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bf935627-0d42-4ca7-8356-2ec446b35236 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 83 | opea-semantic-v1 | 281e434ef38cd799 | test_compose_<Vector_DB>_on_gaudi.sh
build_docker_images()
echo "Building Docker Images...."
if [ ! -d "GenAIComps" ] ; then
git clone --single-branch --branch "${opea_branch:-"main"}" https://github.com/opea-project/GenAIComps.git
fi
service_list="dataprep embedding retriever reranking ChatQnA"
docker compos... | ai_ref_knowledge | OPEA Documentation | test_compose_<Vector_DB>_on_gaudi.sh
build_docker_images()
echo "Building Docker Images...."
if [ ! -d "GenAIComps" ] ; then
git clone --single-branch --branch "${opea_branch:-"main"}" https://github.com/opea-project/GenAIComps.git
fi
service_list="dataprep embedding retriever reranking ChatQnA"
docker compos... | test_compose_<Vector_DB>_on_gaudi.sh
build_docker_images()
echo "Building Docker Images...."
if [ ! -d "GenAIComps" ] ; then
git clone --single-branch --branch "${opea_branch:-"main"}" https://github.com/opea-project/GenAIComps.git
fi
service_list="dataprep embedding retriever reranking ChatQnA"
docker compos... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c1613038-20ad-4aec-bea6-0457583028b7 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 57 | opea-semantic-v1 | fb40d6ea2d128fd4 | #### TEI Embedding Service
```bash
curl ${host_ip}:6006/embed \
-X POST \
-d '{"inputs":"What is Deep Learning?"}' \
-H 'Content-Type: application/json' | ai_ref_knowledge | OPEA Documentation | #### TEI Embedding Service
```bash
curl ${host_ip}:6006/embed \
-X POST \
-d '{"inputs":"What is Deep Learning?"}' \
-H 'Content-Type: application/json' | #### TEI Embedding Service
```bash
curl ${host_ip}:6006/embed \
-X POST \
-d '{"inputs":"What is Deep Learning?"}' \
-H 'Content-Type: application/json' | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c25e755b-a0b1-4a9d-bf5d-c3b77330f434 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 32 | opea-semantic-v1 | ff1993accabdd172 | #### Build Retriever Image ```bash docker build --no-cache -t opea/retriever:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/retrievers/src/Dockerfile .
#### Build Dataprep Image
```bash
docker build --no-cache -t opea/dataprep:latest --build-arg https_proxy=$https_proxy --build-... | ai_ref_knowledge | OPEA Documentation | #### Build Retriever Image ```bash docker build --no-cache -t opea/retriever:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/retrievers/src/Dockerfile .
#### Build Dataprep Image
```bash
docker build --no-cache -t opea/dataprep:latest --build-arg https_proxy=$https_proxy --build-... | #### Build Retriever Image ```bash docker build --no-cache -t opea/retriever:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/retrievers/src/Dockerfile .
#### Build Dataprep Image
```bash
docker build --no-cache -t opea/dataprep:latest --build-arg https_proxy=$https_proxy --build-... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c521ddec-536f-432c-8e1e-d94b36a5a31b | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 72 | opea-semantic-v1 | 65230cbbc650d0c1 | Local File [nke-10k-2023.pdf](https://github.com/opea-project/GenAIComps/blob/main/comps/third_parties/pathway/src/data/nke-10k-2023.pdf). Or click [here](https://github.com/opea-project/GenAIComps/blob/main/comps/third_parties/pathway/src/data/nke-10k-2023.pdf) to download the file via any web browser Or run this comm... | ai_ref_knowledge | OPEA Documentation | Local File [nke-10k-2023.pdf](https://github.com/opea-project/GenAIComps/blob/main/comps/third_parties/pathway/src/data/nke-10k-2023.pdf). Or click [here](https://github.com/opea-project/GenAIComps/blob/main/comps/third_parties/pathway/src/data/nke-10k-2023.pdf) to download the file via any web browser Or run this comm... | Local File [nke-10k-2023.pdf](https://github.com/opea-project/GenAIComps/blob/main/comps/third_parties/pathway/src/data/nke-10k-2023.pdf). Or click [here](https://github.com/opea-project/GenAIComps/blob/main/comps/third_parties/pathway/src/data/nke-10k-2023.pdf) to download the file via any web browser Or run this comm... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c78ba4e5-7ad6-47de-9146-2b6a5ac12904 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 77 | opea-semantic-v1 | fa953901527acca9 | ## Tests for Xeon
test_compose_<Vector_DB>_on_xeon.sh
build_docker_images()
echo "Building Docker Images...."
if [ ! -d "GenAIComps" ] ; then
git clone --single-branch --branch "${opea_branch:-"main"}" https://github.com/opea-project/GenAIComps.git
fi
service_list="dataprep embedding retriever reranking Cha... | ai_ref_knowledge | OPEA Documentation | ## Tests for Xeon
test_compose_<Vector_DB>_on_xeon.sh
build_docker_images()
echo "Building Docker Images...."
if [ ! -d "GenAIComps" ] ; then
git clone --single-branch --branch "${opea_branch:-"main"}" https://github.com/opea-project/GenAIComps.git
fi
service_list="dataprep embedding retriever reranking Cha... | ## Tests for Xeon
test_compose_<Vector_DB>_on_xeon.sh
build_docker_images()
echo "Building Docker Images...."
if [ ! -d "GenAIComps" ] ; then
git clone --single-branch --branch "${opea_branch:-"main"}" https://github.com/opea-project/GenAIComps.git
fi
service_list="dataprep embedding retriever reranking Cha... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c7a6cf57-22e0-478c-b0a3-2fe94c98132d | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 76 | opea-semantic-v1 | f4eeaeb7d8a32b6c | # Tests for ChatQnA with new VectorDB This should go under GenAIExamples/ChatQnA/tests
Test files to create - below examples give a skeleton for test files. | ai_ref_knowledge | OPEA Documentation | # Tests for ChatQnA with new VectorDB This should go under GenAIExamples/ChatQnA/tests
Test files to create - below examples give a skeleton for test files. | # Tests for ChatQnA with new VectorDB This should go under GenAIExamples/ChatQnA/tests
Test files to create - below examples give a skeleton for test files. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c9bd3554-bde0-48e2-b330-0fd19c4aace3 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 60 | opea-semantic-v1 | 204c2fefbd15d639 | your_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)") curl http://${host_ip}:7000/v1/retrieval \ -X POST \ -d "{\"text\":\"test\",\"embedding\":${your_embedding}}" \ -H 'Content-Type: application/json'
#### TEI Reranking Service
Skip for ChatQnA without... | ai_ref_knowledge | OPEA Documentation | your_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)") curl http://${host_ip}:7000/v1/retrieval \ -X POST \ -d "{\"text\":\"test\",\"embedding\":${your_embedding}}" \ -H 'Content-Type: application/json'
#### TEI Reranking Service
Skip for ChatQnA without... | your_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)") curl http://${host_ip}:7000/v1/retrieval \ -X POST \ -d "{\"text\":\"test\",\"embedding\":${your_embedding}}" \ -H 'Content-Type: application/json'
#### TEI Reranking Service
Skip for ChatQnA without... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
cd26f82e-bb81-4310-aa3f-29d00696a7b6 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 1 | opea-semantic-v1 | 1c565a57f386d86a | and Gaudi accelerator and AMD’s ROCm), in environments such as Docker and Kubernetes, including how to customize an application pipeline using different vector database backends.
## Add new VectorDB to ChatQnA Example | ai_ref_knowledge | OPEA Documentation | and Gaudi accelerator and AMD’s ROCm), in environments such as Docker and Kubernetes, including how to customize an application pipeline using different vector database backends.
## Add new VectorDB to ChatQnA Example | and Gaudi accelerator and AMD’s ROCm), in environments such as Docker and Kubernetes, including how to customize an application pipeline using different vector database backends.
## Add new VectorDB to ChatQnA Example | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
cd41faf3-3733-4025-a524-5d4a99506b59 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 29 | opea-semantic-v1 | 01a7093352e088e9 | QuickStart: 3.Consume the ChatQnA Service ```bash curl http://${host_ip}:8888/v1/chatqna \ -H "Content-Type: application/json" \ -d '{ "messages": "What is the revenue of Nike in 2023?" }'
### Build Docker Images | ai_ref_knowledge | OPEA Documentation | QuickStart: 3.Consume the ChatQnA Service ```bash curl http://${host_ip}:8888/v1/chatqna \ -H "Content-Type: application/json" \ -d '{ "messages": "What is the revenue of Nike in 2023?" }'
### Build Docker Images | QuickStart: 3.Consume the ChatQnA Service ```bash curl http://${host_ip}:8888/v1/chatqna \ -H "Content-Type: application/json" \ -d '{ "messages": "What is the revenue of Nike in 2023?" }'
### Build Docker Images | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
cda6f1b4-04c4-4296-aff9-384cf85b4806 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 18 | opea-semantic-v1 | df66f1a374940bdb | ### Build Mega Service of ChatQnA (with VectorDB)
This document outlines the deployment process for a ChatQnA application utilizing the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline on Intel Xeon server. The steps include Docker image creation, container deployment via Docker Compos... | ai_ref_knowledge | OPEA Documentation | ### Build Mega Service of ChatQnA (with VectorDB)
This document outlines the deployment process for a ChatQnA application utilizing the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline on Intel Xeon server. The steps include Docker image creation, container deployment via Docker Compos... | ### Build Mega Service of ChatQnA (with VectorDB)
This document outlines the deployment process for a ChatQnA application utilizing the [GenAIComps](https://github.com/opea-project/GenAIComps.git) microservice pipeline on Intel Xeon server. The steps include Docker image creation, container deployment via Docker Compos... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ce498a40-3a41-4f4c-bba4-ab66f5b2d4c2 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 49 | opea-semantic-v1 | 5194378e290c7522 | the LLM service. ```bash export HF_TOKEN=${your_hf_token} export model_path="/path/to/model" docker run -p 8008:80 -v $model_path:/data --name vllm-service --shm-size 128g opea/vllm:latest --model /data --host 0.0.0.0 --port 80
#### Setup Environment Variables
1. Set the required environment variables: | ai_ref_knowledge | OPEA Documentation | the LLM service. ```bash export HF_TOKEN=${your_hf_token} export model_path="/path/to/model" docker run -p 8008:80 -v $model_path:/data --name vllm-service --shm-size 128g opea/vllm:latest --model /data --host 0.0.0.0 --port 80
#### Setup Environment Variables
1. Set the required environment variables: | the LLM service. ```bash export HF_TOKEN=${your_hf_token} export model_path="/path/to/model" docker run -p 8008:80 -v $model_path:/data --name vllm-service --shm-size 128g opea/vllm:latest --model /data --host 0.0.0.0 --port 80
#### Setup Environment Variables
1. Set the required environment variables: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
cf2c6740-aaef-4abe-bcaa-1f7c712bd5a9 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 46 | opea-semantic-v1 | 50ce1fc9deb13add | | Service | Model | | --------- | ----------------------------------- | | Embedding | BAAI/bge-base-en-v1.5 | | Reranking | BAAI/bge-reranker-base | | LLM | meta-llama/Meta-Llama-3-8B-Instruct |
Change the `xxx_MODEL_ID` below for your needs. For users in China who are unable to download models directly from Huggingfac... | ai_ref_knowledge | OPEA Documentation | | Service | Model | | --------- | ----------------------------------- | | Embedding | BAAI/bge-base-en-v1.5 | | Reranking | BAAI/bge-reranker-base | | LLM | meta-llama/Meta-Llama-3-8B-Instruct |
Change the `xxx_MODEL_ID` below for your needs. For users in China who are unable to download models directly from Huggingfac... | | Service | Model | | --------- | ----------------------------------- | | Embedding | BAAI/bge-base-en-v1.5 | | Reranking | BAAI/bge-reranker-base | | LLM | meta-llama/Meta-Llama-3-8B-Instruct |
Change the `xxx_MODEL_ID` below for your needs. For users in China who are unable to download models directly from Huggingfac... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
cf9f1493-ed6f-47ce-a235-a3c6b59141e8 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 16 | opea-semantic-v1 | c8b719f05a9aa305 | README_<Vector_DB>.md adds details to start the Mega service of ChatQnA on Xeon in respective folders
README_<Vector_DB>.md adds details to start the Mega service of ChatQnA on Gaudi in respective folders. | ai_ref_knowledge | OPEA Documentation | README_<Vector_DB>.md adds details to start the Mega service of ChatQnA on Xeon in respective folders
README_<Vector_DB>.md adds details to start the Mega service of ChatQnA on Gaudi in respective folders. | README_<Vector_DB>.md adds details to start the Mega service of ChatQnA on Xeon in respective folders
README_<Vector_DB>.md adds details to start the Mega service of ChatQnA on Gaudi in respective folders. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
cfb37e0b-b2b9-4292-8e28-0e9b7bbe73f3 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 69 | opea-semantic-v1 | 6677e73190815939 | ```bash curl http://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{ "messages": "What is the revenue of Nike in 2023?" }'
#### Nginx Service | ai_ref_knowledge | OPEA Documentation | ```bash curl http://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{ "messages": "What is the revenue of Nike in 2023?" }'
#### Nginx Service | ```bash curl http://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{ "messages": "What is the revenue of Nike in 2023?" }'
#### Nginx Service | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d131a844-762c-4dfd-baa6-5cbb73c7948d | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 54 | opea-semantic-v1 | ed8cd361756b960d | 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
cd GenAIExamples/ChatQnA/docker_compose/intel/cpu/xeon/ | ai_ref_knowledge | OPEA Documentation | 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
cd GenAIExamples/ChatQnA/docker_compose/intel/cpu/xeon/ | 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
cd GenAIExamples/ChatQnA/docker_compose/intel/cpu/xeon/ | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d1772f13-86a5-48ee-812c-5f38c624eff3 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 45 | opea-semantic-v1 | a1ea3ff43a9f5bdc | ### Start Microservices #### Required Models By default, the embedding, reranking and LLM models are set to a default value as listed below:
| Service | Model |
| --------- | ----------------------------------- |
| Embedding | BAAI/bge-base-en-v1.5 |
| Reranking | BAAI/bge-reranker-base |
| LLM | meta-llama/Meta-Llama-... | ai_ref_knowledge | OPEA Documentation | ### Start Microservices #### Required Models By default, the embedding, reranking and LLM models are set to a default value as listed below:
| Service | Model |
| --------- | ----------------------------------- |
| Embedding | BAAI/bge-base-en-v1.5 |
| Reranking | BAAI/bge-reranker-base |
| LLM | meta-llama/Meta-Llama-... | ### Start Microservices #### Required Models By default, the embedding, reranking and LLM models are set to a default value as listed below:
| Service | Model |
| --------- | ----------------------------------- |
| Embedding | BAAI/bge-base-en-v1.5 |
| Reranking | BAAI/bge-reranker-base |
| LLM | meta-llama/Meta-Llama-... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d7369204-e9b9-4b7f-b151-717fcedd0310 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 8 | opea-semantic-v1 | 6829d559b74af48a | ### Prerequisites
We start by cloning the GenAIExamples and GenAIComps projects. GenAIComps is the fundamental and necessary component used to build the examples examples you find in GenAIExamples and deploy them as microservices. Next, set an environment variable for the desired release version with the **number only*... | ai_ref_knowledge | OPEA Documentation | ### Prerequisites
We start by cloning the GenAIExamples and GenAIComps projects. GenAIComps is the fundamental and necessary component used to build the examples examples you find in GenAIExamples and deploy them as microservices. Next, set an environment variable for the desired release version with the **number only*... | ### Prerequisites
We start by cloning the GenAIExamples and GenAIComps projects. GenAIComps is the fundamental and necessary component used to build the examples examples you find in GenAIExamples and deploy them as microservices. Next, set an environment variable for the desired release version with the **number only*... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d88e51f6-c882-444a-90ed-b4a258dcbf1d | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 41 | opea-semantic-v1 | 54035d6a687aa396 | ```bash cd GenAIExamples/ChatQnA/ui docker build --no-cache -t opea/chatqna-conversation-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile.react .
#### Build Nginx Docker Image | ai_ref_knowledge | OPEA Documentation | ```bash cd GenAIExamples/ChatQnA/ui docker build --no-cache -t opea/chatqna-conversation-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile.react .
#### Build Nginx Docker Image | ```bash cd GenAIExamples/ChatQnA/ui docker build --no-cache -t opea/chatqna-conversation-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile.react .
#### Build Nginx Docker Image | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d8c15716-e154-4eb0-be0c-94245d4ab1d6 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 68 | opea-semantic-v1 | 0db09bf7f36f9838 | #### MegaService
```bash
curl http://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{
"messages": "What is the revenue of Nike in 2023?"
}' | ai_ref_knowledge | OPEA Documentation | #### MegaService
```bash
curl http://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{
"messages": "What is the revenue of Nike in 2023?"
}' | #### MegaService
```bash
curl http://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{
"messages": "What is the revenue of Nike in 2023?"
}' | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e08ed34c-b409-4b20-a2f2-a101dca3ed5e | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 35 | opea-semantic-v1 | f95e7e021209833f | ```bash git clone https://github.com/opea-project/GenAIExamples.git cd GenAIExamples/ChatQnA docker build --no-cache -t opea/chatqna:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .
Option 2. MegaService without Rerank
To construct the Mega Service without Rerank, we utili... | ai_ref_knowledge | OPEA Documentation | ```bash git clone https://github.com/opea-project/GenAIExamples.git cd GenAIExamples/ChatQnA docker build --no-cache -t opea/chatqna:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .
Option 2. MegaService without Rerank
To construct the Mega Service without Rerank, we utili... | ```bash git clone https://github.com/opea-project/GenAIExamples.git cd GenAIExamples/ChatQnA docker build --no-cache -t opea/chatqna:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f Dockerfile .
Option 2. MegaService without Rerank
To construct the Mega Service without Rerank, we utili... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e0cbee4d-ca5a-4048-bc4d-99bfe0edb1b9 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 23 | opea-semantic-v1 | 90af30db0c3cc7c2 | ```bash # Example: host_ip="192.168.1.1" export host_ip="External_Public_IP" export HUGGINGFACEHUB_API_TOKEN="Your_Huggingface_API_Token"
##### 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"
# Exa... | 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"
##### 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"
# Exa... | ```bash # Example: host_ip="192.168.1.1" export host_ip="External_Public_IP" export HUGGINGFACEHUB_API_TOKEN="Your_Huggingface_API_Token"
##### 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"
# Exa... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e0d0074e-08b2-43aa-959e-3b8960a494e0 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 64 | opea-semantic-v1 | a5e80835cfc64258 | ```bash docker logs vllm-service 2>&1 | grep complete
If the service is ready, you will get the response like below. ```text
INFO: Application startup complete. | ai_ref_knowledge | OPEA Documentation | ```bash docker logs vllm-service 2>&1 | grep complete
If the service is ready, you will get the response like below. ```text
INFO: Application startup complete. | ```bash docker logs vllm-service 2>&1 | grep complete
If the service is ready, you will get the response like below. ```text
INFO: Application startup complete. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e1c9c299-ef4d-4e3b-b90f-858b8147f18f | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 80 | opea-semantic-v1 | e28763a045e60de4 | 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 "$CONTENT" "Data preparation succeeded" "dataprep-<Vector_DB>-service-xeon... | ai_ref_knowledge | OPEA Documentation | echo "===========Ingest data==================" local CONTENT=$(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 "$CONTENT" "Data preparation succeeded" "dataprep-<Vector_DB>-service-xeon... | echo "===========Ingest data==================" local CONTENT=$(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-xeon... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e2568a29-2126-48f6-8ee8-676f4628cc3a | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 63 | opea-semantic-v1 | 456f16bd4678dad3 | and warm up the model. After it's finished, the service will be ready. Try the command below to check whether the LLM serving is ready.
```bash
docker logs vllm-service 2>&1 | grep complete | ai_ref_knowledge | OPEA Documentation | and warm up the model. After it's finished, the service will be ready. Try the command below to check whether the LLM serving is ready.
```bash
docker logs vllm-service 2>&1 | grep complete | and warm up the model. After it's finished, the service will be ready. Try the command below to check whether the LLM serving is ready.
```bash
docker logs vllm-service 2>&1 | grep complete | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e44b0968-58ec-4b1c-aa46-f57fde0643ec | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 27 | opea-semantic-v1 | 439cf20df64d407e | ```bash docker pull opea/chatqna:latest docker pull opea/chatqna-ui:latest
Note: You should build docker image from source by yourself if:
- You are developing off the git main branch (as the container's ports in the repo may be different from the published docker image). - You can't download the docker image. - You wa... | ai_ref_knowledge | OPEA Documentation | ```bash docker pull opea/chatqna:latest docker pull opea/chatqna-ui:latest
Note: You should build docker image from source by yourself if:
- You are developing off the git main branch (as the container's ports in the repo may be different from the published docker image). - You can't download the docker image. - You wa... | ```bash docker pull opea/chatqna:latest docker pull opea/chatqna-ui:latest
Note: You should build docker image from source by yourself if:
- You are developing off the git main branch (as the container's ports in the repo may be different from the published docker image). - You can't download the docker image. - You wa... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ea3b2df7-0a7a-4d96-af04-02a79899d8a3 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 38 | opea-semantic-v1 | 663032c06346466e | #### Build UI Docker Image Build frontend Docker image via below command:
```bash
cd GenAIExamples/ChatQnA/ui
docker build --no-cache -t opea/chatqna-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile . | ai_ref_knowledge | OPEA Documentation | #### Build UI Docker Image Build frontend Docker image via below command:
```bash
cd GenAIExamples/ChatQnA/ui
docker build --no-cache -t opea/chatqna-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile . | #### Build UI Docker Image Build frontend Docker image via below command:
```bash
cd GenAIExamples/ChatQnA/ui
docker build --no-cache -t opea/chatqna-ui:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f ./docker/Dockerfile . | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ec65f8f3-135c-444c-80f6-163db7f9905d | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 82 | opea-semantic-v1 | c5a1984413f51a56 | local CONTENT=$(http_proxy="" curl http://${ip_address}:8889/v1/retrievaltool -X POST -H "Content-Type: application/json" -d '{ "text": "Explain the OPEA project?" }')
## Tests for Gaudi | ai_ref_knowledge | OPEA Documentation | local CONTENT=$(http_proxy="" curl http://${ip_address}:8889/v1/retrievaltool -X POST -H "Content-Type: application/json" -d '{ "text": "Explain the OPEA project?" }')
## Tests for Gaudi | local CONTENT=$(http_proxy="" curl http://${ip_address}:8889/v1/retrievaltool -X POST -H "Content-Type: application/json" -d '{ "text": "Explain the OPEA project?" }')
## Tests for Gaudi | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f39dc1b6-87a3-40c3-ae11-759fc216ca1a | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 11 | opea-semantic-v1 | d2faa8f655f218c4 | # GenAIComps git clone https://github.com/opea-project/GenAIComps.git cd GenAIComps git checkout tags/v${RELEASE_VERSION} cd .. # GenAIExamples git clone https://github.com/opea-project/GenAIExamples.git cd GenAIExamples git checkout tags/v${RELEASE_VERSION} cd ..
To customize ChatQnA with the new VectorDB the changes ... | ai_ref_knowledge | OPEA Documentation | # GenAIComps git clone https://github.com/opea-project/GenAIComps.git cd GenAIComps git checkout tags/v${RELEASE_VERSION} cd .. # GenAIExamples git clone https://github.com/opea-project/GenAIExamples.git cd GenAIExamples git checkout tags/v${RELEASE_VERSION} cd ..
To customize ChatQnA with the new VectorDB the changes ... | # GenAIComps git clone https://github.com/opea-project/GenAIComps.git cd GenAIComps git checkout tags/v${RELEASE_VERSION} cd .. # GenAIExamples git clone https://github.com/opea-project/GenAIExamples.git cd GenAIExamples git checkout tags/v${RELEASE_VERSION} cd ..
To customize ChatQnA with the new VectorDB the changes ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f647550d-3c95-4291-9a51-bd1599f175eb | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 0 | opea-semantic-v1 | 97fc6e67b55d6445 | # Customize the VectorDB for the ChatQnA Example
The OPEA sub-project [GenAIExamples](https://github.com/opea-project/GenAIExamples) houses multiple GenAI RAG sample applications such as chatbots, document summarization, code generation, and code translation to name a few. The [ChatQnA application](https://github.com/o... | ai_ref_knowledge | OPEA Documentation | # Customize the VectorDB for the ChatQnA Example
The OPEA sub-project [GenAIExamples](https://github.com/opea-project/GenAIExamples) houses multiple GenAI RAG sample applications such as chatbots, document summarization, code generation, and code translation to name a few. The [ChatQnA application](https://github.com/o... | # Customize the VectorDB for the ChatQnA Example
The OPEA sub-project [GenAIExamples](https://github.com/opea-project/GenAIExamples) houses multiple GenAI RAG sample applications such as chatbots, document summarization, code generation, and code translation to name a few. The [ChatQnA application](https://github.com/o... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
fc19d1f8-a391-4365-87a1-af6729302b32 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/add_vector_db.md | unknown | 5d03d65f-13b1-4d5c-96a7-8ed3a9c48bcb | 25 | opea-semantic-v1 | cda65ea34f4e3324 | ##### Set up other environment variables, make sure to update the INDEX_NAME variable to Pinecone index value: ```bash source ./set_env.sh
#### Quick Start: 2.Run Docker Compose
```bash
docker compose -f compose_<Vector_DB>.yaml up -d | ai_ref_knowledge | OPEA Documentation | ##### Set up other environment variables, make sure to update the INDEX_NAME variable to Pinecone index value: ```bash source ./set_env.sh
#### Quick Start: 2.Run Docker Compose
```bash
docker compose -f compose_<Vector_DB>.yaml up -d | ##### Set up other environment variables, make sure to update the INDEX_NAME variable to Pinecone index value: ```bash source ./set_env.sh
#### Quick Start: 2.Run Docker Compose
```bash
docker compose -f compose_<Vector_DB>.yaml up -d | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
00e4db7c-73c1-4b81-abb8-e8e04f7aa59a | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 79 | opea-semantic-v1 | 474d171dc5259587 | the port mapping in the `compose.yaml` file as shown below: ```yaml chatqna-aipc-ui-server: image: opea/chatqna-ui${TAG:-latest} ... ports: - "YOUR_HOST_PORT:5173" # Change YOUR_HOST_PORT to the desired port
After making this change, rebuild and restart the containers for the change to take effect. | ai_ref_knowledge | OPEA Documentation | the port mapping in the `compose.yaml` file as shown below: ```yaml chatqna-aipc-ui-server: image: opea/chatqna-ui${TAG:-latest} ... ports: - "YOUR_HOST_PORT:5173" # Change YOUR_HOST_PORT to the desired port
After making this change, rebuild and restart the containers for the change to take effect. | the port mapping in the `compose.yaml` file as shown below: ```yaml chatqna-aipc-ui-server: image: opea/chatqna-ui${TAG:-latest} ... ports: - "YOUR_HOST_PORT:5173" # Change YOUR_HOST_PORT to the desired port
After making this change, rebuild and restart the containers for the change to take effect. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
016dd9cb-c511-4d67-ba46-e1e4af12e081 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 48 | opea-semantic-v1 | e86978611f08d6c7 | embedding model used is `BAAI/bge-base-en-v1.5`, which has a vector size of 768. Therefore, the output of the curl command is a vector of length 768.
### Retriever Microservice | ai_ref_knowledge | OPEA Documentation | embedding model used is `BAAI/bge-base-en-v1.5`, which has a vector size of 768. Therefore, the output of the curl command is a vector of length 768.
### Retriever Microservice | embedding model used is `BAAI/bge-base-en-v1.5`, which has a vector size of 768. Therefore, the output of the curl command is a vector of length 768.
### Retriever Microservice | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0462210f-b9da-45c6-9869-f69bb631c908 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 0 | opea-semantic-v1 | 5afcf2cca6b27acc | # Single node on-prem deployment with Ollama on AIPC
This section covers single-node on-prem deployment of the ChatQnA example using Ollama. There are several ways to enable RAG with vectordb and LLM models, but this tutorial will be covering how to build an end-to-end ChatQnA pipeline with the Redis vector database an... | ai_ref_knowledge | OPEA Documentation | # Single node on-prem deployment with Ollama on AIPC
This section covers single-node on-prem deployment of the ChatQnA example using Ollama. There are several ways to enable RAG with vectordb and LLM models, but this tutorial will be covering how to build an end-to-end ChatQnA pipeline with the Redis vector database an... | # Single node on-prem deployment with Ollama on AIPC
This section covers single-node on-prem deployment of the ChatQnA example using Ollama. There are several ways to enable RAG with vectordb and LLM models, but this tutorial will be covering how to build an end-to-end ChatQnA pipeline with the Redis vector database an... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0afc6b4e-db0e-4c56-abdd-c336eb23bc24 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 43 | opea-semantic-v1 | 206950fb024bbcda | :::{tab-item} Ollama
```bash
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
5db065a9fdf9 opea/chatqna-ui:latest "docker-entrypoint.s…" 29 seconds ago Up 25 seconds 0.0.0.0:5173->5173/tcp, :::5173->5173/tcp chatqna-aipc-ui-server
6fa87927d00c opea/chatqna:latest "python chatqna.py" 29 seconds ago Up 25 seconds 0.... | ai_ref_knowledge | OPEA Documentation | :::{tab-item} Ollama
```bash
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
5db065a9fdf9 opea/chatqna-ui:latest "docker-entrypoint.s…" 29 seconds ago Up 25 seconds 0.0.0.0:5173->5173/tcp, :::5173->5173/tcp chatqna-aipc-ui-server
6fa87927d00c opea/chatqna:latest "python chatqna.py" 29 seconds ago Up 25 seconds 0.... | :::{tab-item} Ollama
```bash
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
5db065a9fdf9 opea/chatqna-ui:latest "docker-entrypoint.s…" 29 seconds ago Up 25 seconds 0.0.0.0:5173->5173/tcp, :::5173->5173/tcp chatqna-aipc-ui-server
6fa87927d00c opea/chatqna:latest "python chatqna.py" 29 seconds ago Up 25 seconds 0.... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0cfad2cd-7127-48f0-8345-c8b63b2c96c1 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 74 | opea-semantic-v1 | 18112855e62b5791 | Here is the output for reference:
```bash
data: b'\n'
data: b'An'
data: b'swer'
data: b':'
data: b' In'
data: b' fiscal'
data: b' '
data: b'2'
data: b'0'
data: b'2'
data: b'3'
data: b','
data: b' N'
data: b'I'
data: b'KE'
data: b','
data: b' Inc'
data: b'.'
data: b' achieved'
data: b' record'
data: b' Rev'
data: b'en'
... | ai_ref_knowledge | OPEA Documentation | Here is the output for reference:
```bash
data: b'\n'
data: b'An'
data: b'swer'
data: b':'
data: b' In'
data: b' fiscal'
data: b' '
data: b'2'
data: b'0'
data: b'2'
data: b'3'
data: b','
data: b' N'
data: b'I'
data: b'KE'
data: b','
data: b' Inc'
data: b'.'
data: b' achieved'
data: b' record'
data: b' Rev'
data: b'en'
... | Here is the output for reference:
```bash
data: b'\n'
data: b'An'
data: b'swer'
data: b':'
data: b' In'
data: b' fiscal'
data: b' '
data: b'2'
data: b'0'
data: b'2'
data: b'3'
data: b','
data: b' N'
data: b'I'
data: b'KE'
data: b','
data: b' Inc'
data: b'.'
data: b' achieved'
data: b' record'
data: b' Rev'
data: b'en'
... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0ea4cf09-5129-4b79-b827-adabbb74d363 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 30 | opea-semantic-v1 | 21a05ed53ae948ec | associated tools. The tools and models listed in the table can be configured via environment variables in either the `set_env.sh` script or the `compose.yaml` file.
::::{tab-set} | ai_ref_knowledge | OPEA Documentation | associated tools. The tools and models listed in the table can be configured via environment variables in either the `set_env.sh` script or the `compose.yaml` file.
::::{tab-set} | associated tools. The tools and models listed in the table can be configured via environment variables in either the `set_env.sh` script or the `compose.yaml` file.
::::{tab-set} | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
15464867-c5d7-4efc-a471-bd6b54f244f3 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 34 | opea-semantic-v1 | 1fcbfccc2d94e80a | models, modify `set_env.sh` before running it. For example, the environment variable `LLM_MODEL_ID` can be changed to another model by specifying the HuggingFace model card ID.
```bash
cd $WORKSPACE/GenAIExamples/ChatQnA/docker_compose/intel/cpu/aipc
source ./set_env.sh | ai_ref_knowledge | OPEA Documentation | models, modify `set_env.sh` before running it. For example, the environment variable `LLM_MODEL_ID` can be changed to another model by specifying the HuggingFace model card ID.
```bash
cd $WORKSPACE/GenAIExamples/ChatQnA/docker_compose/intel/cpu/aipc
source ./set_env.sh | models, modify `set_env.sh` before running it. For example, the environment variable `LLM_MODEL_ID` can be changed to another model by specifying the HuggingFace model card ID.
```bash
cd $WORKSPACE/GenAIExamples/ChatQnA/docker_compose/intel/cpu/aipc
source ./set_env.sh | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1580f68c-8b21-4402-bbf2-952a069ca774 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 17 | opea-semantic-v1 | 2aed832bb879cfb0 | #### Set Ollama Service Configuration
The Ollama Service Configuration file is /etc/systemd/system/ollama.service. Edit the file to set OLLAMA_HOST environment, replacing <host_ip> with the hosts IPV4 external public IP address. For example, if the host_ip is 10.132.x.y, then `Environment="OLLAMA_HOST=10.132.x.y:11434"... | ai_ref_knowledge | OPEA Documentation | #### Set Ollama Service Configuration
The Ollama Service Configuration file is /etc/systemd/system/ollama.service. Edit the file to set OLLAMA_HOST environment, replacing <host_ip> with the hosts IPV4 external public IP address. For example, if the host_ip is 10.132.x.y, then `Environment="OLLAMA_HOST=10.132.x.y:11434"... | #### Set Ollama Service Configuration
The Ollama Service Configuration file is /etc/systemd/system/ollama.service. Edit the file to set OLLAMA_HOST environment, replacing <host_ip> with the hosts IPV4 external public IP address. For example, if the host_ip is 10.132.x.y, then `Environment="OLLAMA_HOST=10.132.x.y:11434"... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1690aad5-d767-4738-a75b-246e30937bbd | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 76 | opea-semantic-v1 | 895b458acd82d4f2 | ### NGINX Service
This will ensure the NGINX service is working properly. ```bash
curl http://${host_ip}:${NGINX_PORT}/v1/chatqna \
-H "Content-Type: application/json" \
-d '{"messages": "What is the revenue of Nike in 2023?"}' | ai_ref_knowledge | OPEA Documentation | ### NGINX Service
This will ensure the NGINX service is working properly. ```bash
curl http://${host_ip}:${NGINX_PORT}/v1/chatqna \
-H "Content-Type: application/json" \
-d '{"messages": "What is the revenue of Nike in 2023?"}' | ### NGINX Service
This will ensure the NGINX service is working properly. ```bash
curl http://${host_ip}:${NGINX_PORT}/v1/chatqna \
-H "Content-Type: application/json" \
-d '{"messages": "What is the revenue of Nike in 2023?"}' | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
185eaff2-4c28-4732-af36-44d910e7fd27 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 73 | opea-semantic-v1 | 56fef78f5e18429f | ```bash curl http://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{ "model": "'"${OLLAMA_MODEL}"'", "messages": "What is the revenue of Nike in 2023?" }'
Here is the output for reference: | ai_ref_knowledge | OPEA Documentation | ```bash curl http://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{ "model": "'"${OLLAMA_MODEL}"'", "messages": "What is the revenue of Nike in 2023?" }'
Here is the output for reference: | ```bash curl http://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{ "model": "'"${OLLAMA_MODEL}"'", "messages": "What is the revenue of Nike in 2023?" }'
Here is the output for reference: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
19adfe6a-8165-4655-8881-00fd2117ef5a | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 75 | opea-semantic-v1 | b0b406fc67fdb2a5 | data: b'ues' data: b' of' data: b' $' data: b'5' data: b'1' data: b'.' data: b'2' data: b' billion' data: b'.' data: b'</s>' data: [DONE]
### NGINX Service | ai_ref_knowledge | OPEA Documentation | data: b'ues' data: b' of' data: b' $' data: b'5' data: b'1' data: b'.' data: b'2' data: b' billion' data: b'.' data: b'</s>' data: [DONE]
### NGINX Service | data: b'ues' data: b' of' data: b' $' data: b'5' data: b'1' data: b'.' data: b'2' data: b' billion' data: b'.' data: b'</s>' data: [DONE]
### NGINX Service | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
22c1e372-41bb-4f33-b9bc-5b3655628400 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 45 | opea-semantic-v1 | 07a416bd7da5f0e0 | ### TEI Embedding Service
The TEI embedding service takes in a string as input, embeds the string into a vector of a specific length determined by the embedding model, and returns this vector. | ai_ref_knowledge | OPEA Documentation | ### TEI Embedding Service
The TEI embedding service takes in a string as input, embeds the string into a vector of a specific length determined by the embedding model, and returns this vector. | ### TEI Embedding Service
The TEI embedding service takes in a string as input, embeds the string into a vector of a specific length determined by the embedding model, and returns this vector. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
22cd5491-6b76-4c25-b962-580377672a16 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 68 | opea-semantic-v1 | 304b7027c3dbcffc | To delete the file or link, use the following commands:
#### Delete link
```bash
# The dataprep service will add a .txt postfix for link file | ai_ref_knowledge | OPEA Documentation | To delete the file or link, use the following commands:
#### Delete link
```bash
# The dataprep service will add a .txt postfix for link file | To delete the file or link, use the following commands:
#### Delete link
```bash
# The dataprep service will add a .txt postfix for link file | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
22e1b56f-75ed-4309-9107-0021f7cc529e | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 9 | opea-semantic-v1 | 871a3c7d6d606cd3 | Set up a [HuggingFace](https://huggingface.co/) account and generate a [user access token](https://huggingface.co/docs/transformers.js/en/guides/private#step-1-generating-a-user-access-token). Request access to the [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) model.
... | ai_ref_knowledge | OPEA Documentation | Set up a [HuggingFace](https://huggingface.co/) account and generate a [user access token](https://huggingface.co/docs/transformers.js/en/guides/private#step-1-generating-a-user-access-token). Request access to the [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) model.
... | Set up a [HuggingFace](https://huggingface.co/) account and generate a [user access token](https://huggingface.co/docs/transformers.js/en/guides/private#step-1-generating-a-user-access-token). Request access to the [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) model.
... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
26136672-6d62-430b-b6b4-3d238d264b96 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 61 | opea-semantic-v1 | a020fece51e4b34d | {"model":"llama3","created_at":"2024-09-05T08:47:21.129254135Z","response":" a","done":false} {"model":"llama3","created_at":"2024-09-05T08:47:22.066555829Z","response":" sub","done":false} {"model":"llama3","created_at":"2024-09-05T08:47:22.993695854Z","response":"field","done":false} {"model":"llama3","created_at":"2... | ai_ref_knowledge | OPEA Documentation | {"model":"llama3","created_at":"2024-09-05T08:47:21.129254135Z","response":" a","done":false} {"model":"llama3","created_at":"2024-09-05T08:47:22.066555829Z","response":" sub","done":false} {"model":"llama3","created_at":"2024-09-05T08:47:22.993695854Z","response":"field","done":false} {"model":"llama3","created_at":"2... | {"model":"llama3","created_at":"2024-09-05T08:47:21.129254135Z","response":" a","done":false} {"model":"llama3","created_at":"2024-09-05T08:47:22.066555829Z","response":" sub","done":false} {"model":"llama3","created_at":"2024-09-05T08:47:22.993695854Z","response":"field","done":false} {"model":"llama3","created_at":"2... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
279a6633-245d-4f04-bdfa-cc285ba656c5 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 50 | opea-semantic-v1 | a629e188b08aa2a2 | the embedding vector is determined by the embedding model. The model is set with the environment variable EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5", which has a vector size of 768.
Check the vector dimension of the embedding model used and set `your_embedding` dimension equal to it. | ai_ref_knowledge | OPEA Documentation | the embedding vector is determined by the embedding model. The model is set with the environment variable EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5", which has a vector size of 768.
Check the vector dimension of the embedding model used and set `your_embedding` dimension equal to it. | the embedding vector is determined by the embedding model. The model is set with the environment variable EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5", which has a vector size of 768.
Check the vector dimension of the embedding model used and set `your_embedding` dimension equal to it. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
288b099e-1be8-4fdb-ae90-501e5bbd24df | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 44 | opea-semantic-v1 | a6dbba722dfc258d | 051e0d68e263 ghcr.io/huggingface/text-embeddings-inference:cpu-1.5 "text-embeddings-rou…" 29 seconds ago Up 27 seconds 0.0.0.0:6006->80/tcp, :::6006->80/tcp tei-embedding-server 632a6634b06b opea/llm-ollama "bash entrypoint.sh" 29 seconds ago Up 27 seconds 0.0.0.0:9000->9000/tcp, :::9000->9000/tcp llm-ollama
:::
:::: | ai_ref_knowledge | OPEA Documentation | 051e0d68e263 ghcr.io/huggingface/text-embeddings-inference:cpu-1.5 "text-embeddings-rou…" 29 seconds ago Up 27 seconds 0.0.0.0:6006->80/tcp, :::6006->80/tcp tei-embedding-server 632a6634b06b opea/llm-ollama "bash entrypoint.sh" 29 seconds ago Up 27 seconds 0.0.0.0:9000->9000/tcp, :::9000->9000/tcp llm-ollama
:::
:::: | 051e0d68e263 ghcr.io/huggingface/text-embeddings-inference:cpu-1.5 "text-embeddings-rou…" 29 seconds ago Up 27 seconds 0.0.0.0:6006->80/tcp, :::6006->80/tcp tei-embedding-server 632a6634b06b opea/llm-ollama "bash entrypoint.sh" 29 seconds ago Up 27 seconds 0.0.0.0:9000->9000/tcp, :::9000->9000/tcp llm-ollama
:::
:::: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
2abe253c-af48-4366-b7cc-0f58520cff9c | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 31 | opea-semantic-v1 | f8957dc68eed14f1 | :::{tab-item} Ollama
|use case components | Tools | Model | Service Type |
|---------------- |--------------|-----------------------------|-------|
|Data Prep | LangChain | NA |OPEA Microservice |
|VectorDB | Redis | NA |Open source service|
|Embedding | TEI | BAAI/bge-base-en-v1.5 |OPEA Microservice |
|Reranking | TEI... | ai_ref_knowledge | OPEA Documentation | :::{tab-item} Ollama
|use case components | Tools | Model | Service Type |
|---------------- |--------------|-----------------------------|-------|
|Data Prep | LangChain | NA |OPEA Microservice |
|VectorDB | Redis | NA |Open source service|
|Embedding | TEI | BAAI/bge-base-en-v1.5 |OPEA Microservice |
|Reranking | TEI... | :::{tab-item} Ollama
|use case components | Tools | Model | Service Type |
|---------------- |--------------|-----------------------------|-------|
|Data Prep | LangChain | NA |OPEA Microservice |
|VectorDB | Redis | NA |Open source service|
|Embedding | TEI | BAAI/bge-base-en-v1.5 |OPEA Microservice |
|Reranking | TEI... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
2b29f966-3b9f-4db2-a6c2-4471817b7ab0 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 69 | opea-semantic-v1 | 81bb624a1422d1bc | #### Delete link ```bash # The dataprep service will add a .txt postfix for link file
curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
-d '{"file_path": "https://opea.dev.txt"}' \
-H "Content-Type: application/json" | ai_ref_knowledge | OPEA Documentation | #### Delete link ```bash # The dataprep service will add a .txt postfix for link file
curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
-d '{"file_path": "https://opea.dev.txt"}' \
-H "Content-Type: application/json" | #### Delete link ```bash # The dataprep service will add a .txt postfix for link file
curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
-d '{"file_path": "https://opea.dev.txt"}' \
-H "Content-Type: application/json" | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
2decb154-d5fe-4b2c-a469-783677f409ac | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 41 | opea-semantic-v1 | 656f55a082796b93 | ### Check the container status
Check if all the containers launched via `docker compose` are running i.e. each container's `STATUS` is `Up` and `Healthy`. | ai_ref_knowledge | OPEA Documentation | ### Check the container status
Check if all the containers launched via `docker compose` are running i.e. each container's `STATUS` is `Up` and `Healthy`. | ### Check the container status
Check if all the containers launched via `docker compose` are running i.e. each container's `STATUS` is `Up` and `Healthy`. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
3a2e37c2-9162-454e-8c38-776a179b6944 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 33 | opea-semantic-v1 | 394068fd96608a28 | ::: ::::
Set the necessary environment variables to set up the use case. To swap out models, modify `set_env.sh` before running it. For example, the environment variable `LLM_MODEL_ID` can be changed to another model by specifying the HuggingFace model card ID. | ai_ref_knowledge | OPEA Documentation | ::: ::::
Set the necessary environment variables to set up the use case. To swap out models, modify `set_env.sh` before running it. For example, the environment variable `LLM_MODEL_ID` can be changed to another model by specifying the HuggingFace model card ID. | ::: ::::
Set the necessary environment variables to set up the use case. To swap out models, modify `set_env.sh` before running it. For example, the environment variable `LLM_MODEL_ID` can be changed to another model by specifying the HuggingFace model card ID. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
3ec4d8e4-bb5c-40e6-834b-ae18090829b3 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 36 | opea-semantic-v1 | f782b739333cd5d5 | Run `docker compose` with the provided YAML file to start all the services mentioned above as containers.
::::{tab-set}
:::{tab-item} Ollama | ai_ref_knowledge | OPEA Documentation | Run `docker compose` with the provided YAML file to start all the services mentioned above as containers.
::::{tab-set}
:::{tab-item} Ollama | Run `docker compose` with the provided YAML file to start all the services mentioned above as containers.
::::{tab-set}
:::{tab-item} Ollama | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
3f1273bc-2f59-4699-ab92-a86450307b71 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 78 | opea-semantic-v1 | aa8b19af28c508ba | ## Launch UI
To access the frontend, open the following URL in a web browser: http://${host_ip}:${NGINX_PORT}. By default, the UI runs on port 5173 internally. A different host port can be used to access the frontend by modifying the port mapping in the `compose.yaml` file as shown below:
```yaml
chatqna-aipc-ui-serve... | ai_ref_knowledge | OPEA Documentation | ## Launch UI
To access the frontend, open the following URL in a web browser: http://${host_ip}:${NGINX_PORT}. By default, the UI runs on port 5173 internally. A different host port can be used to access the frontend by modifying the port mapping in the `compose.yaml` file as shown below:
```yaml
chatqna-aipc-ui-serve... | ## Launch UI
To access the frontend, open the following URL in a web browser: http://${host_ip}:${NGINX_PORT}. By default, the UI runs on port 5173 internally. A different host port can be used to access the frontend by modifying the port mapping in the `compose.yaml` file as shown below:
```yaml
chatqna-aipc-ui-serve... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
3fa10cdb-3b93-4217-a9d1-b519a1db9aa3 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 8 | opea-semantic-v1 | 3149abf6d943d88f | with the latest updates will be used. ```bash export RELEASE_VERSION=<Release_Version> # Set desired release version - number only cd GenAIExamples git checkout tags/v${RELEASE_VERSION} cd ..
Set up a [HuggingFace](https://huggingface.co/) account and generate a [user access token](https://huggingface.co/docs/transform... | ai_ref_knowledge | OPEA Documentation | with the latest updates will be used. ```bash export RELEASE_VERSION=<Release_Version> # Set desired release version - number only cd GenAIExamples git checkout tags/v${RELEASE_VERSION} cd ..
Set up a [HuggingFace](https://huggingface.co/) account and generate a [user access token](https://huggingface.co/docs/transform... | with the latest updates will be used. ```bash export RELEASE_VERSION=<Release_Version> # Set desired release version - number only cd GenAIExamples git checkout tags/v${RELEASE_VERSION} cd ..
Set up a [HuggingFace](https://huggingface.co/) account and generate a [user access token](https://huggingface.co/docs/transform... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
41925a4d-b1ea-461a-9f40-6cafd10a1e81 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 32 | opea-semantic-v1 | 46630512fb6ab437 | |Reranking | TEI | BAAI/bge-reranker-base | OPEA Microservice | |LLM | Ollama | llama3 |OPEA Microservice | |UI | | NA | Gateway Service |
:::
:::: | ai_ref_knowledge | OPEA Documentation | |Reranking | TEI | BAAI/bge-reranker-base | OPEA Microservice | |LLM | Ollama | llama3 |OPEA Microservice | |UI | | NA | Gateway Service |
:::
:::: | |Reranking | TEI | BAAI/bge-reranker-base | OPEA Microservice | |LLM | Ollama | llama3 |OPEA Microservice | |UI | | NA | Gateway Service |
:::
:::: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
43399bd9-d4aa-46da-aa02-a14fb9520d23 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 47 | opea-semantic-v1 | 1b87d12bdaecdca9 | ```bash curl ${host_ip}:6006/embed \ -X POST \ -d '{"inputs":"What is Deep Learning?"}' \ -H 'Content-Type: application/json'
In this example, the embedding model used is `BAAI/bge-base-en-v1.5`, which has a vector size of 768. Therefore, the output of the curl command is a vector of length 768. | ai_ref_knowledge | OPEA Documentation | ```bash curl ${host_ip}:6006/embed \ -X POST \ -d '{"inputs":"What is Deep Learning?"}' \ -H 'Content-Type: application/json'
In this example, the embedding model used is `BAAI/bge-base-en-v1.5`, which has a vector size of 768. Therefore, the output of the curl command is a vector of length 768. | ```bash curl ${host_ip}:6006/embed \ -X POST \ -d '{"inputs":"What is Deep Learning?"}' \ -H 'Content-Type: application/json'
In this example, the embedding model used is `BAAI/bge-base-en-v1.5`, which has a vector size of 768. Therefore, the output of the curl command is a vector of length 768. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4572c6be-ce7d-4ec8-9b92-6e474010ce07 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 29 | opea-semantic-v1 | 2637ea4afe0587b4 | ## Use Case Setup
ChatQnA will utilize the following GenAIComps services and associated tools. The tools and models listed in the table can be configured via environment variables in either the `set_env.sh` script or the `compose.yaml` file. | ai_ref_knowledge | OPEA Documentation | ## Use Case Setup
ChatQnA will utilize the following GenAIComps services and associated tools. The tools and models listed in the table can be configured via environment variables in either the `set_env.sh` script or the `compose.yaml` file. | ## Use Case Setup
ChatQnA will utilize the following GenAIComps services and associated tools. The tools and models listed in the table can be configured via environment variables in either the `set_env.sh` script or the `compose.yaml` file. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
45aa761b-5dab-4939-bad1-c9ad658e7f2c | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 46 | opea-semantic-v1 | 4e26d8ce469cea6c | takes in a string as input, embeds the string into a vector of a specific length determined by the embedding model, and returns this vector.
```bash
curl ${host_ip}:6006/embed \
-X POST \
-d '{"inputs":"What is Deep Learning?"}' \
-H 'Content-Type: application/json' | ai_ref_knowledge | OPEA Documentation | takes in a string as input, embeds the string into a vector of a specific length determined by the embedding model, and returns this vector.
```bash
curl ${host_ip}:6006/embed \
-X POST \
-d '{"inputs":"What is Deep Learning?"}' \
-H 'Content-Type: application/json' | takes in a string as input, embeds the string into a vector of a specific length determined by the embedding model, and returns this vector.
```bash
curl ${host_ip}:6006/embed \
-X POST \
-d '{"inputs":"What is Deep Learning?"}' \
-H 'Content-Type: application/json' | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4aaece57-0ea6-4eb4-a348-a2ab69f5f499 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 24 | opea-semantic-v1 | 14f349a4d6361306 | After downloading the models, list the models by executing the `ollama list` command.
The output should be similar to the following: | ai_ref_knowledge | OPEA Documentation | After downloading the models, list the models by executing the `ollama list` command.
The output should be similar to the following: | After downloading the models, list the models by executing the `ollama list` command.
The output should be similar to the following: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4c862fe4-da91-42e2-9580-6e38112cafea | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 67 | opea-semantic-v1 | edffcfe6fd9d535f | The list of uploaded files can be retrieved using this command: ```bash curl -X POST "http://${host_ip}:6007/v1/dataprep/get_file" \ -H "Content-Type: application/json"
To delete the file or link, use the following commands: | ai_ref_knowledge | OPEA Documentation | The list of uploaded files can be retrieved using this command: ```bash curl -X POST "http://${host_ip}:6007/v1/dataprep/get_file" \ -H "Content-Type: application/json"
To delete the file or link, use the following commands: | The list of uploaded files can be retrieved using this command: ```bash curl -X POST "http://${host_ip}:6007/v1/dataprep/get_file" \ -H "Content-Type: application/json"
To delete the file or link, use the following commands: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
537a80bd-33d1-4d17-bfe3-f0719ed3e68e | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 3 | opea-semantic-v1 | fb310747fa31762f | The OPEA GenAIComps microservices used to deploy a single node vLLM or TGI megaservice solution for ChatQnA are listed below:
1. Data Prep
2. Embedding
3. Retriever
4. Reranking
5. LLM with Ollama | ai_ref_knowledge | OPEA Documentation | The OPEA GenAIComps microservices used to deploy a single node vLLM or TGI megaservice solution for ChatQnA are listed below:
1. Data Prep
2. Embedding
3. Retriever
4. Reranking
5. LLM with Ollama | The OPEA GenAIComps microservices used to deploy a single node vLLM or TGI megaservice solution for ChatQnA are listed below:
1. Data Prep
2. Embedding
3. Retriever
4. Reranking
5. LLM with Ollama | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
56506738-4ca3-4af8-a0ee-b2361c1a0345 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 56 | opea-semantic-v1 | f0e8fbf01b98f840 | decreasing order of the similarity score. The document corresponding to the index with the highest score is the most relevant document for the input query.
```bash
curl http://${host_ip}:8808/rerank \
-X POST \
-d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \
-H ... | ai_ref_knowledge | OPEA Documentation | decreasing order of the similarity score. The document corresponding to the index with the highest score is the most relevant document for the input query.
```bash
curl http://${host_ip}:8808/rerank \
-X POST \
-d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \
-H ... | decreasing order of the similarity score. The document corresponding to the index with the highest score is the most relevant document for the input query.
```bash
curl http://${host_ip}:8808/rerank \
-X POST \
-d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \
-H ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5837d1eb-7781-4f96-aa9e-b01c6ca06042 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 4 | opea-semantic-v1 | 1b854a6185bed6d0 | 1. Data Prep 2. Embedding 3. Retriever 4. Reranking 5. LLM with Ollama
This solution is designed to demonstrate the use of Redis vectorDB for RAG and the Meta-Llama-3-8B-Instruct model for LLM inference on Intel Client PCs. The steps will involve setting up Docker containers, using a sample Nike dataset in PDF format, ... | ai_ref_knowledge | OPEA Documentation | 1. Data Prep 2. Embedding 3. Retriever 4. Reranking 5. LLM with Ollama
This solution is designed to demonstrate the use of Redis vectorDB for RAG and the Meta-Llama-3-8B-Instruct model for LLM inference on Intel Client PCs. The steps will involve setting up Docker containers, using a sample Nike dataset in PDF format, ... | 1. Data Prep 2. Embedding 3. Retriever 4. Reranking 5. LLM with Ollama
This solution is designed to demonstrate the use of Redis vectorDB for RAG and the Meta-Llama-3-8B-Instruct model for LLM inference on Intel Client PCs. The steps will involve setting up Docker containers, using a sample Nike dataset in PDF format, ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5b588359-3c50-4e18-bb10-723401779abd | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 11 | opea-semantic-v1 | de91c8738036ebca | The example requires setting the `host_ip` to "localhost" to deploy the microservices on endpoints enabled with ports. ```bash export host_ip="localhost"
Set the NGINX port. ```bash
# Example: NGINX_PORT=80
export NGINX_PORT=<Nginx_Port> | ai_ref_knowledge | OPEA Documentation | The example requires setting the `host_ip` to "localhost" to deploy the microservices on endpoints enabled with ports. ```bash export host_ip="localhost"
Set the NGINX port. ```bash
# Example: NGINX_PORT=80
export NGINX_PORT=<Nginx_Port> | The example requires setting the `host_ip` to "localhost" to deploy the microservices on endpoints enabled with ports. ```bash export host_ip="localhost"
Set the NGINX port. ```bash
# Example: NGINX_PORT=80
export NGINX_PORT=<Nginx_Port> | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5de91180-7240-42b5-9898-75f303b903af | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 19 | opea-semantic-v1 | 35bb52568659bf59 | #### Set https_proxy environment for Ollama
If the system's network is accessed through a proxy, add a https_proxy entry to the Ollama Service Configuration file:
```bash
Environment="https_proxy=Your_HTTPS_Proxy" | ai_ref_knowledge | OPEA Documentation | #### Set https_proxy environment for Ollama
If the system's network is accessed through a proxy, add a https_proxy entry to the Ollama Service Configuration file:
```bash
Environment="https_proxy=Your_HTTPS_Proxy" | #### Set https_proxy environment for Ollama
If the system's network is accessed through a proxy, add a https_proxy entry to the Ollama Service Configuration file:
```bash
Environment="https_proxy=Your_HTTPS_Proxy" | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6908f234-fffd-4b72-b363-7ad1e5c84f95 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 77 | opea-semantic-v1 | 693467e36925b4ef | ensure the NGINX service is working properly. ```bash curl http://${host_ip}:${NGINX_PORT}/v1/chatqna \ -H "Content-Type: application/json" \ -d '{"messages": "What is the revenue of Nike in 2023?"}'
The output will be similar to that of the ChatQnA megaservice. | ai_ref_knowledge | OPEA Documentation | ensure the NGINX service is working properly. ```bash curl http://${host_ip}:${NGINX_PORT}/v1/chatqna \ -H "Content-Type: application/json" \ -d '{"messages": "What is the revenue of Nike in 2023?"}'
The output will be similar to that of the ChatQnA megaservice. | ensure the NGINX service is working properly. ```bash curl http://${host_ip}:${NGINX_PORT}/v1/chatqna \ -H "Content-Type: application/json" \ -d '{"messages": "What is the revenue of Nike in 2023?"}'
The output will be similar to that of the ChatQnA megaservice. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6e586652-10d1-4737-b6f3-7f19d3fe74cd | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 54 | opea-semantic-v1 | cc1e4795a91640c3 | a list of top `n` retrieved documents relevant to the input query, and top_n where n refers to the number of documents to be returned.
The output is retrieved text that is relevant to the input data:
```bash
{"id":"b16024e140e78e39a60e8678622be630","retrieved_docs":[],"initial_query":"test","top_n":1,"metadata":[]} | ai_ref_knowledge | OPEA Documentation | a list of top `n` retrieved documents relevant to the input query, and top_n where n refers to the number of documents to be returned.
The output is retrieved text that is relevant to the input data:
```bash
{"id":"b16024e140e78e39a60e8678622be630","retrieved_docs":[],"initial_query":"test","top_n":1,"metadata":[]} | a list of top `n` retrieved documents relevant to the input query, and top_n where n refers to the number of documents to be returned.
The output is retrieved text that is relevant to the input data:
```bash
{"id":"b16024e140e78e39a60e8678622be630","retrieved_docs":[],"initial_query":"test","top_n":1,"metadata":[]} | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6eb0c42d-f33d-4adf-b874-0537a94394a4 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 6 | opea-semantic-v1 | ab5fd42f4284fdfa | ## Prerequisites
Set up a workspace and clone the [GenAIExamples](https://github.com/opea-project/GenAIExamples) GitHub repo. ```bash
export WORKSPACE=<Path>
cd $WORKSPACE
git clone https://github.com/opea-project/GenAIExamples.git # GenAIExamples | ai_ref_knowledge | OPEA Documentation | ## Prerequisites
Set up a workspace and clone the [GenAIExamples](https://github.com/opea-project/GenAIExamples) GitHub repo. ```bash
export WORKSPACE=<Path>
cd $WORKSPACE
git clone https://github.com/opea-project/GenAIExamples.git # GenAIExamples | ## Prerequisites
Set up a workspace and clone the [GenAIExamples](https://github.com/opea-project/GenAIExamples) GitHub repo. ```bash
export WORKSPACE=<Path>
cd $WORKSPACE
git clone https://github.com/opea-project/GenAIExamples.git # GenAIExamples | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7742425b-c48a-40d7-b468-834aa3927c9c | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 52 | opea-semantic-v1 | 035ef6e3dee5afc3 | ```bash export your_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
curl http://${host_ip}:7000/v1/retrieval \
-X POST \
-d "{\"text\":\"test\",\"embedding\":${your_embedding}}" \
-H 'Content-Type: application/json' | ai_ref_knowledge | OPEA Documentation | ```bash export your_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
curl http://${host_ip}:7000/v1/retrieval \
-X POST \
-d "{\"text\":\"test\",\"embedding\":${your_embedding}}" \
-H 'Content-Type: application/json' | ```bash export your_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)")
curl http://${host_ip}:7000/v1/retrieval \
-X POST \
-d "{\"text\":\"test\",\"embedding\":${your_embedding}}" \
-H 'Content-Type: application/json' | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7a5e0bde-c9d3-4994-90d5-bd052554a152 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 39 | opea-semantic-v1 | 30cd83c9b4aaa49b | ::::{tab-set} :::{tab-item} Ollama
ubuntu@aipc:~/GenAIExamples/ChatQnA/docker_compose/intel/cpu/aipc$ docker compose -f ./compose.yaml up -d
WARN[0000] The "LANGCHAIN_API_KEY" variable is not set. Defaulting to a blank string. WARN[0000] The "LANGCHAIN_TRACING_V2" variable is not set. Defaulting to a blank string. WAR... | ai_ref_knowledge | OPEA Documentation | ::::{tab-set} :::{tab-item} Ollama
ubuntu@aipc:~/GenAIExamples/ChatQnA/docker_compose/intel/cpu/aipc$ docker compose -f ./compose.yaml up -d
WARN[0000] The "LANGCHAIN_API_KEY" variable is not set. Defaulting to a blank string. WARN[0000] The "LANGCHAIN_TRACING_V2" variable is not set. Defaulting to a blank string. WAR... | ::::{tab-set} :::{tab-item} Ollama
ubuntu@aipc:~/GenAIExamples/ChatQnA/docker_compose/intel/cpu/aipc$ docker compose -f ./compose.yaml up -d
WARN[0000] The "LANGCHAIN_API_KEY" variable is not set. Defaulting to a blank string. WARN[0000] The "LANGCHAIN_TRACING_V2" variable is not set. Defaulting to a blank string. WAR... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7c699be0-4e73-4871-a93a-30f49b70237b | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 14 | opea-semantic-v1 | 7eb82b98c188f814 | The examples utilize model weights from Ollama and langchain.
### Set Up Ollama LLM Service
Use [Ollama](https://ollama.com/) as the LLM service for AIPC. | ai_ref_knowledge | OPEA Documentation | The examples utilize model weights from Ollama and langchain.
### Set Up Ollama LLM Service
Use [Ollama](https://ollama.com/) as the LLM service for AIPC. | The examples utilize model weights from Ollama and langchain.
### Set Up Ollama LLM Service
Use [Ollama](https://ollama.com/) as the LLM service for AIPC. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7cf6f6b4-3a45-432b-b7ea-4e3c72703d2f | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 35 | opea-semantic-v1 | f2bd7d63765327b7 | ## Deploy the Use Case
Run `docker compose` with the provided YAML file to start all the services mentioned above as containers. | ai_ref_knowledge | OPEA Documentation | ## Deploy the Use Case
Run `docker compose` with the provided YAML file to start all the services mentioned above as containers. | ## Deploy the Use Case
Run `docker compose` with the provided YAML file to start all the services mentioned above as containers. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8044f1bb-1a4c-44f6-bc15-cfe8b1937950 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 18 | opea-semantic-v1 | 968d62c6a90d9c14 | set OLLAMA_HOST environment, replacing <host_ip> with the hosts IPV4 external public IP address. For example, if the host_ip is 10.132.x.y, then `Environment="OLLAMA_HOST=10.132.x.y:11434"' should be used.
```bash
Environment="OLLAMA_HOST=host_ip:11434" | ai_ref_knowledge | OPEA Documentation | set OLLAMA_HOST environment, replacing <host_ip> with the hosts IPV4 external public IP address. For example, if the host_ip is 10.132.x.y, then `Environment="OLLAMA_HOST=10.132.x.y:11434"' should be used.
```bash
Environment="OLLAMA_HOST=host_ip:11434" | set OLLAMA_HOST environment, replacing <host_ip> with the hosts IPV4 external public IP address. For example, if the host_ip is 10.132.x.y, then `Environment="OLLAMA_HOST=10.132.x.y:11434"' should be used.
```bash
Environment="OLLAMA_HOST=host_ip:11434" | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8875ba1f-8136-4ff6-8481-c5a651e785c6 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 28 | opea-semantic-v1 | 9da03c98df3b46df | {"model":"llama3.2","created_at":"2024-10-12T12:55:28.124514468Z","response":" learning","done":false} {"model":"llama3.2","created_at":"2024-10-12T12:55:28.149754216Z","response":" is","done":false} {"model":"llama3.2","created_at":"2024-10-12T12:55:28.180420784Z","response":" a","done":false} {"model":"llama3.2","cre... | ai_ref_knowledge | OPEA Documentation | {"model":"llama3.2","created_at":"2024-10-12T12:55:28.124514468Z","response":" learning","done":false} {"model":"llama3.2","created_at":"2024-10-12T12:55:28.149754216Z","response":" is","done":false} {"model":"llama3.2","created_at":"2024-10-12T12:55:28.180420784Z","response":" a","done":false} {"model":"llama3.2","cre... | {"model":"llama3.2","created_at":"2024-10-12T12:55:28.124514468Z","response":" learning","done":false} {"model":"llama3.2","created_at":"2024-10-12T12:55:28.149754216Z","response":" is","done":false} {"model":"llama3.2","created_at":"2024-10-12T12:55:28.180420784Z","response":" a","done":false} {"model":"llama3.2","cre... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
889cc87e-73c6-4dc3-bed4-4c17fa8e66b8 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 64 | opea-semantic-v1 | fc60e209ac57cbfa | `nke-10k-2023.pdf` is Nike's annual report on a form 10-K. Run this command to download the file: ```bash wget https://github.com/opea-project/GenAIComps/blob/main/comps/third_parties/pathway/src/data/nke-10k-2023.pdf
Upload the file:
```bash
curl -X POST "http://${host_ip}:6007/v1/dataprep" \
-H "Content-Type: multip... | ai_ref_knowledge | OPEA Documentation | `nke-10k-2023.pdf` is Nike's annual report on a form 10-K. Run this command to download the file: ```bash wget https://github.com/opea-project/GenAIComps/blob/main/comps/third_parties/pathway/src/data/nke-10k-2023.pdf
Upload the file:
```bash
curl -X POST "http://${host_ip}:6007/v1/dataprep" \
-H "Content-Type: multip... | `nke-10k-2023.pdf` is Nike's annual report on a form 10-K. Run this command to download the file: ```bash wget https://github.com/opea-project/GenAIComps/blob/main/comps/third_parties/pathway/src/data/nke-10k-2023.pdf
Upload the file:
```bash
curl -X POST "http://${host_ip}:6007/v1/dataprep" \
-H "Content-Type: multip... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8b7c38e2-1be8-45a6-b178-ed36e5bd2739 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 1 | opea-semantic-v1 | 7da4107402606780 | tutorial will be covering how to build an end-to-end ChatQnA pipeline with the Redis vector database and a llama-3 model deployed on the client CPU.
## Overview | ai_ref_knowledge | OPEA Documentation | tutorial will be covering how to build an end-to-end ChatQnA pipeline with the Redis vector database and a llama-3 model deployed on the client CPU.
## Overview | tutorial will be covering how to build an end-to-end ChatQnA pipeline with the Redis vector database and a llama-3 model deployed on the client CPU.
## Overview | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8ccf3b6c-0362-48c4-aad5-1f9bfca3c572 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 38 | opea-semantic-v1 | 066bc644cc6d2543 | ### Check Env Variables After running `docker compose`, check for warning messages for environment variables that are **NOT** set. Address them if needed.
::::{tab-set}
:::{tab-item} Ollama | ai_ref_knowledge | OPEA Documentation | ### Check Env Variables After running `docker compose`, check for warning messages for environment variables that are **NOT** set. Address them if needed.
::::{tab-set}
:::{tab-item} Ollama | ### Check Env Variables After running `docker compose`, check for warning messages for environment variables that are **NOT** set. Address them if needed.
::::{tab-set}
:::{tab-item} Ollama | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8cd8afd8-7302-451a-a9f8-e9a71af2e1db | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 13 | opea-semantic-v1 | a8b5d282cbeaa8fb | For machines behind a firewall, set up the proxy environment variables: ```bash export https_proxy="Your_HTTPs_Proxy" # Example: no_proxy="localhost, 127.0.0.1, 192.168.1.1" export no_proxy=$no_proxy,chatqna-aipc-backend-server,tei-embedding-service,retriever,tei-reranking-service,redis-vector-db,dataprep-redis-service... | ai_ref_knowledge | OPEA Documentation | For machines behind a firewall, set up the proxy environment variables: ```bash export https_proxy="Your_HTTPs_Proxy" # Example: no_proxy="localhost, 127.0.0.1, 192.168.1.1" export no_proxy=$no_proxy,chatqna-aipc-backend-server,tei-embedding-service,retriever,tei-reranking-service,redis-vector-db,dataprep-redis-service... | For machines behind a firewall, set up the proxy environment variables: ```bash export https_proxy="Your_HTTPs_Proxy" # Example: no_proxy="localhost, 127.0.0.1, 192.168.1.1" export no_proxy=$no_proxy,chatqna-aipc-backend-server,tei-embedding-service,retriever,tei-reranking-service,redis-vector-db,dataprep-redis-service... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
903be5c5-880f-4a68-9396-a4e2fed819a2 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 72 | opea-semantic-v1 | bc02d0f744e8e009 | ### ChatQnA MegaService
```bash
curl http://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{
"model": "'"${OLLAMA_MODEL}"'",
"messages": "What is the revenue of Nike in 2023?"
}' | ai_ref_knowledge | OPEA Documentation | ### ChatQnA MegaService
```bash
curl http://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{
"model": "'"${OLLAMA_MODEL}"'",
"messages": "What is the revenue of Nike in 2023?"
}' | ### ChatQnA MegaService
```bash
curl http://${host_ip}:8888/v1/chatqna -H "Content-Type: application/json" -d '{
"model": "'"${OLLAMA_MODEL}"'",
"messages": "What is the revenue of Nike in 2023?"
}' | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
9043a53a-8dfa-4751-818d-9ea1c25b47b5 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 40 | opea-semantic-v1 | 400c45a0ea11477f | set. Defaulting to a blank string. WARN[0000] The "LANGCHAIN_API_KEY" variable is not set. Defaulting to a blank string. WARN[0000] The "LANGCHAIN_TRACING_V2" variable is not set.
Defaulting to a blank string. WARN[0000] /home/ubuntu/GenAIExamples/ChatQnA/docker_compose/intel/cpu/aipc/compose.yaml: `version` is obsolet... | ai_ref_knowledge | OPEA Documentation | set. Defaulting to a blank string. WARN[0000] The "LANGCHAIN_API_KEY" variable is not set. Defaulting to a blank string. WARN[0000] The "LANGCHAIN_TRACING_V2" variable is not set.
Defaulting to a blank string. WARN[0000] /home/ubuntu/GenAIExamples/ChatQnA/docker_compose/intel/cpu/aipc/compose.yaml: `version` is obsolet... | set. Defaulting to a blank string. WARN[0000] The "LANGCHAIN_API_KEY" variable is not set. Defaulting to a blank string. WARN[0000] The "LANGCHAIN_TRACING_V2" variable is not set.
Defaulting to a blank string. WARN[0000] /home/ubuntu/GenAIExamples/ChatQnA/docker_compose/intel/cpu/aipc/compose.yaml: `version` is obsolet... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
93c811bd-2b75-4a6b-9306-c23cc08a21da | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 20 | opea-semantic-v1 | cc73c729f28cef7e | If the system's network is accessed through a proxy, add a https_proxy entry to the Ollama Service Configuration file: ```bash Environment="https_proxy=Your_HTTPS_Proxy"
#### Restart Ollama services | ai_ref_knowledge | OPEA Documentation | If the system's network is accessed through a proxy, add a https_proxy entry to the Ollama Service Configuration file: ```bash Environment="https_proxy=Your_HTTPS_Proxy"
#### Restart Ollama services | If the system's network is accessed through a proxy, add a https_proxy entry to the Ollama Service Configuration file: ```bash Environment="https_proxy=Your_HTTPS_Proxy"
#### Restart Ollama services | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
950e8240-e6e3-4239-b6d6-bf2e3a5e77da | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 55 | opea-semantic-v1 | 42abfe3177ec7d69 | ### TEI Reranking Service
The TEI Reranking Service reranks the documents returned by the retrieval service. It consumes the query and list of documents and returns the document index in decreasing order of the similarity score. The document corresponding to the index with the highest score is the most relevant documen... | ai_ref_knowledge | OPEA Documentation | ### TEI Reranking Service
The TEI Reranking Service reranks the documents returned by the retrieval service. It consumes the query and list of documents and returns the document index in decreasing order of the similarity score. The document corresponding to the index with the highest score is the most relevant documen... | ### TEI Reranking Service
The TEI Reranking Service reranks the documents returned by the retrieval service. It consumes the query and list of documents and returns the document index in decreasing order of the similarity score. The document corresponding to the index with the highest score is the most relevant documen... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
993bd84f-fba9-435d-8315-bafa13aad8fe | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 2 | opea-semantic-v1 | ae3eee3d75d91d86 | ## Overview
The OPEA GenAIComps microservices used to deploy a single node vLLM or TGI megaservice solution for ChatQnA are listed below: | ai_ref_knowledge | OPEA Documentation | ## Overview
The OPEA GenAIComps microservices used to deploy a single node vLLM or TGI megaservice solution for ChatQnA are listed below: | ## Overview
The OPEA GenAIComps microservices used to deploy a single node vLLM or TGI megaservice solution for ChatQnA are listed below: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a2ecbeae-561f-4357-b6ae-8efcd441c1ff | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 51 | opea-semantic-v1 | 63038ef9931a2895 | Check the vector dimension of the embedding model used and set `your_embedding` dimension equal to it.
```bash
export your_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)") | ai_ref_knowledge | OPEA Documentation | Check the vector dimension of the embedding model used and set `your_embedding` dimension equal to it.
```bash
export your_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)") | Check the vector dimension of the embedding model used and set `your_embedding` dimension equal to it.
```bash
export your_embedding=$(python3 -c "import random; embedding = [random.uniform(-1, 1) for _ in range(768)]; print(embedding)") | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a904ae56-a8d4-477c-9cb6-79ba9b56c254 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 27 | opea-semantic-v1 | 00a3fa1c39c468bf | The output may look like this:
```bash
{"model":"llama3.2","created_at":"2024-10-12T12:55:28.098813868Z","response":"Deep","done":false}
{"model":"llama3.2","created_at":"2024-10-12T12:55:28.124514468Z","response":" learning","done":false}
{"model":"llama3.2","created_at":"2024-10-12T12:55:28.149754216Z","response":" i... | ai_ref_knowledge | OPEA Documentation | The output may look like this:
```bash
{"model":"llama3.2","created_at":"2024-10-12T12:55:28.098813868Z","response":"Deep","done":false}
{"model":"llama3.2","created_at":"2024-10-12T12:55:28.124514468Z","response":" learning","done":false}
{"model":"llama3.2","created_at":"2024-10-12T12:55:28.149754216Z","response":" i... | The output may look like this:
```bash
{"model":"llama3.2","created_at":"2024-10-12T12:55:28.098813868Z","response":"Deep","done":false}
{"model":"llama3.2","created_at":"2024-10-12T12:55:28.124514468Z","response":" learning","done":false}
{"model":"llama3.2","created_at":"2024-10-12T12:55:28.149754216Z","response":" i... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
aee21a5d-9458-4884-90e8-a518cce46cf9 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 57 | opea-semantic-v1 | 57dd2c79a5188380 | ```bash curl http://${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'
Sample output:
```bash
[{"index":1,"score":0.94238955},{"index":0,"score":0.120219156}] | ai_ref_knowledge | OPEA Documentation | ```bash curl http://${host_ip}:8808/rerank \ -X POST \ -d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \ -H 'Content-Type: application/json'
Sample output:
```bash
[{"index":1,"score":0.94238955},{"index":0,"score":0.120219156}] | ```bash curl http://${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'
Sample output:
```bash
[{"index":1,"score":0.94238955},{"index":0,"score":0.120219156}] | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b1b85679-058e-4489-a68a-963ca112113e | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 22 | opea-semantic-v1 | 161f9bc3b85450ce | Run the command to download LLM models. The <host_ip> is the one set in the `Set Ollama Service Configuration`.
```bash
export host_ip=<host_ip>
export OLLAMA_HOST=http://${host_ip}:11434
ollama pull llama3.2 | ai_ref_knowledge | OPEA Documentation | Run the command to download LLM models. The <host_ip> is the one set in the `Set Ollama Service Configuration`.
```bash
export host_ip=<host_ip>
export OLLAMA_HOST=http://${host_ip}:11434
ollama pull llama3.2 | Run the command to download LLM models. The <host_ip> is the one set in the `Set Ollama Service Configuration`.
```bash
export host_ip=<host_ip>
export OLLAMA_HOST=http://${host_ip}:11434
ollama pull llama3.2 | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
b2cf6ab3-1d8f-451c-9b56-e1468691eff1 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 10 | opea-semantic-v1 | adb2ee8b5be678a5 | Set the `HUGGINGFACEHUB_API_TOKEN` environment variable to the value of the Hugging Face token by executing the following command: ```bash export HUGGINGFACEHUB_API_TOKEN="Your_Huggingface_API_Token"
The example requires setting the `host_ip` to "localhost" to deploy the microservices on endpoints enabled with ports. `... | ai_ref_knowledge | OPEA Documentation | Set the `HUGGINGFACEHUB_API_TOKEN` environment variable to the value of the Hugging Face token by executing the following command: ```bash export HUGGINGFACEHUB_API_TOKEN="Your_Huggingface_API_Token"
The example requires setting the `host_ip` to "localhost" to deploy the microservices on endpoints enabled with ports. `... | Set the `HUGGINGFACEHUB_API_TOKEN` environment variable to the value of the Hugging Face token by executing the following command: ```bash export HUGGINGFACEHUB_API_TOKEN="Your_Huggingface_API_Token"
The example requires setting the `host_ip` to "localhost" to deploy the microservices on endpoints enabled with ports. `... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ba1eebe1-b4f3-46b5-a0c3-90fc7f1cfd16 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 21 | opea-semantic-v1 | cf84d07a03d96b00 | #### Pull Ollama LLM model
Run the command to download LLM models. The <host_ip> is the one set in the `Set Ollama Service Configuration`. | ai_ref_knowledge | OPEA Documentation | #### Pull Ollama LLM model
Run the command to download LLM models. The <host_ip> is the one set in the `Set Ollama Service Configuration`. | #### Pull Ollama LLM model
Run the command to download LLM models. The <host_ip> is the one set in the `Set Ollama Service Configuration`. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
bc974fd0-e900-4c30-b1b8-682253544589 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 80 | opea-semantic-v1 | 6d8c7a06565d9bae | Navigate to the `docker compose` directory for this hardware platform. ```bash cd $WORKSPACE/GenAIExamples/ChatQnA/docker_compose/intel/cpu/aipc
To stop and remove all the containers, use the command below: | ai_ref_knowledge | OPEA Documentation | Navigate to the `docker compose` directory for this hardware platform. ```bash cd $WORKSPACE/GenAIExamples/ChatQnA/docker_compose/intel/cpu/aipc
To stop and remove all the containers, use the command below: | Navigate to the `docker compose` directory for this hardware platform. ```bash cd $WORKSPACE/GenAIExamples/ChatQnA/docker_compose/intel/cpu/aipc
To stop and remove all the containers, use the command below: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c0c7002f-e403-4a2d-b60b-263efe4ca88b | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 15 | opea-semantic-v1 | 14c6ca72359faaec | ### Set Up Ollama LLM Service Use [Ollama](https://ollama.com/) as the LLM service for AIPC.
Please follow the instructions to set up Ollama on the PC. This will set the entrypoint needed for the Ollama to work with the ChatQnA example. | ai_ref_knowledge | OPEA Documentation | ### Set Up Ollama LLM Service Use [Ollama](https://ollama.com/) as the LLM service for AIPC.
Please follow the instructions to set up Ollama on the PC. This will set the entrypoint needed for the Ollama to work with the ChatQnA example. | ### Set Up Ollama LLM Service Use [Ollama](https://ollama.com/) as the LLM service for AIPC.
Please follow the instructions to set up Ollama on the PC. This will set the entrypoint needed for the Ollama to work with the ChatQnA example. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c1b6435e-b8bd-4821-afb1-a3106c8506a5 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 71 | opea-semantic-v1 | 5743fabd822ccc9f | #### Delete all uploaded files and links
```bash
curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
-d '{"file_path": "all"}' \
-H "Content-Type: application/json" | ai_ref_knowledge | OPEA Documentation | #### Delete all uploaded files and links
```bash
curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
-d '{"file_path": "all"}' \
-H "Content-Type: application/json" | #### Delete all uploaded files and links
```bash
curl -X POST "http://${host_ip}:6007/v1/dataprep/delete_file" \
-d '{"file_path": "all"}' \
-H "Content-Type: application/json" | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c378c5fe-f6aa-408f-82b6-a7c854559f5f | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 53 | opea-semantic-v1 | e09d5421cfe18709 | curl http://${host_ip}:7000/v1/retrieval \ -X POST \ -d "{\"text\":\"test\",\"embedding\":${your_embedding}}" \ -H 'Content-Type: application/json'
The output of the retriever microservice comprises of the a unique id for the request, initial query or the input to the retrieval microservice, a list of top `n` retrieved... | ai_ref_knowledge | OPEA Documentation | curl http://${host_ip}:7000/v1/retrieval \ -X POST \ -d "{\"text\":\"test\",\"embedding\":${your_embedding}}" \ -H 'Content-Type: application/json'
The output of the retriever microservice comprises of the a unique id for the request, initial query or the input to the retrieval microservice, a list of top `n` retrieved... | curl http://${host_ip}:7000/v1/retrieval \ -X POST \ -d "{\"text\":\"test\",\"embedding\":${your_embedding}}" \ -H 'Content-Type: application/json'
The output of the retriever microservice comprises of the a unique id for the request, initial query or the input to the retrieval microservice, a list of top `n` retrieved... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c8c16a2c-8ba1-41c1-bdae-c23390c8c9b1 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 23 | opea-semantic-v1 | e6db72b90e67e480 | ```bash export host_ip=<host_ip> export OLLAMA_HOST=http://${host_ip}:11434 ollama pull llama3.2
After downloading the models, list the models by executing the `ollama list` command. | ai_ref_knowledge | OPEA Documentation | ```bash export host_ip=<host_ip> export OLLAMA_HOST=http://${host_ip}:11434 ollama pull llama3.2
After downloading the models, list the models by executing the `ollama list` command. | ```bash export host_ip=<host_ip> export OLLAMA_HOST=http://${host_ip}:11434 ollama pull llama3.2
After downloading the models, list the models by executing the `ollama list` command. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ccc79d0f-2975-4276-8601-034ba7471f14 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 5 | opea-semantic-v1 | 0aa3fee8982a5a7d | question about Nike to receive a response. Although multiple versions of the UI can be deployed, this tutorial will focus solely on the default version.
## Prerequisites | ai_ref_knowledge | OPEA Documentation | question about Nike to receive a response. Although multiple versions of the UI can be deployed, this tutorial will focus solely on the default version.
## Prerequisites | question about Nike to receive a response. Although multiple versions of the UI can be deployed, this tutorial will focus solely on the default version.
## Prerequisites | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d1178fcd-1c59-4863-a9e9-0a9b5b2a3ed9 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 49 | opea-semantic-v1 | c064f22ac878ac85 | ### Retriever Microservice
To consume the retriever microservice, generate a mock embedding vector with a Python script. The length of the embedding vector is determined by the embedding model. The model is set with the environment variable EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5", which has a vector size of 768. | ai_ref_knowledge | OPEA Documentation | ### Retriever Microservice
To consume the retriever microservice, generate a mock embedding vector with a Python script. The length of the embedding vector is determined by the embedding model. The model is set with the environment variable EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5", which has a vector size of 768. | ### Retriever Microservice
To consume the retriever microservice, generate a mock embedding vector with a Python script. The length of the embedding vector is determined by the embedding model. The model is set with the environment variable EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5", which has a vector size of 768. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d13e3e3d-31e3-4f97-a6c2-4c3caf6d8d45 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 60 | opea-semantic-v1 | 6f318f0725d02637 | Ollama service generates text for the input prompt.
Here is the expected result from Ollama:
```bash
{"model":"llama3","created_at":"2024-09-05T08:47:17.160752424Z","response":"Deep","done":false}
{"model":"llama3","created_at":"2024-09-05T08:47:18.229472564Z","response":" learning","done":false}
{"model":"llama3","cre... | ai_ref_knowledge | OPEA Documentation | Ollama service generates text for the input prompt.
Here is the expected result from Ollama:
```bash
{"model":"llama3","created_at":"2024-09-05T08:47:17.160752424Z","response":"Deep","done":false}
{"model":"llama3","created_at":"2024-09-05T08:47:18.229472564Z","response":" learning","done":false}
{"model":"llama3","cre... | Ollama service generates text for the input prompt.
Here is the expected result from Ollama:
```bash
{"model":"llama3","created_at":"2024-09-05T08:47:17.160752424Z","response":"Deep","done":false}
{"model":"llama3","created_at":"2024-09-05T08:47:18.229472564Z","response":" learning","done":false}
{"model":"llama3","cre... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d427d2d6-b26f-4b0a-adcf-90ba7c22abc4 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 7 | opea-semantic-v1 | d55d3853d3069154 | Set up a workspace and clone the [GenAIExamples](https://github.com/opea-project/GenAIExamples) GitHub repo. ```bash export WORKSPACE=<Path> cd $WORKSPACE git clone https://github.com/opea-project/GenAIExamples.git # GenAIExamples
**(Optional)** It is recommended to use a stable release version by setting `RELEASE_VERS... | ai_ref_knowledge | OPEA Documentation | Set up a workspace and clone the [GenAIExamples](https://github.com/opea-project/GenAIExamples) GitHub repo. ```bash export WORKSPACE=<Path> cd $WORKSPACE git clone https://github.com/opea-project/GenAIExamples.git # GenAIExamples
**(Optional)** It is recommended to use a stable release version by setting `RELEASE_VERS... | Set up a workspace and clone the [GenAIExamples](https://github.com/opea-project/GenAIExamples) GitHub repo. ```bash export WORKSPACE=<Path> cd $WORKSPACE git clone https://github.com/opea-project/GenAIExamples.git # GenAIExamples
**(Optional)** It is recommended to use a stable release version by setting `RELEASE_VERS... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d7e20e98-e242-47eb-9e79-b852efe97343 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 42 | opea-semantic-v1 | 02491c97a52fb463 | Check if all the containers launched via `docker compose` are running i.e. each container's `STATUS` is `Up` and `Healthy`.
Run this command to see this info:
```bash
docker ps -a | ai_ref_knowledge | OPEA Documentation | Check if all the containers launched via `docker compose` are running i.e. each container's `STATUS` is `Up` and `Healthy`.
Run this command to see this info:
```bash
docker ps -a | Check if all the containers launched via `docker compose` are running i.e. each container's `STATUS` is `Up` and `Healthy`.
Run this command to see this info:
```bash
docker ps -a | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d8b43789-efba-4840-bebf-19b256654326 | OPEA Documentation | file://datasets/opea-docs/tutorial/ChatQnA/deploy/aipc.md | unknown | aeb0057b-c949-441a-965a-28121ba1ab77 | 26 | opea-semantic-v1 | f755d9136cf28834 | Access ollama service to verify that Ollama is functioning correctly.
```bash
curl http://${host_ip}:11434/api/generate -d '{"model": "llama3.2", "prompt":"What is Deep Learning?"}' | ai_ref_knowledge | OPEA Documentation | Access ollama service to verify that Ollama is functioning correctly.
```bash
curl http://${host_ip}:11434/api/generate -d '{"model": "llama3.2", "prompt":"What is Deep Learning?"}' | Access ollama service to verify that Ollama is functioning correctly.
```bash
curl http://${host_ip}:11434/api/generate -d '{"model": "llama3.2", "prompt":"What is Deep Learning?"}' | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation |
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