below. ```text INFO: Application startup complete.
Then try the `cURL` command below to validate services. | ai_ref_knowledge | OPEA Documentation | If the service is ready, you will get the response like below. ```text INFO: Application startup complete.
Then try the `cURL` command below to validate services. | If the service is ready, you will get the response like below. ```text INFO: Application startup complete.
Then try the `cURL` command below to validate services. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1e1fb8ab-0789-495f-8e38-266ccabfc873 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 34 | opea-semantic-v1 | 54c50b720eb2ede6 | To construct the Mega Service with Rerank, we utilize the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline within the `chatqna.py` Python script. Build MegaService Docker image via below command:
```bash
git clone https://github.com/opea-project/GenAIExamples.git
cd GenAIExamples/Cha... | ai_ref_knowledge | OPEA Documentation | To construct the Mega Service with Rerank, we utilize the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline within the `chatqna.py` Python script. Build MegaService Docker image via below command:
```bash
git clone https://github.com/opea-project/GenAIExamples.git
cd GenAIExamples/Cha... | To construct the Mega Service with Rerank, we utilize the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline within the `chatqna.py` Python script. Build MegaService Docker image via below command:
```bash
git clone https://github.com/opea-project/GenAIExamples.git
cd GenAIExamples/Cha... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
26cb8fe1-18bb-475e-8a11-63860a86dee0 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 33 | opea-semantic-v1 | 2881d65a7320c3a1 | #### Build Dataprep Image ```bash docker build --no-cache -t opea/dataprep:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/src/Dockerfile . cd ..
#### Build MegaService Docker Image
Option 1. MegaService with Rerank
To construct the Mega Service with Rerank, we utilize ... | ai_ref_knowledge | OPEA Documentation | #### Build Dataprep Image ```bash docker build --no-cache -t opea/dataprep:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/src/Dockerfile . cd ..
#### Build MegaService Docker Image
Option 1. MegaService with Rerank
To construct the Mega Service with Rerank, we utilize ... | #### Build Dataprep Image ```bash docker build --no-cache -t opea/dataprep:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/src/Dockerfile . cd ..
#### Build MegaService Docker Image
Option 1. MegaService with Rerank
To construct the Mega Service with Rerank, we utilize ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
2d768941-208f-4ea2-b5ce-81711b42162e | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 13 | opea-semantic-v1 | 911ce2bd1ee07b79 | VectorDB.yaml adds the VectorDB specific configurations
compose_<Vector_DB>.yaml contains all the necessary configs to launch a ChatQnA pipeline with the VectorDB. The different microservices are configured in different sections | ai_ref_knowledge | OPEA Documentation | VectorDB.yaml adds the VectorDB specific configurations
compose_<Vector_DB>.yaml contains all the necessary configs to launch a ChatQnA pipeline with the VectorDB. The different microservices are configured in different sections | VectorDB.yaml adds the VectorDB specific configurations
compose_<Vector_DB>.yaml contains all the necessary configs to launch a ChatQnA pipeline with the VectorDB. The different microservices are configured in different sections | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
3055f58d-1086-49ea-a571-28ba6990803a | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 67 | opea-semantic-v1 | 495455107960953f | ```bash curl http://${host_ip}:9009/v1/chat/completions \ -X POST \ -d '{"model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [{"role": "user", "content": "What is Deep Learning?"}], "max_tokens":17}' \ -H 'Content-Type: application/json'
#### MegaService | ai_ref_knowledge | OPEA Documentation | ```bash curl http://${host_ip}:9009/v1/chat/completions \ -X POST \ -d '{"model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [{"role": "user", "content": "What is Deep Learning?"}], "max_tokens":17}' \ -H 'Content-Type: application/json'
#### MegaService | ```bash curl http://${host_ip}:9009/v1/chat/completions \ -X POST \ -d '{"model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [{"role": "user", "content": "What is Deep Learning?"}], "max_tokens":17}' \ -H 'Content-Type: application/json'
#### MegaService | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
3c2eae76-6118-40fb-b886-13756e1e93a3 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 81 | opea-semantic-v1 | fca9373ec2cc4f56 | # Curl the Mega Service echo "================Testing retriever service: Text Request ================"
local CONTENT=$(http_proxy="" curl http://${ip_address}:8889/v1/retrievaltool -X POST -H "Content-Type: application/json" -d '{
"text": "Explain the OPEA project?"
}') | ai_ref_knowledge | OPEA Documentation | # Curl the Mega Service echo "================Testing retriever service: Text Request ================"
local CONTENT=$(http_proxy="" curl http://${ip_address}:8889/v1/retrievaltool -X POST -H "Content-Type: application/json" -d '{
"text": "Explain the OPEA project?"
}') | # Curl the Mega Service echo "================Testing retriever service: Text Request ================"
local CONTENT=$(http_proxy="" curl http://${ip_address}:8889/v1/retrievaltool -X POST -H "Content-Type: application/json" -d '{
"text": "Explain the OPEA project?"
}') | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
3d957e58-a944-48b9-a2fb-13cf1adb3e75 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 3 | opea-semantic-v1 | 188b906f9a21eef3 | new Vector DB to ChatQnA example with OPEA comps. Here we will be showcasing how to build an (end-to-end) e2e ChatQnA with a new VectorDB.
### Overview | ai_ref_knowledge | OPEA Documentation | new Vector DB to ChatQnA example with OPEA comps. Here we will be showcasing how to build an (end-to-end) e2e ChatQnA with a new VectorDB.
### Overview | new Vector DB to ChatQnA example with OPEA comps. Here we will be showcasing how to build an (end-to-end) e2e ChatQnA with a new VectorDB.
### Overview | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
440626bc-a120-4be7-9f19-94dfc4eb8804 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 24 | opea-semantic-v1 | d3310b8d15e79a15 | If you are in a proxy environment, also set the proxy-related environment variables: ```bash export http_proxy="Your_HTTP_Proxy" export https_proxy="Your_HTTPs_Proxy" # Example: no_proxy="localhost, 127.0.0.1, 192.168.1.1" export no_proxy="Your_No_Proxy",chatqna-xeon-ui-server,chatqna-xeon-backend-server,dataprep-pinec... | ai_ref_knowledge | OPEA Documentation | If you are in a proxy environment, also set the proxy-related environment variables: ```bash export http_proxy="Your_HTTP_Proxy" export https_proxy="Your_HTTPs_Proxy" # Example: no_proxy="localhost, 127.0.0.1, 192.168.1.1" export no_proxy="Your_No_Proxy",chatqna-xeon-ui-server,chatqna-xeon-backend-server,dataprep-pinec... | If you are in a proxy environment, also set the proxy-related environment variables: ```bash export http_proxy="Your_HTTP_Proxy" export https_proxy="Your_HTTPs_Proxy" # Example: no_proxy="localhost, 127.0.0.1, 192.168.1.1" export no_proxy="Your_No_Proxy",chatqna-xeon-ui-server,chatqna-xeon-backend-server,dataprep-pinec... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
453f3e39-60a8-4b32-83b5-a95044429565 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 66 | opea-semantic-v1 | 2f67db85b15ce530 | Then try the `cURL` command below to validate services.
```bash
curl http://${host_ip}:9009/v1/chat/completions \
-X POST \
-d '{"model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [{"role": "user", "content": "What is Deep Learning?"}], "max_tokens":17}' \
-H 'Content-Type: application/json' | ai_ref_knowledge | OPEA Documentation | Then try the `cURL` command below to validate services.
```bash
curl http://${host_ip}:9009/v1/chat/completions \
-X POST \
-d '{"model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [{"role": "user", "content": "What is Deep Learning?"}], "max_tokens":17}' \
-H 'Content-Type: application/json' | Then try the `cURL` command below to validate services.
```bash
curl http://${host_ip}:9009/v1/chat/completions \
-X POST \
-d '{"model": "meta-llama/Meta-Llama-3-8B-Instruct", "messages": [{"role": "user", "content": "What is Deep Learning?"}], "max_tokens":17}' \
-H 'Content-Type: application/json' | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
48a87199-b26d-4c49-bd2e-b2ccd13b4bb8 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 56 | opea-semantic-v1 | d806b70e9ed91f27 | remote client, please make sure the **ports** of the microservices are opened in the firewall of the cloud node. Follow the instructions to validate MicroServices.
#### TEI Embedding Service | ai_ref_knowledge | OPEA Documentation | remote client, please make sure the **ports** of the microservices are opened in the firewall of the cloud node. Follow the instructions to validate MicroServices.
#### TEI Embedding Service | remote client, please make sure the **ports** of the microservices are opened in the firewall of the cloud node. Follow the instructions to validate MicroServices.
#### TEI Embedding Service | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4b649eed-2c23-4c3e-a68a-6f8981da50cb | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 74 | opea-semantic-v1 | 46ce333bd5789208 | the file path according to your environment. Add Knowledge Base via HTTP Links: ```bash curl -X POST "http://${host_ip}:6007/v1/dataprep/ingest" \ -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. To dele... | ai_ref_knowledge | OPEA Documentation | the file path according to your environment. Add Knowledge Base via HTTP Links: ```bash curl -X POST "http://${host_ip}:6007/v1/dataprep/ingest" \ -H "Content-Type: multipart/form-data" \ -F 'link_list=["https://opea.dev"]'
This command updates a knowledge base by submitting a list of HTTP links for processing. To dele... | the file path according to your environment. Add Knowledge Base via HTTP Links: ```bash curl -X POST "http://${host_ip}:6007/v1/dataprep/ingest" \ -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. To dele... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
52c34d80-e304-40a2-b818-9cc4574b92bc | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 70 | opea-semantic-v1 | 34861cf05ea9007d | #### Nginx Service
```bash
curl http://${host_ip}:${NGINX_PORT}/v1/chatqna \
-H "Content-Type: application/json" \
-d '{"messages": "What is the revenue of Nike in 2023?"}' | ai_ref_knowledge | OPEA Documentation | #### Nginx Service
```bash
curl http://${host_ip}:${NGINX_PORT}/v1/chatqna \
-H "Content-Type: application/json" \
-d '{"messages": "What is the revenue of Nike in 2023?"}' | #### Nginx Service
```bash
curl http://${host_ip}:${NGINX_PORT}/v1/chatqna \
-H "Content-Type: application/json" \
-d '{"messages": "What is the revenue of Nike in 2023?"}' | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
52dc53d2-3aa3-45af-bb7e-5f5b9fbe5708 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 59 | opea-semantic-v1 | e38516886df7b960 | Here we use the model `EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"`, which vector size is 768. Check the vector dimension of your embedding model, set `your_embedding` dimension equals to it.
```bash
export your_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedd... | ai_ref_knowledge | OPEA Documentation | Here we use the model `EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"`, which vector size is 768. Check the vector dimension of your embedding model, set `your_embedding` dimension equals to it.
```bash
export your_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedd... | Here we use the model `EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"`, which vector size is 768. Check the vector dimension of your embedding model, set `your_embedding` dimension equals to it.
```bash
export your_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedd... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
54bd3032-c3f3-49c7-82f2-a31bb95416c7 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 53 | opea-semantic-v1 | bbd0898a593b3944 | ```bash source ./set_env.sh
#### Start all the services
> Before running the docker compose command, you need to be in the folder that has the docker compose yaml file | ai_ref_knowledge | OPEA Documentation | ```bash source ./set_env.sh
#### Start all the services
> Before running the docker compose command, you need to be in the folder that has the docker compose yaml file | ```bash source ./set_env.sh
#### Start all the services
> Before running the docker compose command, you need to be in the folder that has the docker compose yaml file | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
568a3eae-b848-4338-b91e-ca23d44c8bb6 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 36 | opea-semantic-v1 | b1de3f04809deb9b | To construct the Mega Service without Rerank, we utilize the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline within the `chatqna_without_rerank.py` Python script. Build MegaService Docker image via below command:
```bash
git clone https://github.com/opea-project/GenAIExamples.git
cd... | ai_ref_knowledge | OPEA Documentation | To construct the Mega Service without Rerank, we utilize the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline within the `chatqna_without_rerank.py` Python script. Build MegaService Docker image via below command:
```bash
git clone https://github.com/opea-project/GenAIExamples.git
cd... | To construct the Mega Service without Rerank, we utilize the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline within the `chatqna_without_rerank.py` Python script. Build MegaService Docker image via below command:
```bash
git clone https://github.com/opea-project/GenAIExamples.git
cd... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
59b7ecfd-470d-4781-be15-7e38e1f18e13 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 12 | opea-semantic-v1 | 7dc95bd777976d64 | To customize ChatQnA with the new VectorDB the changes are in GenAIExamples/ChatQnA
ChatQnA
|__docker_compose
|__intel
|__cpu/xeon
| |__compose_<Vector_DB>.yaml
| |__<Vector_DB>.yaml
| |__README_<Vector_DB>.md
|__hpu/gaudi
|__compose_<Vector_DB>.yaml
|__README_<Vector_DB>.md | ai_ref_knowledge | OPEA Documentation | To customize ChatQnA with the new VectorDB the changes are in GenAIExamples/ChatQnA
ChatQnA
|__docker_compose
|__intel
|__cpu/xeon
| |__compose_<Vector_DB>.yaml
| |__<Vector_DB>.yaml
| |__README_<Vector_DB>.md
|__hpu/gaudi
|__compose_<Vector_DB>.yaml
|__README_<Vector_DB>.md | To customize ChatQnA with the new VectorDB the changes are in GenAIExamples/ChatQnA
ChatQnA
|__docker_compose
|__intel
|__cpu/xeon
| |__compose_<Vector_DB>.yaml
| |__<Vector_DB>.yaml
| |__README_<Vector_DB>.md
|__hpu/gaudi
|__compose_<Vector_DB>.yaml
|__README_<Vector_DB>.md | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5ec18d97-c927-4f67-9db4-d9679f8dd67b | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 30 | opea-semantic-v1 | d8e6a8307b3aeb99 | ### Build Docker Images
First of all, you need to build Docker Images locally and install the python package of it. ```bash
git clone https://github.com/opea-project/GenAIComps.git
cd GenAIComps | ai_ref_knowledge | OPEA Documentation | ### Build Docker Images
First of all, you need to build Docker Images locally and install the python package of it. ```bash
git clone https://github.com/opea-project/GenAIComps.git
cd GenAIComps | ### Build Docker Images
First of all, you need to build Docker Images locally and install the python package of it. ```bash
git clone https://github.com/opea-project/GenAIComps.git
cd GenAIComps | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
64755b46-2bc1-4a81-a1db-a2b9d5634a6f | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 15 | opea-semantic-v1 | a4814a4cea03638f | chatqna-gaudi-nginx-server:Load balancer for Gaudi only
README_<Vector_DB>.md adds details to start the Mega service of ChatQnA on Xeon in respective folders | ai_ref_knowledge | OPEA Documentation | chatqna-gaudi-nginx-server:Load balancer for Gaudi only
README_<Vector_DB>.md adds details to start the Mega service of ChatQnA on Xeon in respective folders | chatqna-gaudi-nginx-server:Load balancer for Gaudi only
README_<Vector_DB>.md adds details to start the Mega service of ChatQnA on Xeon in respective folders | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
69a5a99c-64f3-4e11-8567-bb7a3a69c6cf | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 50 | opea-semantic-v1 | cf41e8363ca54a21 | #### Setup Environment Variables 1. Set the required environment variables:
```bash
# Example: host_ip="192.168.1.1"
export host_ip="External_Public_IP"
export HUGGINGFACEHUB_API_TOKEN="Your_Huggingface_API_Token"
# Example: NGINX_PORT=80
export NGINX_PORT=${your_nginx_port} | ai_ref_knowledge | OPEA Documentation | #### Setup Environment Variables 1. Set the required environment variables:
```bash
# Example: host_ip="192.168.1.1"
export host_ip="External_Public_IP"
export HUGGINGFACEHUB_API_TOKEN="Your_Huggingface_API_Token"
# Example: NGINX_PORT=80
export NGINX_PORT=${your_nginx_port} | #### Setup Environment Variables 1. Set the required environment variables:
```bash
# Example: host_ip="192.168.1.1"
export host_ip="External_Public_IP"
export HUGGINGFACEHUB_API_TOKEN="Your_Huggingface_API_Token"
# Example: NGINX_PORT=80
export NGINX_PORT=${your_nginx_port} | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6cd957b0-449f-4931-a33d-8930af813b06 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 37 | opea-semantic-v1 | fefd1c2fbec1810a | ```bash git clone https://github.com/opea-project/GenAIExamples.git cd GenAIExamples/ChatQnA docker build --no-cache -t opea/chatqna-without-rerank:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile.without_rerank .
#### Build UI Docker Image
Build frontend Docker image via bel... | ai_ref_knowledge | OPEA Documentation | ```bash git clone https://github.com/opea-project/GenAIExamples.git cd GenAIExamples/ChatQnA docker build --no-cache -t opea/chatqna-without-rerank:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile.without_rerank .
#### Build UI Docker Image
Build frontend Docker image via bel... | ```bash git clone https://github.com/opea-project/GenAIExamples.git cd GenAIExamples/ChatQnA docker build --no-cache -t opea/chatqna-without-rerank:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile.without_rerank .
#### Build UI Docker Image
Build frontend Docker image via bel... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
70f23f23-979e-4deb-8f79-001583d15e08 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 48 | opea-semantic-v1 | e783c8d0ea28fd13 | HF_ENDPOINT="https://hf-mirror.com" model_name="meta-llama/Meta-Llama-3-8B-Instruct" docker run -p 8008:80 -v ./data:/data --name vllm-service -e HF_ENDPOINT=$HF_ENDPOINT -e http_proxy=$http_proxy -e https_proxy=$https_proxy --shm-size 128g opea/vllm:latest --model $model_name --host 0.0.0.0 --port 80
2. Offline
- Sea... | ai_ref_knowledge | OPEA Documentation | HF_ENDPOINT="https://hf-mirror.com" model_name="meta-llama/Meta-Llama-3-8B-Instruct" docker run -p 8008:80 -v ./data:/data --name vllm-service -e HF_ENDPOINT=$HF_ENDPOINT -e http_proxy=$http_proxy -e https_proxy=$https_proxy --shm-size 128g opea/vllm:latest --model $model_name --host 0.0.0.0 --port 80
2. Offline
- Sea... | HF_ENDPOINT="https://hf-mirror.com" model_name="meta-llama/Meta-Llama-3-8B-Instruct" docker run -p 8008:80 -v ./data:/data --name vllm-service -e HF_ENDPOINT=$HF_ENDPOINT -e http_proxy=$http_proxy -e https_proxy=$https_proxy --shm-size 128g opea/vllm:latest --model $model_name --host 0.0.0.0 --port 80
2. Offline
- Sea... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
72857a03-a71d-4a9b-857e-f939db38134d | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 9 | opea-semantic-v1 | 952357cbbf16bda5 | 1.1, etc) and checkout using the tag with that version. The GenAIComps should contain the customized components(third_party, dataprep, retrievers) for the VectorDB as mentioned before.
```bash
# Set workspace
export WORKSPACE=<path>
cd $WORKSPACE | ai_ref_knowledge | OPEA Documentation | 1.1, etc) and checkout using the tag with that version. The GenAIComps should contain the customized components(third_party, dataprep, retrievers) for the VectorDB as mentioned before.
```bash
# Set workspace
export WORKSPACE=<path>
cd $WORKSPACE | 1.1, etc) and checkout using the tag with that version. The GenAIComps should contain the customized components(third_party, dataprep, retrievers) for the VectorDB as mentioned before.
```bash
# Set workspace
export WORKSPACE=<path>
cd $WORKSPACE | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7b2eee73-bf19-4a9a-bbe1-b4b7c72a3f8d | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 73 | opea-semantic-v1 | 01847c17f790f6b1 | Upload: ```bash curl -X POST "http://${host_ip}:6007/v1/dataprep/ingest" \ -H "Content-Type: multipart/form-data" \ -F "files=@./nke-10k-2023.pdf"
This command updates a knowledge base by uploading a local file for processing. Update the file path according to your environment. Add Knowledge Base via HTTP Links:
```bas... | ai_ref_knowledge | OPEA Documentation | Upload: ```bash curl -X POST "http://${host_ip}:6007/v1/dataprep/ingest" \ -H "Content-Type: multipart/form-data" \ -F "files=@./nke-10k-2023.pdf"
This command updates a knowledge base by uploading a local file for processing. Update the file path according to your environment. Add Knowledge Base via HTTP Links:
```bas... | Upload: ```bash curl -X POST "http://${host_ip}:6007/v1/dataprep/ingest" \ -H "Content-Type: multipart/form-data" \ -F "files=@./nke-10k-2023.pdf"
This command updates a knowledge base by uploading a local file for processing. Update the file path according to your environment. Add Knowledge Base via HTTP Links:
```bas... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7d53dece-6fbe-426a-b720-2460ac741cde | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 43 | opea-semantic-v1 | 0d4f0e3961bf8f8e | Then run the command `docker images`, you will have the following 5 Docker Images:
1. `opea/dataprep:latest`
2. `opea/retriever:latest`
3. `opea/chatqna:latest` or `opea/chatqna-without-rerank:latest`
4. `opea/chatqna-ui:latest`
5. `opea/nginx:latest` | ai_ref_knowledge | OPEA Documentation | Then run the command `docker images`, you will have the following 5 Docker Images:
1. `opea/dataprep:latest`
2. `opea/retriever:latest`
3. `opea/chatqna:latest` or `opea/chatqna-without-rerank:latest`
4. `opea/chatqna-ui:latest`
5. `opea/nginx:latest` | Then run the command `docker images`, you will have the following 5 Docker Images:
1. `opea/dataprep:latest`
2. `opea/retriever:latest`
3. `opea/chatqna:latest` or `opea/chatqna-without-rerank:latest`
4. `opea/chatqna-ui:latest`
5. `opea/nginx:latest` | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
80c6f483-eb5b-4b56-8583-bf2d0b7677e8 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 20 | opea-semantic-v1 | 432032a5c862685d | 1. Set up the environment variables. 2. Run Docker Compose. 3. Consume the ChatQnA Service.
The default pipeline deploys with vLLM as the LLM serving component and leverages the re-rank component. | ai_ref_knowledge | OPEA Documentation | 1. Set up the environment variables. 2. Run Docker Compose. 3. Consume the ChatQnA Service.
The default pipeline deploys with vLLM as the LLM serving component and leverages the re-rank component. | 1. Set up the environment variables. 2. Run Docker Compose. 3. Consume the ChatQnA Service.
The default pipeline deploys with vLLM as the LLM serving component and leverages the re-rank component. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8105e377-3593-4f95-a8c5-66236f5264b6 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 71 | opea-semantic-v1 | cc45565b28d96daa | ```bash curl http://${host_ip}:${NGINX_PORT}/v1/chatqna \ -H "Content-Type: application/json" \ -d '{"messages": "What is the revenue of Nike in 2023?"}'
#### Dataprep Microservice（Optional）
If you want to update the default knowledge base, you can use the following commands:
Update Knowledge Base via Local File [nke-1... | ai_ref_knowledge | OPEA Documentation | ```bash curl http://${host_ip}:${NGINX_PORT}/v1/chatqna \ -H "Content-Type: application/json" \ -d '{"messages": "What is the revenue of Nike in 2023?"}'
#### Dataprep Microservice（Optional）
If you want to update the default knowledge base, you can use the following commands:
Update Knowledge Base via Local File [nke-1... | ```bash curl http://${host_ip}:${NGINX_PORT}/v1/chatqna \ -H "Content-Type: application/json" \ -d '{"messages": "What is the revenue of Nike in 2023?"}'
#### Dataprep Microservice（Optional）
If you want to update the default knowledge base, you can use the following commands:
Update Knowledge Base via Local File [nke-1... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8437a6a5-6b8d-4f09-ad5f-6a14797b7634 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 40 | opea-semantic-v1 | c9a2d1daebbea470 | #### Build Conversational React UI Docker Image (Optional) Build frontend Docker image that enables Conversational experience with ChatQnA megaservice via below command:
```bash
cd GenAIExamples/ChatQnA/ui
docker build --no-cache -t opea/chatqna-conversation-ui:latest --build-arg https_proxy=$https_proxy --build-arg ht... | ai_ref_knowledge | OPEA Documentation | #### Build Conversational React UI Docker Image (Optional) Build frontend Docker image that enables Conversational experience with ChatQnA megaservice via below command:
```bash
cd GenAIExamples/ChatQnA/ui
docker build --no-cache -t opea/chatqna-conversation-ui:latest --build-arg https_proxy=$https_proxy --build-arg ht... | #### Build Conversational React UI Docker Image (Optional) Build frontend Docker image that enables Conversational experience with ChatQnA megaservice via below command:
```bash
cd GenAIExamples/ChatQnA/ui
docker build --no-cache -t opea/chatqna-conversation-ui:latest --build-arg https_proxy=$https_proxy --build-arg ht... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
891cb7c1-08b0-4e3a-ab1c-1528d197eb29 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 86 | opea-semantic-v1 | e8e58c69371cf581 | echo "===========Ingest data==================" local CONTENT=$(http_proxy="" curl -X POST "http://${ip_address}:6007/v1/dataprep/ingest" \ -H "Content-Type: multipart/form-data" \ -F 'link_list=["https://opea.dev/"]') local EXIT_CODE=$(validate "$CONTENT" "Data preparation succeeded" "dataprep-<Vector_DB>-service-gaud... | ai_ref_knowledge | OPEA Documentation | echo "===========Ingest data==================" local CONTENT=$(http_proxy="" curl -X POST "http://${ip_address}:6007/v1/dataprep/ingest" \ -H "Content-Type: multipart/form-data" \ -F 'link_list=["https://opea.dev/"]') local EXIT_CODE=$(validate "$CONTENT" "Data preparation succeeded" "dataprep-<Vector_DB>-service-gaud... | echo "===========Ingest data==================" local CONTENT=$(http_proxy="" curl -X POST "http://${ip_address}:6007/v1/dataprep/ingest" \ -H "Content-Type: multipart/form-data" \ -F 'link_list=["https://opea.dev/"]') local EXIT_CODE=$(validate "$CONTENT" "Data preparation succeeded" "dataprep-<Vector_DB>-service-gaud... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
897e7f66-9eb8-4cb1-90ce-908443831ee8 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 5 | opea-semantic-v1 | 2e8272b7b070f9a8 | this tutorial, we will walk through how to enable a new VectorDB with the below list of microservices from OPEA: GenAIComps to setup a ChatQnA.
1. Data Prep
2. Embedding
3. Retriever
4. Reranking
5. LLM with Ollama | ai_ref_knowledge | OPEA Documentation | this tutorial, we will walk through how to enable a new VectorDB with the below list of microservices from OPEA: GenAIComps to setup a ChatQnA.
1. Data Prep
2. Embedding
3. Retriever
4. Reranking
5. LLM with Ollama | this tutorial, we will walk through how to enable a new VectorDB with the below list of microservices from OPEA: GenAIComps to setup a ChatQnA.
1. Data Prep
2. Embedding
3. Retriever
4. Reranking
5. LLM with Ollama | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
89b94c2a-144f-44aa-a61a-b33896af42fa | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 42 | opea-semantic-v1 | cbc11bce99fc9448 | ```bash cd GenAIComps docker build -t opea/nginx:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/third_parties/nginx/src/Dockerfile .
Then run the command `docker images`, you will have the following 5 Docker Images: | ai_ref_knowledge | OPEA Documentation | ```bash cd GenAIComps docker build -t opea/nginx:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/third_parties/nginx/src/Dockerfile .
Then run the command `docker images`, you will have the following 5 Docker Images: | ```bash cd GenAIComps docker build -t opea/nginx:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/third_parties/nginx/src/Dockerfile .
Then run the command `docker images`, you will have the following 5 Docker Images: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8bc14c04-7b37-4435-8e8c-08c931dbc80f | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 44 | opea-semantic-v1 | e89f1cf9620790e1 | 1. `opea/dataprep:latest` 2. `opea/retriever:latest` 3. `opea/chatqna:latest` or `opea/chatqna-without-rerank:latest` 4. `opea/chatqna-ui:latest` 5. `opea/nginx:latest`
### Start Microservices
#### Required Models
By default, the embedding, reranking and LLM models are set to a default value as listed below: | ai_ref_knowledge | OPEA Documentation | 1. `opea/dataprep:latest` 2. `opea/retriever:latest` 3. `opea/chatqna:latest` or `opea/chatqna-without-rerank:latest` 4. `opea/chatqna-ui:latest` 5. `opea/nginx:latest`
### Start Microservices
#### Required Models
By default, the embedding, reranking and LLM models are set to a default value as listed below: | 1. `opea/dataprep:latest` 2. `opea/retriever:latest` 3. `opea/chatqna:latest` or `opea/chatqna-without-rerank:latest` 4. `opea/chatqna-ui:latest` 5. `opea/nginx:latest`
### Start Microservices
#### Required Models
By default, the embedding, reranking and LLM models are set to a default value as listed below: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8feda191-757f-4a1f-8718-8dd047cf239c | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 78 | opea-semantic-v1 | 0d2b4ce1a2411266 | ChatQnA" docker compose -f build.yaml build ${service_list} --no-cache docker pull ghcr.io/huggingface/text-embeddings-inference:cpu-1.5 docker pull <Vector_DB> specific images docker images && sleep 1s echo "Docker images built!"
start_services()
echo "Starting Docker Services...."
export EMBEDDING_MODEL_ID="BAAI/... | ai_ref_knowledge | OPEA Documentation | ChatQnA" docker compose -f build.yaml build ${service_list} --no-cache docker pull ghcr.io/huggingface/text-embeddings-inference:cpu-1.5 docker pull <Vector_DB> specific images docker images && sleep 1s echo "Docker images built!"
start_services()
echo "Starting Docker Services...."
export EMBEDDING_MODEL_ID="BAAI/... | ChatQnA" docker compose -f build.yaml build ${service_list} --no-cache docker pull ghcr.io/huggingface/text-embeddings-inference:cpu-1.5 docker pull <Vector_DB> specific images docker images && sleep 1s echo "Docker images built!"
start_services()
echo "Starting Docker Services...."
export EMBEDDING_MODEL_ID="BAAI/... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
9104301e-7eb7-460f-8c7b-1b28911c0795 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 47 | opea-semantic-v1 | ac6039c5aeb3af04 | Huggingface, you can use [ModelScope](https://www.modelscope.cn/models) or a Huggingface mirror to download models. The vLLM can load the models either online or offline as described below:
1. Online
```bash
export HF_TOKEN=${your_hf_token}
export HF_ENDPOINT="https://hf-mirror.com"
model_name="meta-llama/Meta-Llam... | ai_ref_knowledge | OPEA Documentation | Huggingface, you can use [ModelScope](https://www.modelscope.cn/models) or a Huggingface mirror to download models. The vLLM can load the models either online or offline as described below:
1. Online
```bash
export HF_TOKEN=${your_hf_token}
export HF_ENDPOINT="https://hf-mirror.com"
model_name="meta-llama/Meta-Llam... | Huggingface, you can use [ModelScope](https://www.modelscope.cn/models) or a Huggingface mirror to download models. The vLLM can load the models either online or offline as described below:
1. Online
```bash
export HF_TOKEN=${your_hf_token}
export HF_ENDPOINT="https://hf-mirror.com"
model_name="meta-llama/Meta-Llam... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
91a2d2bb-5380-4767-815f-a5b95702b497 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 85 | opea-semantic-v1 | e0066988911a9a63 | TEI_RERANKING_ENDPOINT="http://${ip_address}:8808" export TGI_LLM_ENDPOINT="http://${ip_address}:8008" export MILVUS_HOST=${ip_address} export HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN} export MEGA_SERVICE_HOST_IP=${ip_address} export EMBEDDING_SERVICE_HOST_IP=${ip_address} export RETRIEVER_SERVICE_HOST_IP=${... | ai_ref_knowledge | OPEA Documentation | TEI_RERANKING_ENDPOINT="http://${ip_address}:8808" export TGI_LLM_ENDPOINT="http://${ip_address}:8008" export MILVUS_HOST=${ip_address} export HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN} export MEGA_SERVICE_HOST_IP=${ip_address} export EMBEDDING_SERVICE_HOST_IP=${ip_address} export RETRIEVER_SERVICE_HOST_IP=${... | TEI_RERANKING_ENDPOINT="http://${ip_address}:8808" export TGI_LLM_ENDPOINT="http://${ip_address}:8008" export MILVUS_HOST=${ip_address} export HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN} export MEGA_SERVICE_HOST_IP=${ip_address} export EMBEDDING_SERVICE_HOST_IP=${ip_address} export RETRIEVER_SERVICE_HOST_IP=${... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
972fa96a-889a-40c9-9cd8-82a805ad45b2 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 52 | opea-semantic-v1 | 12c3c5c311d8788c | 2. If you are in a proxy environment, also set the proxy-related environment variables:
```bash
export http_proxy="Your_HTTP_Proxy"
export https_proxy="Your_HTTPs_Proxy"
# Example: no_proxy="localhost, 127.0.0.1, 192.168.1.1"
export no_proxy="Your_No_Proxy",chatqna-xeon-ui-server,chatqna-xeon-backend-server,datapre... | ai_ref_knowledge | OPEA Documentation | 2. If you are in a proxy environment, also set the proxy-related environment variables:
```bash
export http_proxy="Your_HTTP_Proxy"
export https_proxy="Your_HTTPs_Proxy"
# Example: no_proxy="localhost, 127.0.0.1, 192.168.1.1"
export no_proxy="Your_No_Proxy",chatqna-xeon-ui-server,chatqna-xeon-backend-server,datapre... | 2. If you are in a proxy environment, also set the proxy-related environment variables:
```bash
export http_proxy="Your_HTTP_Proxy"
export https_proxy="Your_HTTPs_Proxy"
# Example: no_proxy="localhost, 127.0.0.1, 192.168.1.1"
export no_proxy="Your_No_Proxy",chatqna-xeon-ui-server,chatqna-xeon-backend-server,datapre... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a050a8fd-0ba3-4900-bff9-0159dc3d1abf | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 55 | opea-semantic-v1 | 084f5141f14ab2ec | ### Validate Microservices
Note, when verify the microservices by curl or API from remote client, please make sure the **ports** of the microservices are opened in the firewall of the cloud node. Follow the instructions to validate MicroServices. | ai_ref_knowledge | OPEA Documentation | ### Validate Microservices
Note, when verify the microservices by curl or API from remote client, please make sure the **ports** of the microservices are opened in the firewall of the cloud node. Follow the instructions to validate MicroServices. | ### Validate Microservices
Note, when verify the microservices by curl or API from remote client, please make sure the **ports** of the microservices are opened in the firewall of the cloud node. Follow the instructions to validate MicroServices. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a6b29fc6-d54b-47f6-9342-6bb21b494acc | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 21 | opea-semantic-v1 | e6279d5a45bf1f4e | The default pipeline deploys with vLLM as the LLM serving component and leverages the re-rank component.
Note: The default LLM is `meta-llama/Meta-Llama-38B-Instruct`. Before deploying the aplication, please make sure either you've requested and have been granted access to it on [HuggingFace](https://huggingface.co/met... | ai_ref_knowledge | OPEA Documentation | The default pipeline deploys with vLLM as the LLM serving component and leverages the re-rank component.
Note: The default LLM is `meta-llama/Meta-Llama-38B-Instruct`. Before deploying the aplication, please make sure either you've requested and have been granted access to it on [HuggingFace](https://huggingface.co/met... | The default pipeline deploys with vLLM as the LLM serving component and leverages the re-rank component.
Note: The default LLM is `meta-llama/Meta-Llama-38B-Instruct`. Before deploying the aplication, please make sure either you've requested and have been granted access to it on [HuggingFace](https://huggingface.co/met... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
aa06d5e0-c982-46c8-be7e-155aca5b4667 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 10 | opea-semantic-v1 | 891c8c84136f7714 | # Set desired release version - number only export RELEASE_VERSION=<insert-release-version>
# GenAIComps
git clone https://github.com/opea-project/GenAIComps.git
cd GenAIComps
git checkout tags/v${RELEASE_VERSION}
cd .. # GenAIExamples
git clone https://github.com/opea-project/GenAIExamples.git
cd GenAIExamples
git che... | ai_ref_knowledge | OPEA Documentation | # Set desired release version - number only export RELEASE_VERSION=<insert-release-version>
# GenAIComps
git clone https://github.com/opea-project/GenAIComps.git
cd GenAIComps
git checkout tags/v${RELEASE_VERSION}
cd .. # GenAIExamples
git clone https://github.com/opea-project/GenAIExamples.git
cd GenAIExamples
git che... | # Set desired release version - number only export RELEASE_VERSION=<insert-release-version>
# GenAIComps
git clone https://github.com/opea-project/GenAIComps.git
cd GenAIComps
git checkout tags/v${RELEASE_VERSION}
cd .. # GenAIExamples
git clone https://github.com/opea-project/GenAIExamples.git
cd GenAIExamples
git che... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ab67e83d-49ac-4f1e-b82d-8b2784f7d71b | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 14 | opea-semantic-v1 | ddbe84057ea94f7d | compose_<Vector_DB>.yaml contains all the necessary configs to launch a ChatQnA pipeline with the VectorDB. The different microservices are configured in different sections
services: VectorDB specific services and healthcheck | ai_ref_knowledge | OPEA Documentation | compose_<Vector_DB>.yaml contains all the necessary configs to launch a ChatQnA pipeline with the VectorDB. The different microservices are configured in different sections
services: VectorDB specific services and healthcheck | compose_<Vector_DB>.yaml contains all the necessary configs to launch a ChatQnA pipeline with the VectorDB. The different microservices are configured in different sections
services: VectorDB specific services and healthcheck | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ac5fe411-c125-4018-a50a-9d55504a7892 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 51 | opea-semantic-v1 | 31122dc29799bd45 | ```bash # Example: host_ip="192.168.1.1" export host_ip="External_Public_IP" export HUGGINGFACEHUB_API_TOKEN="Your_Huggingface_API_Token" # Example: NGINX_PORT=80 export NGINX_PORT=${your_nginx_port}
2. If you are in a proxy environment, also set the proxy-related environment variables: | ai_ref_knowledge | OPEA Documentation | ```bash # Example: host_ip="192.168.1.1" export host_ip="External_Public_IP" export HUGGINGFACEHUB_API_TOKEN="Your_Huggingface_API_Token" # Example: NGINX_PORT=80 export NGINX_PORT=${your_nginx_port}
2. If you are in a proxy environment, also set the proxy-related environment variables: | ```bash # Example: host_ip="192.168.1.1" export host_ip="External_Public_IP" export HUGGINGFACEHUB_API_TOKEN="Your_Huggingface_API_Token" # Example: NGINX_PORT=80 export NGINX_PORT=${your_nginx_port}
2. If you are in a proxy environment, also set the proxy-related environment variables: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ad3cce3e-98ef-44ab-afe9-afb40242467c | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 4 | opea-semantic-v1 | d409420d0195d9a9 | ### Overview
There are several ways to setup a ChatQnA use case with different VectorDBs. Here in this tutorial, we will walk through how to enable a new VectorDB with the below list of microservices from OPEA:
GenAIComps to setup a ChatQnA. | ai_ref_knowledge | OPEA Documentation | ### Overview
There are several ways to setup a ChatQnA use case with different VectorDBs. Here in this tutorial, we will walk through how to enable a new VectorDB with the below list of microservices from OPEA:
GenAIComps to setup a ChatQnA. | ### Overview
There are several ways to setup a ChatQnA use case with different VectorDBs. Here in this tutorial, we will walk through how to enable a new VectorDB with the below list of microservices from OPEA:
GenAIComps to setup a ChatQnA. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b2f16e41-2eda-4c8b-a013-7e468be0883e | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 75 | opea-semantic-v1 | 390fa85fe9c43ad6 | list of HTTP links for processing. To delete the files/link you uploaded: ```bash curl -X POST "http://${host_ip}:6009/v1/dataprep/delete" \ -d '{"file_path": "all"}' \ -H "Content-Type: application/json"
# Tests for ChatQnA with new VectorDB
This should go under GenAIExamples/ChatQnA/tests | ai_ref_knowledge | OPEA Documentation | list of HTTP links for processing. To delete the files/link you uploaded: ```bash curl -X POST "http://${host_ip}:6009/v1/dataprep/delete" \ -d '{"file_path": "all"}' \ -H "Content-Type: application/json"
# Tests for ChatQnA with new VectorDB
This should go under GenAIExamples/ChatQnA/tests | list of HTTP links for processing. To delete the files/link you uploaded: ```bash curl -X POST "http://${host_ip}:6009/v1/dataprep/delete" \ -d '{"file_path": "all"}' \ -H "Content-Type: application/json"
# Tests for ChatQnA with new VectorDB
This should go under GenAIExamples/ChatQnA/tests | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b65f1f2f-5e34-4ed0-8f23-d65c046ab2ea | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 62 | opea-semantic-v1 | 1a3e97ad6c7355e2 | ```bash curl http://${host_ip}:8808/rerank \ -X POST \ -d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \ -H 'Content-Type: application/json'
#### LLM backend Service
In the first startup, this service will take more time to download, load and warm up the model. Afte... | ai_ref_knowledge | OPEA Documentation | ```bash curl http://${host_ip}:8808/rerank \ -X POST \ -d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \ -H 'Content-Type: application/json'
#### LLM backend Service
In the first startup, this service will take more time to download, load and warm up the model. Afte... | ```bash curl http://${host_ip}:8808/rerank \ -X POST \ -d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \ -H 'Content-Type: application/json'
#### LLM backend Service
In the first startup, this service will take more time to download, load and warm up the model. Afte... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b6836198-55ff-492e-a72d-51171a624217 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 31 | opea-semantic-v1 | 78c8da9a32fca71b | First of all, you need to build Docker Images locally and install the python package of it. ```bash git clone https://github.com/opea-project/GenAIComps.git cd GenAIComps
#### Build Retriever Image
```bash
docker build --no-cache -t opea/retriever:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http... | ai_ref_knowledge | OPEA Documentation | First of all, you need to build Docker Images locally and install the python package of it. ```bash git clone https://github.com/opea-project/GenAIComps.git cd GenAIComps
#### Build Retriever Image
```bash
docker build --no-cache -t opea/retriever:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http... | First of all, you need to build Docker Images locally and install the python package of it. ```bash git clone https://github.com/opea-project/GenAIComps.git cd GenAIComps
#### Build Retriever Image
```bash
docker build --no-cache -t opea/retriever:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b8984c0e-74c5-4b85-be96-0c4b0362126a | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 28 | opea-semantic-v1 | 60b46f76477d36e5 | be different from the published docker image). - You can't download the docker image. - You want to use a specific version of Docker image.
#### QuickStart: 3.Consume the ChatQnA Service
```bash
curl http://${host_ip}:8888/v1/chatqna \
-H "Content-Type: application/json" \
-d '{
"messages": "What is the revenue of N... | ai_ref_knowledge | OPEA Documentation | be different from the published docker image). - You can't download the docker image. - You want to use a specific version of Docker image.
#### QuickStart: 3.Consume the ChatQnA Service
```bash
curl http://${host_ip}:8888/v1/chatqna \
-H "Content-Type: application/json" \
-d '{
"messages": "What is the revenue of N... | be different from the published docker image). - You can't download the docker image. - You want to use a specific version of Docker image.
#### QuickStart: 3.Consume the ChatQnA Service
```bash
curl http://${host_ip}:8888/v1/chatqna \
-H "Content-Type: application/json" \
-d '{
"messages": "What is the revenue of N... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b908cef9-38f7-4ea1-a343-571e126d4cc6 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 17 | opea-semantic-v1 | 006b977dd123b149 | README_<Vector_DB>.md adds details to start the Mega service of ChatQnA on Gaudi in respective folders.
Following are the contents of README_<Vector_DB>.md | ai_ref_knowledge | OPEA Documentation | README_<Vector_DB>.md adds details to start the Mega service of ChatQnA on Gaudi in respective folders.
Following are the contents of README_<Vector_DB>.md | README_<Vector_DB>.md adds details to start the Mega service of ChatQnA on Gaudi in respective folders.
Following are the contents of README_<Vector_DB>.md | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b9bee9e4-4018-4d3f-bb17-433aeec7da4c | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 61 | opea-semantic-v1 | d9240a5f59054c7c | #### TEI Reranking Service Skip for ChatQnA without Rerank pipeline
```bash
curl http://${host_ip}:8808/rerank \
-X POST \
-d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \
-H 'Content-Type: application/json' | ai_ref_knowledge | OPEA Documentation | #### TEI Reranking Service Skip for ChatQnA without Rerank pipeline
```bash
curl http://${host_ip}:8808/rerank \
-X POST \
-d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \
-H 'Content-Type: application/json' | #### TEI Reranking Service Skip for ChatQnA without Rerank pipeline
```bash
curl http://${host_ip}:8808/rerank \
-X POST \
-d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \
-H 'Content-Type: application/json' | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation |
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