chunk_id stringlengths 36 36 | source stringclasses 35
values | source_url stringlengths 0 290 | upstream_license stringclasses 1
value | document_id stringlengths 36 36 | chunk_index int64 0 324k | retrieved_at stringclasses 2
values | chunker_version stringclasses 4
values | content_hash stringlengths 15 64 | content stringlengths 50 44.7k | namespace stringclasses 9
values | source_name stringclasses 35
values | raw_text stringlengths 50 44.7k | cleaned_text stringlengths 50 44.7k | tags stringclasses 49
values | collection_name stringclasses 11
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
b2f8a4a5-38d3-4273-a03c-312913bc472e | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/deploy/gaudi.md | unknown | 621ec5dc-4467-432e-8d76-36fe747ab3d0 | 23 | opea-semantic-v1 | 1608c0e0aeeee5ab | In this section, you will walk through the different ways to interact with the deployed microservices.
### Add Knowledge Base via HTTP Links | ai_ref_knowledge | OPEA Documentation | In this section, you will walk through the different ways to interact with the deployed microservices.
### Add Knowledge Base via HTTP Links | In this section, you will walk through the different ways to interact with the deployed microservices.
### Add Knowledge Base via HTTP Links | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
bbe3317f-6eb7-4ff9-8fcb-124fb9ab12c3 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/deploy/gaudi.md | unknown | 621ec5dc-4467-432e-8d76-36fe747ab3d0 | 1 | opea-semantic-v1 | 2614f5a4b4a8990f | ## Overview
There are several ways to setup a DocIndexRetriever use case. Here in this tutorial, we will walk through how to enable the below list of microservices from OPEA GenAIComps to deploy a single node TGI megaservice solution. | ai_ref_knowledge | OPEA Documentation | ## Overview
There are several ways to setup a DocIndexRetriever use case. Here in this tutorial, we will walk through how to enable the below list of microservices from OPEA GenAIComps to deploy a single node TGI megaservice solution. | ## Overview
There are several ways to setup a DocIndexRetriever use case. Here in this tutorial, we will walk through how to enable the below list of microservices from OPEA GenAIComps to deploy a single node TGI megaservice solution. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c8197c2c-7a99-4a8b-a4b0-057935e25f64 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/deploy/gaudi.md | unknown | 621ec5dc-4467-432e-8d76-36fe747ab3d0 | 19 | opea-semantic-v1 | 81e7363ed690c75e | Check the startup log by `docker compose -f ./compose.yaml logs`. The warning messages print out the variables if they are **NOT** set.
```bash
GenAIExamples/DocIndexRetriever/docker_compose/intel/hpu/gaudi$ sudo -E docker compose -f ./compose.yaml logs
WARN[0000] The "no_proxy" variable is not set. Defaulting to a bla... | ai_ref_knowledge | OPEA Documentation | Check the startup log by `docker compose -f ./compose.yaml logs`. The warning messages print out the variables if they are **NOT** set.
```bash
GenAIExamples/DocIndexRetriever/docker_compose/intel/hpu/gaudi$ sudo -E docker compose -f ./compose.yaml logs
WARN[0000] The "no_proxy" variable is not set. Defaulting to a bla... | Check the startup log by `docker compose -f ./compose.yaml logs`. The warning messages print out the variables if they are **NOT** set.
```bash
GenAIExamples/DocIndexRetriever/docker_compose/intel/hpu/gaudi$ sudo -E docker compose -f ./compose.yaml logs
WARN[0000] The "no_proxy" variable is not set. Defaulting to a bla... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
cf066e44-7820-49bb-a564-1d384082b82d | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/deploy/gaudi.md | unknown | 621ec5dc-4467-432e-8d76-36fe747ab3d0 | 14 | opea-semantic-v1 | 8735f5f45aa0feae | | NA | Open source service | |Embedding | TEI | BAAI/bge-base-en-v1.5 | OPEA Microservice | |Reranking | TEI | BAAI/bge-reranker-base | OPEA Microservice |
Tools and models mentioned in the table are configurable either through the environment variable or `compose.yaml` | ai_ref_knowledge | OPEA Documentation | | NA | Open source service | |Embedding | TEI | BAAI/bge-base-en-v1.5 | OPEA Microservice | |Reranking | TEI | BAAI/bge-reranker-base | OPEA Microservice |
Tools and models mentioned in the table are configurable either through the environment variable or `compose.yaml` | | NA | Open source service | |Embedding | TEI | BAAI/bge-base-en-v1.5 | OPEA Microservice | |Reranking | TEI | BAAI/bge-reranker-base | OPEA Microservice |
Tools and models mentioned in the table are configurable either through the environment variable or `compose.yaml` | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d83d232a-621e-410c-9960-de86a9c3cbba | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/deploy/gaudi.md | unknown | 621ec5dc-4467-432e-8d76-36fe747ab3d0 | 8 | opea-semantic-v1 | 7e15df426303512a | This step involves either building or pulling four required Docker images. Each image serves a specific purpose in the DocIndexRetriever architecture.
::::::{tab-set} | ai_ref_knowledge | OPEA Documentation | This step involves either building or pulling four required Docker images. Each image serves a specific purpose in the DocIndexRetriever architecture.
::::::{tab-set} | This step involves either building or pulling four required Docker images. Each image serves a specific purpose in the DocIndexRetriever architecture.
::::::{tab-set} | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e3c07f05-35a7-4470-8552-1f61fcedcf3f | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/deploy/gaudi.md | unknown | 621ec5dc-4467-432e-8d76-36fe747ab3d0 | 24 | opea-semantic-v1 | beea9124501614ed | ### Add Knowledge Base via HTTP Links
```bash
curl -X POST "http://${host_ip}:6007/v1/dataprep/ingest" \
-H "Content-Type: multipart/form-data" \
-F 'link_list=["https://opea.dev"]'
# expected output
{"status":200,"message":"Data preparation succeeded"} | ai_ref_knowledge | OPEA Documentation | ### Add Knowledge Base via HTTP Links
```bash
curl -X POST "http://${host_ip}:6007/v1/dataprep/ingest" \
-H "Content-Type: multipart/form-data" \
-F 'link_list=["https://opea.dev"]'
# expected output
{"status":200,"message":"Data preparation succeeded"} | ### Add Knowledge Base via HTTP Links
```bash
curl -X POST "http://${host_ip}:6007/v1/dataprep/ingest" \
-H "Content-Type: multipart/form-data" \
-F 'link_list=["https://opea.dev"]'
# expected output
{"status":200,"message":"Data preparation succeeded"} | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e3dcb29f-af78-470c-b44b-0a68ebf9fefb | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/deploy/gaudi.md | unknown | 621ec5dc-4467-432e-8d76-36fe747ab3d0 | 2 | opea-semantic-v1 | 971dc5e14e659225 | this tutorial, we will walk through how to enable the below list of microservices from OPEA GenAIComps to deploy a single node TGI megaservice solution.
1. Embedding TEI Service
2. Retriever Vector Store Service
3. Rerank TEI Service
4. Dataprep Service | ai_ref_knowledge | OPEA Documentation | this tutorial, we will walk through how to enable the below list of microservices from OPEA GenAIComps to deploy a single node TGI megaservice solution.
1. Embedding TEI Service
2. Retriever Vector Store Service
3. Rerank TEI Service
4. Dataprep Service | this tutorial, we will walk through how to enable the below list of microservices from OPEA GenAIComps to deploy a single node TGI megaservice solution.
1. Embedding TEI Service
2. Retriever Vector Store Service
3. Rerank TEI Service
4. Dataprep Service | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e666e5be-58a3-41b3-90d3-e19762189a9b | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/deploy/gaudi.md | unknown | 621ec5dc-4467-432e-8d76-36fe747ab3d0 | 5 | opea-semantic-v1 | b7c902c902461cc6 | The example requires you to set the the following variables to deploy the microservices on endpoint enabled with ports.
```bash
export host_ip=$(hostname -I | awk '{print $1}')
export HUGGINGFACEHUB_API_TOKEN=<your HF token>
export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"
export RERANK_MODEL_ID="BAAI/bge-reranker-bas... | ai_ref_knowledge | OPEA Documentation | The example requires you to set the the following variables to deploy the microservices on endpoint enabled with ports.
```bash
export host_ip=$(hostname -I | awk '{print $1}')
export HUGGINGFACEHUB_API_TOKEN=<your HF token>
export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"
export RERANK_MODEL_ID="BAAI/bge-reranker-bas... | The example requires you to set the the following variables to deploy the microservices on endpoint enabled with ports.
```bash
export host_ip=$(hostname -I | awk '{print $1}')
export HUGGINGFACEHUB_API_TOKEN=<your HF token>
export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"
export RERANK_MODEL_ID="BAAI/bge-reranker-bas... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e83f1920-a6c4-449b-892b-3dc7b40edae1 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/deploy/gaudi.md | unknown | 621ec5dc-4467-432e-8d76-36fe747ab3d0 | 4 | opea-semantic-v1 | 6f3d3a172f60c3e9 | to use all components of DocIndexRetriever on Gaudi AI Accelerator. We will go through how to setup docker container to start a microservices and megaservice.
## Prerequisites | ai_ref_knowledge | OPEA Documentation | to use all components of DocIndexRetriever on Gaudi AI Accelerator. We will go through how to setup docker container to start a microservices and megaservice.
## Prerequisites | to use all components of DocIndexRetriever on Gaudi AI Accelerator. We will go through how to setup docker container to start a microservices and megaservice.
## Prerequisites | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
eab70769-58a8-4221-90d7-79ea866bfcfb | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/deploy/gaudi.md | unknown | 621ec5dc-4467-432e-8d76-36fe747ab3d0 | 22 | opea-semantic-v1 | a351b008895ccc8e | ## Interacting with DocIndexRetriever deployment
In this section, you will walk through the different ways to interact with the deployed microservices. | ai_ref_knowledge | OPEA Documentation | ## Interacting with DocIndexRetriever deployment
In this section, you will walk through the different ways to interact with the deployed microservices. | ## Interacting with DocIndexRetriever deployment
In this section, you will walk through the different ways to interact with the deployed microservices. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f8674104-2896-4beb-b621-f7c5241a7591 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/deploy/gaudi.md | unknown | 621ec5dc-4467-432e-8d76-36fe747ab3d0 | 20 | opea-semantic-v1 | ae3b3538f32f3abf | To do a quick sanity check, try `docker ps -a` to see if all the containers are running.
```bash
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
3b5fa9a722da opea/doc-index-retriever-server:${RELEASE_VERSION} "docker-entrypoint.s…" 32 hours ago Up 2 hours 0.0.0.0:8889->8889/tcp, :::8889->8889/tcp doc-index-retrie... | ai_ref_knowledge | OPEA Documentation | To do a quick sanity check, try `docker ps -a` to see if all the containers are running.
```bash
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
3b5fa9a722da opea/doc-index-retriever-server:${RELEASE_VERSION} "docker-entrypoint.s…" 32 hours ago Up 2 hours 0.0.0.0:8889->8889/tcp, :::8889->8889/tcp doc-index-retrie... | To do a quick sanity check, try `docker ps -a` to see if all the containers are running.
```bash
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
3b5fa9a722da opea/doc-index-retriever-server:${RELEASE_VERSION} "docker-entrypoint.s…" 32 hours ago Up 2 hours 0.0.0.0:8889->8889/tcp, :::8889->8889/tcp doc-index-retrie... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
fa0f258c-da5c-4f06-bce1-c024eab2c8bf | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/deploy/gaudi.md | unknown | 621ec5dc-4467-432e-8d76-36fe747ab3d0 | 7 | opea-semantic-v1 | 04bed08989864289 | ## Prepare (Building / Pulling) Docker images
This step involves either building or pulling four required Docker images. Each image serves a specific purpose in the DocIndexRetriever architecture. | ai_ref_knowledge | OPEA Documentation | ## Prepare (Building / Pulling) Docker images
This step involves either building or pulling four required Docker images. Each image serves a specific purpose in the DocIndexRetriever architecture. | ## Prepare (Building / Pulling) Docker images
This step involves either building or pulling four required Docker images. Each image serves a specific purpose in the DocIndexRetriever architecture. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
fcaa6bab-a3ec-4f7a-9f47-6cd52c84f959 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/deploy/gaudi.md | unknown | 621ec5dc-4467-432e-8d76-36fe747ab3d0 | 9 | opea-semantic-v1 | f23785731f622ce2 | ### Build MegaService Image
The Megaservice is a pipeline that channels data through different microservices, each performing varied tasks. We define the different microservices and the flow of data between them in the `retrieval_tool.py` file. | ai_ref_knowledge | OPEA Documentation | ### Build MegaService Image
The Megaservice is a pipeline that channels data through different microservices, each performing varied tasks. We define the different microservices and the flow of data between them in the `retrieval_tool.py` file. | ### Build MegaService Image
The Megaservice is a pipeline that channels data through different microservices, each performing varied tasks. We define the different microservices and the flow of data between them in the `retrieval_tool.py` file. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
12e43f79-1ce1-4895-9156-9faec4a0e7cd | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/deploy/xeon.md | unknown | ee6ae493-7f72-4ebf-88d2-80bde492c48e | 7 | opea-semantic-v1 | 465bc52c39cafee3 | # DocRetriever without Rerank (optional) docker compose -f compose_without_rank.yaml up -d
Note: add the following environment variables in compose yaml if meet issues for downloading models:
```bash
HF_ENDPOINT: https://hf-mirror.com
HF_HUB_ENABLE_HF_TRANSFER: false | ai_ref_knowledge | OPEA Documentation | # DocRetriever without Rerank (optional) docker compose -f compose_without_rank.yaml up -d
Note: add the following environment variables in compose yaml if meet issues for downloading models:
```bash
HF_ENDPOINT: https://hf-mirror.com
HF_HUB_ENABLE_HF_TRANSFER: false | # DocRetriever without Rerank (optional) docker compose -f compose_without_rank.yaml up -d
Note: add the following environment variables in compose yaml if meet issues for downloading models:
```bash
HF_ENDPOINT: https://hf-mirror.com
HF_HUB_ENABLE_HF_TRANSFER: false | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
2d5344c4-5e19-4760-ba46-fef9ec948511 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/deploy/xeon.md | unknown | ee6ae493-7f72-4ebf-88d2-80bde492c48e | 8 | opea-semantic-v1 | e8455b35d2e00769 | Check the startup log by `docker compose -f ./compose.yaml logs`. The warning messages print out the variables if they are **NOT** set.
```bash
GenAIExamples/DocIndexRetriever/docker_compose/intel/cpu/xeon$ sudo -E docker compose -f ./compose.yaml logs
WARN[0000] The "no_proxy" variable is not set. Defaulting to a blan... | ai_ref_knowledge | OPEA Documentation | Check the startup log by `docker compose -f ./compose.yaml logs`. The warning messages print out the variables if they are **NOT** set.
```bash
GenAIExamples/DocIndexRetriever/docker_compose/intel/cpu/xeon$ sudo -E docker compose -f ./compose.yaml logs
WARN[0000] The "no_proxy" variable is not set. Defaulting to a blan... | Check the startup log by `docker compose -f ./compose.yaml logs`. The warning messages print out the variables if they are **NOT** set.
```bash
GenAIExamples/DocIndexRetriever/docker_compose/intel/cpu/xeon$ sudo -E docker compose -f ./compose.yaml logs
WARN[0000] The "no_proxy" variable is not set. Defaulting to a blan... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
39b5ab9f-b11b-4cc0-b758-8c0ac2d112f5 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/deploy/xeon.md | unknown | ee6ae493-7f72-4ebf-88d2-80bde492c48e | 9 | opea-semantic-v1 | 8447fddd7f42cf41 | To do a quick sanity check, try `docker ps -a` to see if all the containers are running.
```bash
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
3b5fa9a722da opea/doc-index-retriever-server:${RELEASE_VERSION} "docker-entrypoint.s…" 32 hours ago Up 2 hours 0.0.0.0:8889->8889/tcp, :::8889->8889/tcp doc-index-retrie... | ai_ref_knowledge | OPEA Documentation | To do a quick sanity check, try `docker ps -a` to see if all the containers are running.
```bash
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
3b5fa9a722da opea/doc-index-retriever-server:${RELEASE_VERSION} "docker-entrypoint.s…" 32 hours ago Up 2 hours 0.0.0.0:8889->8889/tcp, :::8889->8889/tcp doc-index-retrie... | To do a quick sanity check, try `docker ps -a` to see if all the containers are running.
```bash
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
3b5fa9a722da opea/doc-index-retriever-server:${RELEASE_VERSION} "docker-entrypoint.s…" 32 hours ago Up 2 hours 0.0.0.0:8889->8889/tcp, :::8889->8889/tcp doc-index-retrie... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
565506e5-c60f-4db4-9e9a-aff584f76d16 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/deploy/xeon.md | unknown | ee6ae493-7f72-4ebf-88d2-80bde492c48e | 4 | opea-semantic-v1 | cd9e0fed395ae75e | $1}') export HUGGINGFACEHUB_API_TOKEN=<your HF token> export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5" export RERANK_MODEL_ID="BAAI/bge-reranker-base" export TEI_EMBEDDING_ENDPOINT="http://${host_ip}:6006" export TEI_RERANKING_ENDPOINT="http://${host_ip}:8808" export EMBEDDING_SERVICE_HOST_IP=${host_ip} export RETRIEV... | ai_ref_knowledge | OPEA Documentation | $1}') export HUGGINGFACEHUB_API_TOKEN=<your HF token> export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5" export RERANK_MODEL_ID="BAAI/bge-reranker-base" export TEI_EMBEDDING_ENDPOINT="http://${host_ip}:6006" export TEI_RERANKING_ENDPOINT="http://${host_ip}:8808" export EMBEDDING_SERVICE_HOST_IP=${host_ip} export RETRIEV... | $1}') export HUGGINGFACEHUB_API_TOKEN=<your HF token> export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5" export RERANK_MODEL_ID="BAAI/bge-reranker-base" export TEI_EMBEDDING_ENDPOINT="http://${host_ip}:6006" export TEI_RERANKING_ENDPOINT="http://${host_ip}:8808" export EMBEDDING_SERVICE_HOST_IP=${host_ip} export RETRIEV... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
632dd745-76e5-47a8-aa2d-2bed8daf9ce0 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/deploy/xeon.md | unknown | ee6ae493-7f72-4ebf-88d2-80bde492c48e | 5 | opea-semantic-v1 | 9b695db9070f0a4b | Set the necessary environment variables to setup the use case by running the `set_env.sh` script.
Run the `set_env.sh` script. ```bash
cd $WORKSPACE/GenAIExamples/DocIndexRetriever/docker_compose/intel/cpu/xeon
source ./set_env.sh | ai_ref_knowledge | OPEA Documentation | Set the necessary environment variables to setup the use case by running the `set_env.sh` script.
Run the `set_env.sh` script. ```bash
cd $WORKSPACE/GenAIExamples/DocIndexRetriever/docker_compose/intel/cpu/xeon
source ./set_env.sh | Set the necessary environment variables to setup the use case by running the `set_env.sh` script.
Run the `set_env.sh` script. ```bash
cd $WORKSPACE/GenAIExamples/DocIndexRetriever/docker_compose/intel/cpu/xeon
source ./set_env.sh | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6ebb2653-e620-4191-bcde-75de96bae85e | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/deploy/xeon.md | unknown | ee6ae493-7f72-4ebf-88d2-80bde492c48e | 6 | opea-semantic-v1 | d1d7d494ec8df0a9 | tutorial, we will be deploying via docker compose with the provided YAML file. The docker compose instructions should start all the above-mentioned services as containers.
```bash
cd $WORKSPACE/GenAIExamples/DocIndexRetriever/docker_compose/intel/cpu/xeon/
docker compose up -d | ai_ref_knowledge | OPEA Documentation | tutorial, we will be deploying via docker compose with the provided YAML file. The docker compose instructions should start all the above-mentioned services as containers.
```bash
cd $WORKSPACE/GenAIExamples/DocIndexRetriever/docker_compose/intel/cpu/xeon/
docker compose up -d | tutorial, we will be deploying via docker compose with the provided YAML file. The docker compose instructions should start all the above-mentioned services as containers.
```bash
cd $WORKSPACE/GenAIExamples/DocIndexRetriever/docker_compose/intel/cpu/xeon/
docker compose up -d | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
82b56f9b-3bcb-4f9f-9a95-c1bed4db32ba | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/deploy/xeon.md | unknown | ee6ae493-7f72-4ebf-88d2-80bde492c48e | 1 | opea-semantic-v1 | 1a7f71d0d16243b0 | 1. Embedding TEI Service 2. Retriever Vector Store Service 3. Rerank TEI Service 4. Dataprep Service
The solution is aimed to show how to use all components of DocIndexRetriever on Intel Xeon Scalable processors. We will go through how to setup docker container to start a microservices and megaservice. | ai_ref_knowledge | OPEA Documentation | 1. Embedding TEI Service 2. Retriever Vector Store Service 3. Rerank TEI Service 4. Dataprep Service
The solution is aimed to show how to use all components of DocIndexRetriever on Intel Xeon Scalable processors. We will go through how to setup docker container to start a microservices and megaservice. | 1. Embedding TEI Service 2. Retriever Vector Store Service 3. Rerank TEI Service 4. Dataprep Service
The solution is aimed to show how to use all components of DocIndexRetriever on Intel Xeon Scalable processors. We will go through how to setup docker container to start a microservices and megaservice. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
abfc16a7-cc2d-442c-8547-5d668a07f9f9 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/deploy/xeon.md | unknown | ee6ae493-7f72-4ebf-88d2-80bde492c48e | 2 | opea-semantic-v1 | 930160f7dfd7514b | use all components of DocIndexRetriever on Intel Xeon Scalable processors. We will go through how to setup docker container to start a microservices and megaservice.
## Prerequisites | ai_ref_knowledge | OPEA Documentation | use all components of DocIndexRetriever on Intel Xeon Scalable processors. We will go through how to setup docker container to start a microservices and megaservice.
## Prerequisites | use all components of DocIndexRetriever on Intel Xeon Scalable processors. We will go through how to setup docker container to start a microservices and megaservice.
## Prerequisites | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c9d0ef49-381f-4430-8b0f-6ef66d56d348 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/deploy/xeon.md | unknown | ee6ae493-7f72-4ebf-88d2-80bde492c48e | 3 | opea-semantic-v1 | edd6c544eaf0d2a5 | The example requires you to set the the following variables to deploy the microservices on endpoint enabled with ports.
```bash
export host_ip=$(hostname -I | awk '{print $1}')
export HUGGINGFACEHUB_API_TOKEN=<your HF token>
export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"
export RERANK_MODEL_ID="BAAI/bge-reranker-bas... | ai_ref_knowledge | OPEA Documentation | The example requires you to set the the following variables to deploy the microservices on endpoint enabled with ports.
```bash
export host_ip=$(hostname -I | awk '{print $1}')
export HUGGINGFACEHUB_API_TOKEN=<your HF token>
export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"
export RERANK_MODEL_ID="BAAI/bge-reranker-bas... | The example requires you to set the the following variables to deploy the microservices on endpoint enabled with ports.
```bash
export host_ip=$(hostname -I | awk '{print $1}')
export HUGGINGFACEHUB_API_TOKEN=<your HF token>
export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"
export RERANK_MODEL_ID="BAAI/bge-reranker-bas... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e64970da-94d5-40e2-aa1f-3e73d9374b49 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocIndexRetriever/deploy/xeon.md | unknown | ee6ae493-7f72-4ebf-88d2-80bde492c48e | 0 | opea-semantic-v1 | 6794581efbd10445 | # Single node on-prem deployment with TGI on Xeon Scalable processors
This deployment section covers single-node on-prem deployment of the DocIndexRetriever example with OPEA comps to deploy using TGI service. The solution demonstrates building a doc retriever service using the TGI deployed on Intel® Xeon® Scalable pro... | ai_ref_knowledge | OPEA Documentation | # Single node on-prem deployment with TGI on Xeon Scalable processors
This deployment section covers single-node on-prem deployment of the DocIndexRetriever example with OPEA comps to deploy using TGI service. The solution demonstrates building a doc retriever service using the TGI deployed on Intel® Xeon® Scalable pro... | # Single node on-prem deployment with TGI on Xeon Scalable processors
This deployment section covers single-node on-prem deployment of the DocIndexRetriever example with OPEA comps to deploy using TGI service. The solution demonstrates building a doc retriever service using the TGI deployed on Intel® Xeon® Scalable pro... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
011decc0-53a3-4ea1-bb8d-d4041e86c357 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 58 | opea-semantic-v1 | 1a4aae815214498a | inputs into multiple chunks, map each document to an individual summary, and consolidate all summaries into a single global summary. `stream=True` is not allowed here.
In this mode, `chunk_size` is set to `min(MAX_TOTAL_TOKENS - input.max_tokens - 50, MAX_INPUT_TOKENS)`. | ai_ref_knowledge | OPEA Documentation | inputs into multiple chunks, map each document to an individual summary, and consolidate all summaries into a single global summary. `stream=True` is not allowed here.
In this mode, `chunk_size` is set to `min(MAX_TOTAL_TOKENS - input.max_tokens - 50, MAX_INPUT_TOKENS)`. | inputs into multiple chunks, map each document to an individual summary, and consolidate all summaries into a single global summary. `stream=True` is not allowed here.
In this mode, `chunk_size` is set to `min(MAX_TOTAL_TOKENS - input.max_tokens - 50, MAX_INPUT_TOKENS)`. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0a9f2434-951f-4d76-9964-ee509d28d346 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 45 | opea-semantic-v1 | 5c6100d6b3edb554 | #### Megaservice with Long Context
When performing summarization with long contexts - content longer than the model's context limit - different summarization strategies can be used such as *auto*, *stuff*, *truncate*, *map_reduce*, or *refine*. The best strategy is determined from various factors including model contex... | ai_ref_knowledge | OPEA Documentation | #### Megaservice with Long Context
When performing summarization with long contexts - content longer than the model's context limit - different summarization strategies can be used such as *auto*, *stuff*, *truncate*, *map_reduce*, or *refine*. The best strategy is determined from various factors including model contex... | #### Megaservice with Long Context
When performing summarization with long contexts - content longer than the model's context limit - different summarization strategies can be used such as *auto*, *stuff*, *truncate*, *map_reduce*, or *refine*. The best strategy is determined from various factors including model contex... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0e8367d7-ca1e-4df1-9d0d-6cfde14ef66f | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 40 | opea-semantic-v1 | 81f7cefe229c1f3c | Form input: ```bash curl http://${host_ip}:8888/v1/docsum \ -H "Content-Type: multipart/form-data" \ -F "type=audio" \ -F "messages=UklGRigAAABXQVZFZm10IBIAAAABAAEARKwAAIhYAQACABAAAABkYXRhAgAAAAEA" \ -F "max_tokens=32" \ -F "language=en" \ -F "stream=true"
::::: | ai_ref_knowledge | OPEA Documentation | Form input: ```bash curl http://${host_ip}:8888/v1/docsum \ -H "Content-Type: multipart/form-data" \ -F "type=audio" \ -F "messages=UklGRigAAABXQVZFZm10IBIAAAABAAEARKwAAIhYAQACABAAAABkYXRhAgAAAAEA" \ -F "max_tokens=32" \ -F "language=en" \ -F "stream=true"
::::: | Form input: ```bash curl http://${host_ip}:8888/v1/docsum \ -H "Content-Type: multipart/form-data" \ -F "type=audio" \ -F "messages=UklGRigAAABXQVZFZm10IBIAAAABAAEARKwAAIhYAQACABAAAABkYXRhAgAAAAEA" \ -F "max_tokens=32" \ -F "language=en" \ -F "stream=true"
::::: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0ffd2a1b-4fb7-4fe7-ab38-4d1cd95e135c | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 37 | opea-semantic-v1 | ec0521dc4dcae429 | :::::{tab-item} Audio
Audio uploads are not supported through *curl* commands, so use the UI to upload it. It is possible to pass base64 encoded strings of the audio file: | ai_ref_knowledge | OPEA Documentation | :::::{tab-item} Audio
Audio uploads are not supported through *curl* commands, so use the UI to upload it. It is possible to pass base64 encoded strings of the audio file: | :::::{tab-item} Audio
Audio uploads are not supported through *curl* commands, so use the UI to upload it. It is possible to pass base64 encoded strings of the audio file: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
106558d2-f499-4f10-be7e-22acf2419199 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 32 | opea-semantic-v1 | 665396dba635fd3f | ::::::{tab-set} :::::{tab-item} Text
JSON input:
```bash
curl -X POST http://${host_ip}:8888/v1/docsum \
-H "Content-Type: application/json" \
-d '{"type": "text", "messages": "Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI en... | ai_ref_knowledge | OPEA Documentation | ::::::{tab-set} :::::{tab-item} Text
JSON input:
```bash
curl -X POST http://${host_ip}:8888/v1/docsum \
-H "Content-Type: application/json" \
-d '{"type": "text", "messages": "Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI en... | ::::::{tab-set} :::::{tab-item} Text
JSON input:
```bash
curl -X POST http://${host_ip}:8888/v1/docsum \
-H "Content-Type: application/json" \
-d '{"type": "text", "messages": "Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI en... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
193fdfbf-a6d6-4eae-87e1-6e2cd70ef11e | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 25 | opea-semantic-v1 | c433241f75a7ca82 | service docker logs docsum-gaudi-tgi-server | grep Connected # If the service is ready, you will get the response like below. 2024-09-03T02:47:53.402023Z INFO text_generation_router::server: router/src/server.rs:2311: Connected
:::
:::: | ai_ref_knowledge | OPEA Documentation | service docker logs docsum-gaudi-tgi-server | grep Connected # If the service is ready, you will get the response like below. 2024-09-03T02:47:53.402023Z INFO text_generation_router::server: router/src/server.rs:2311: Connected
:::
:::: | service docker logs docsum-gaudi-tgi-server | grep Connected # If the service is ready, you will get the response like below. 2024-09-03T02:47:53.402023Z INFO text_generation_router::server: router/src/server.rs:2311: Connected
:::
:::: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1bb0108a-ed3d-4883-b15b-08a60d3bbcd4 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 54 | opea-semantic-v1 | 0047c0ae640e90ee | :::::{tab-item} truncate
Truncate mode will truncate the input text and keep only the first chunk, whose length is equal to `min(MAX_TOTAL_TOKENS - input.max_tokens - 50, MAX_INPUT_TOKENS)`. | ai_ref_knowledge | OPEA Documentation | :::::{tab-item} truncate
Truncate mode will truncate the input text and keep only the first chunk, whose length is equal to `min(MAX_TOTAL_TOKENS - input.max_tokens - 50, MAX_INPUT_TOKENS)`. | :::::{tab-item} truncate
Truncate mode will truncate the input text and keep only the first chunk, whose length is equal to `min(MAX_TOTAL_TOKENS - input.max_tokens - 50, MAX_INPUT_TOKENS)`. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1ddf1715-bc59-4937-a34c-631f993a2b6d | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 22 | opea-semantic-v1 | b6dcb5196e1662ce | ::::{tab-set} :::{tab-item} vllm
```bash
# vLLM service
docker logs docsum-gaudi-vllm-service 2>&1 | grep complete
# If the service is ready, you will get the response like below. INFO: Application startup complete. | ai_ref_knowledge | OPEA Documentation | ::::{tab-set} :::{tab-item} vllm
```bash
# vLLM service
docker logs docsum-gaudi-vllm-service 2>&1 | grep complete
# If the service is ready, you will get the response like below. INFO: Application startup complete. | ::::{tab-set} :::{tab-item} vllm
```bash
# vLLM service
docker logs docsum-gaudi-vllm-service 2>&1 | grep complete
# If the service is ready, you will get the response like below. INFO: Application startup complete. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1e803d07-f760-46cd-bd01-54c24df20d4a | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 16 | opea-semantic-v1 | ed8d82c344567631 | compose` with the provided YAML file to start all the services mentioned above as containers. The vLLM or TGI service can be used for DocSum.
::::{tab-set}
:::{tab-item} vllm | ai_ref_knowledge | OPEA Documentation | compose` with the provided YAML file to start all the services mentioned above as containers. The vLLM or TGI service can be used for DocSum.
::::{tab-set}
:::{tab-item} vllm | compose` with the provided YAML file to start all the services mentioned above as containers. The vLLM or TGI service can be used for DocSum.
::::{tab-set}
:::{tab-item} vllm | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1f704ec0-4c81-4f5f-bab8-699d372d27e9 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 14 | opea-semantic-v1 | afae1c3b455c57af | on a laptop, modify `BACKEND_SERVICE_ENDPOINT` to use `localhost` or `127.0.0.1` instead of `host_ip` inside `set_env.sh` for the backend to properly receive data from the UI.
Run the `set_env.sh` script. ```bash
cd $WORKSPACE/GenAIExamples/DocSum/docker_compose
source ./set_env.sh | ai_ref_knowledge | OPEA Documentation | on a laptop, modify `BACKEND_SERVICE_ENDPOINT` to use `localhost` or `127.0.0.1` instead of `host_ip` inside `set_env.sh` for the backend to properly receive data from the UI.
Run the `set_env.sh` script. ```bash
cd $WORKSPACE/GenAIExamples/DocSum/docker_compose
source ./set_env.sh | on a laptop, modify `BACKEND_SERVICE_ENDPOINT` to use `localhost` or `127.0.0.1` instead of `host_ip` inside `set_env.sh` for the backend to properly receive data from the UI.
Run the `set_env.sh` script. ```bash
cd $WORKSPACE/GenAIExamples/DocSum/docker_compose
source ./set_env.sh | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
211e0846-21c9-4295-ae46-4a15a1d4c312 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 65 | opea-semantic-v1 | 17bc8a22b4a1883f | The Gradio UI is recommended because it can work with multimedia documents, .doc, and .pdf files.
### Gradio UI
To access the frontend, open the following URL in a web browser: http://${host_ip}:5173. By default, the UI runs on port 5173 internally. A different host port can be used to access the frontend by modifying ... | ai_ref_knowledge | OPEA Documentation | The Gradio UI is recommended because it can work with multimedia documents, .doc, and .pdf files.
### Gradio UI
To access the frontend, open the following URL in a web browser: http://${host_ip}:5173. By default, the UI runs on port 5173 internally. A different host port can be used to access the frontend by modifying ... | The Gradio UI is recommended because it can work with multimedia documents, .doc, and .pdf files.
### Gradio UI
To access the frontend, open the following URL in a web browser: http://${host_ip}:5173. By default, the UI runs on port 5173 internally. A different host port can be used to access the frontend by modifying ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
25f2e2fb-40d8-4088-bb46-baba872b3218 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 46 | opea-semantic-v1 | 83fbb578e93a8a5b | as *auto*, *stuff*, *truncate*, *map_reduce*, or *refine*. The best strategy is determined from various factors including model context size limits and number of input tokens.
The following parameters can be adjusted to work with long context:
- "summary_type": can be "auto", "stuff", "truncate", "map_reduce", "refine"... | ai_ref_knowledge | OPEA Documentation | as *auto*, *stuff*, *truncate*, *map_reduce*, or *refine*. The best strategy is determined from various factors including model context size limits and number of input tokens.
The following parameters can be adjusted to work with long context:
- "summary_type": can be "auto", "stuff", "truncate", "map_reduce", "refine"... | as *auto*, *stuff*, *truncate*, *map_reduce*, or *refine*. The best strategy is determined from various factors including model context size limits and number of input tokens.
The following parameters can be adjusted to work with long context:
- "summary_type": can be "auto", "stuff", "truncate", "map_reduce", "refine"... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
2c4369e3-0deb-401a-ae12-6e72f504dba6 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 34 | opea-semantic-v1 | 76dfc0e9a238bdd2 | models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5." \ -F "max_tokens=32" \ -F "language=en" \ -F "stream=true"
Form input with Chinese mode:
```bash
curl http://${host_ip}:8888/v1/docsum \
-H "Content-Type: multipart/form-data" \
-F "type=text" \
... | ai_ref_knowledge | OPEA Documentation | models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5." \ -F "max_tokens=32" \ -F "language=en" \ -F "stream=true"
Form input with Chinese mode:
```bash
curl http://${host_ip}:8888/v1/docsum \
-H "Content-Type: multipart/form-data" \
-F "type=text" \
... | models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5." \ -F "max_tokens=32" \ -F "language=en" \ -F "stream=true"
Form input with Chinese mode:
```bash
curl http://${host_ip}:8888/v1/docsum \
-H "Content-Type: multipart/form-data" \
-F "type=text" \
... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
2ceb6329-2b71-4044-89f7-a9dcf13891a2 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 24 | opea-semantic-v1 | 1daa3c0c0cd5d3e7 | ::: :::{tab-item} TGI
```bash
# TGI service
docker logs docsum-gaudi-tgi-server | grep Connected
# If the service is ready, you will get the response like below. 2024-09-03T02:47:53.402023Z INFO text_generation_router::server: router/src/server.rs:2311: Connected | ai_ref_knowledge | OPEA Documentation | ::: :::{tab-item} TGI
```bash
# TGI service
docker logs docsum-gaudi-tgi-server | grep Connected
# If the service is ready, you will get the response like below. 2024-09-03T02:47:53.402023Z INFO text_generation_router::server: router/src/server.rs:2311: Connected | ::: :::{tab-item} TGI
```bash
# TGI service
docker logs docsum-gaudi-tgi-server | grep Connected
# If the service is ready, you will get the response like below. 2024-09-03T02:47:53.402023Z INFO text_generation_router::server: router/src/server.rs:2311: Connected | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
344168b6-6473-49df-ab27-9cc4d236e37c | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 12 | opea-semantic-v1 | 5a1ca4556def8a31 | associated tools. The tools and models listed in the table can be configured via environment variables in either the `set_env.sh` script or the `compose.yaml` file.
|Use Case Components | Tools | Model | Service Type |
|---------------- |--------------|-----------------------------|------------------ |
|LLM | vLLM or T... | ai_ref_knowledge | OPEA Documentation | associated tools. The tools and models listed in the table can be configured via environment variables in either the `set_env.sh` script or the `compose.yaml` file.
|Use Case Components | Tools | Model | Service Type |
|---------------- |--------------|-----------------------------|------------------ |
|LLM | vLLM or T... | associated tools. The tools and models listed in the table can be configured via environment variables in either the `set_env.sh` script or the `compose.yaml` file.
|Use Case Components | Tools | Model | Service Type |
|---------------- |--------------|-----------------------------|------------------ |
|LLM | vLLM or T... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
3f16c487-4eea-4081-8c87-695c9aa11b57 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 59 | opea-semantic-v1 | ef262e93331a7a21 | In this mode, `chunk_size` is set to `min(MAX_TOTAL_TOKENS - input.max_tokens - 50, MAX_INPUT_TOKENS)`.
```bash
curl http://${host_ip}:8888/v1/docsum \
-H "Content-Type: multipart/form-data" \
-F "type=text" \
-F "messages=" \
-F "max_tokens=32" \
-F "files=@/path to your file (.txt, .docx, .pdf)" \
-F "language=... | ai_ref_knowledge | OPEA Documentation | In this mode, `chunk_size` is set to `min(MAX_TOTAL_TOKENS - input.max_tokens - 50, MAX_INPUT_TOKENS)`.
```bash
curl http://${host_ip}:8888/v1/docsum \
-H "Content-Type: multipart/form-data" \
-F "type=text" \
-F "messages=" \
-F "max_tokens=32" \
-F "files=@/path to your file (.txt, .docx, .pdf)" \
-F "language=... | In this mode, `chunk_size` is set to `min(MAX_TOTAL_TOKENS - input.max_tokens - 50, MAX_INPUT_TOKENS)`.
```bash
curl http://${host_ip}:8888/v1/docsum \
-H "Content-Type: multipart/form-data" \
-F "type=text" \
-F "messages=" \
-F "max_tokens=32" \
-F "files=@/path to your file (.txt, .docx, .pdf)" \
-F "language=... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
42f441db-301d-402e-a07f-da195c2663a1 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 30 | opea-semantic-v1 | 8e58b3d6c994108d | ### DocSum Megaservice
Documents (.txt, .doc, .pdf), audio, and video can be uploaded to get a summary of the content. For each type of document, there are different input formats. | ai_ref_knowledge | OPEA Documentation | ### DocSum Megaservice
Documents (.txt, .doc, .pdf), audio, and video can be uploaded to get a summary of the content. For each type of document, there are different input formats. | ### DocSum Megaservice
Documents (.txt, .doc, .pdf), audio, and video can be uploaded to get a summary of the content. For each type of document, there are different input formats. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4580c619-e3e7-4664-9b01-9893702b5fbb | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 47 | opea-semantic-v1 | eb8c99aac1ae2d66 | token length for each chunk. Set to be different default value according to "summary_type". - "chunk_overlap": overlap token length between each chunk, default is 0.1*chunk_size
Select the "summary_type" of interest to see how to run with it. | ai_ref_knowledge | OPEA Documentation | token length for each chunk. Set to be different default value according to "summary_type". - "chunk_overlap": overlap token length between each chunk, default is 0.1*chunk_size
Select the "summary_type" of interest to see how to run with it. | token length for each chunk. Set to be different default value according to "summary_type". - "chunk_overlap": overlap token length between each chunk, default is 0.1*chunk_size
Select the "summary_type" of interest to see how to run with it. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
4881fbed-e5e3-4c1c-b479-5d79bcbdca50 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 8 | opea-semantic-v1 | c66d07f9e3b791ee | 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 | |
524de368-78dc-4933-96b9-41f0860f4edf | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 4 | opea-semantic-v1 | ee3a6628d1edc5a2 | of the UI can be deployed, this tutorial will focus solely on the Gradio UI because it can handle multimedia docuemnts, .doc, and .pdf files.
## Prerequisites | ai_ref_knowledge | OPEA Documentation | of the UI can be deployed, this tutorial will focus solely on the Gradio UI because it can handle multimedia docuemnts, .doc, and .pdf files.
## Prerequisites | of the UI can be deployed, this tutorial will focus solely on the Gradio UI because it can handle multimedia docuemnts, .doc, and .pdf files.
## Prerequisites | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
52a1401c-7468-45b3-bd44-ad9e9dd5442c | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 18 | opea-semantic-v1 | 8154f1de1424f898 | Run this command to see this info: ```bash docker ps -a
Sample output:
```bash
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
d02da5001212 opea/docsum-gradio-ui:latest "python docsum_ui_gr…" 2 minutes ago Up 19 seconds 0.0.0.0:5173->5173/tcp, [::]:5173->5173/tcp docsum-gaudi-ui-server
43de0d8ee9dd opea/docsum:la... | ai_ref_knowledge | OPEA Documentation | Run this command to see this info: ```bash docker ps -a
Sample output:
```bash
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
d02da5001212 opea/docsum-gradio-ui:latest "python docsum_ui_gr…" 2 minutes ago Up 19 seconds 0.0.0.0:5173->5173/tcp, [::]:5173->5173/tcp docsum-gaudi-ui-server
43de0d8ee9dd opea/docsum:la... | Run this command to see this info: ```bash docker ps -a
Sample output:
```bash
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
d02da5001212 opea/docsum-gradio-ui:latest "python docsum_ui_gr…" 2 minutes ago Up 19 seconds 0.0.0.0:5173->5173/tcp, [::]:5173->5173/tcp docsum-gaudi-ui-server
43de0d8ee9dd opea/docsum:la... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
58d0c26d-2e6d-4982-a783-c6d66ed69176 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 67 | opea-semantic-v1 | b04cf4ed4239fea4 | Navigate to the `docker compose` directory for this hardware platform. ```bash cd $WORKSPACE/GenAIExamples/DocSum/docker_compose/intel/hpu/gaudi
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/DocSum/docker_compose/intel/hpu/gaudi
To stop and remove all the containers, use the command below: | Navigate to the `docker compose` directory for this hardware platform. ```bash cd $WORKSPACE/GenAIExamples/DocSum/docker_compose/intel/hpu/gaudi
To stop and remove all the containers, use the command below: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
59c8effe-3a94-40a5-90fb-d10610e2104a | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 19 | opea-semantic-v1 | e908f6f9371a1a1f | whisper_serv…" 3 minutes ago Up 2 minutes 0.0.0.0:7066->7066/tcp, [::]:7066->7066/tcp docsum-gaudi-whisper-server 951abf0ebb5a opea/vllm:latest "python3 -m vllm.ent…" 3 minutes ago Up 2 minutes (healthy) 0.0.0.0:8008->80/tcp, [::]:8008->80/tcp docsum-gaudi-vllm-service
Each docker container's log can also be checked us... | ai_ref_knowledge | OPEA Documentation | whisper_serv…" 3 minutes ago Up 2 minutes 0.0.0.0:7066->7066/tcp, [::]:7066->7066/tcp docsum-gaudi-whisper-server 951abf0ebb5a opea/vllm:latest "python3 -m vllm.ent…" 3 minutes ago Up 2 minutes (healthy) 0.0.0.0:8008->80/tcp, [::]:8008->80/tcp docsum-gaudi-vllm-service
Each docker container's log can also be checked us... | whisper_serv…" 3 minutes ago Up 2 minutes 0.0.0.0:7066->7066/tcp, [::]:7066->7066/tcp docsum-gaudi-whisper-server 951abf0ebb5a opea/vllm:latest "python3 -m vllm.ent…" 3 minutes ago Up 2 minutes (healthy) 0.0.0.0:8008->80/tcp, [::]:8008->80/tcp docsum-gaudi-vllm-service
Each docker container's log can also be checked us... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5be174ec-f654-4bc1-bd5d-474f8e502d50 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 33 | opea-semantic-v1 | 21fffe2467380094 | and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."}'
Form input with English mode (default):
```bash
curl http://${host_ip}:8888/v1/docsum \
-H "Content-Type: multipart/form-data" ... | ai_ref_knowledge | OPEA Documentation | and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."}'
Form input with English mode (default):
```bash
curl http://${host_ip}:8888/v1/docsum \
-H "Content-Type: multipart/form-data" ... | and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."}'
Form input with English mode (default):
```bash
curl http://${host_ip}:8888/v1/docsum \
-H "Content-Type: multipart/form-data" ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
5c18f364-419c-4bc6-9aff-c87048eb3bb7 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 7 | opea-semantic-v1 | 7c9569e37d51d906 | defined in the `set_env.sh` for this example inside the `docker compose` folder. Specifically, for DocSum, append the following to the ssh command: ```bash -L 8888:localhost:8888
Set up a workspace and clone the [GenAIExamples](https://github.com/opea-project/GenAIExamples) GitHub repo. ```bash
export WORKSPACE=<Path>
... | ai_ref_knowledge | OPEA Documentation | defined in the `set_env.sh` for this example inside the `docker compose` folder. Specifically, for DocSum, append the following to the ssh command: ```bash -L 8888:localhost:8888
Set up a workspace and clone the [GenAIExamples](https://github.com/opea-project/GenAIExamples) GitHub repo. ```bash
export WORKSPACE=<Path>
... | defined in the `set_env.sh` for this example inside the `docker compose` folder. Specifically, for DocSum, append the following to the ssh command: ```bash -L 8888:localhost:8888
Set up a workspace and clone the [GenAIExamples](https://github.com/opea-project/GenAIExamples) GitHub repo. ```bash
export WORKSPACE=<Path>
... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
60aff543-9e1f-45a0-801d-c470cdc18b94 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 5 | opea-semantic-v1 | c484ebdc9902c108 | ## Prerequisites
To run the UI on a web browser external to the host machine such as a laptop, the following port(s) need to be port forwarded when using SSH to log in to the host machine:
- 8888: DocSum megaservice port | ai_ref_knowledge | OPEA Documentation | ## Prerequisites
To run the UI on a web browser external to the host machine such as a laptop, the following port(s) need to be port forwarded when using SSH to log in to the host machine:
- 8888: DocSum megaservice port | ## Prerequisites
To run the UI on a web browser external to the host machine such as a laptop, the following port(s) need to be port forwarded when using SSH to log in to the host machine:
- 8888: DocSum megaservice port | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
60e55159-6a78-4465-84d9-643ddfa4e429 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 49 | opea-semantic-v1 | 0f4ea7fadf5e114b | input token length is checked. If it exceeds `MAX_INPUT_TOKENS`, `summary_type` will automatically be set to `refine` mode. Otherwise, it will be set to `stuff` mode.
```bash
curl http://${host_ip}:8888/v1/docsum \
-H "Content-Type: multipart/form-data" \
-F "type=text" \
-F "messages=" \
-F "max_tokens=32" \
-F "... | ai_ref_knowledge | OPEA Documentation | input token length is checked. If it exceeds `MAX_INPUT_TOKENS`, `summary_type` will automatically be set to `refine` mode. Otherwise, it will be set to `stuff` mode.
```bash
curl http://${host_ip}:8888/v1/docsum \
-H "Content-Type: multipart/form-data" \
-F "type=text" \
-F "messages=" \
-F "max_tokens=32" \
-F "... | input token length is checked. If it exceeds `MAX_INPUT_TOKENS`, `summary_type` will automatically be set to `refine` mode. Otherwise, it will be set to `stuff` mode.
```bash
curl http://${host_ip}:8888/v1/docsum \
-H "Content-Type: multipart/form-data" \
-F "type=text" \
-F "messages=" \
-F "max_tokens=32" \
-F "... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
62d07e6e-9ec9-441a-8724-90d999784880 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 39 | opea-semantic-v1 | a4d0b1161307de40 | JSON input: ```bash curl -X POST http://${host_ip}:8888/v1/docsum \ -H "Content-Type: application/json" \ -d '{"type": "audio", "messages": "UklGRigAAABXQVZFZm10IBIAAAABAAEARKwAAIhYAQACABAAAABkYXRhAgAAAAEA"}'
Form input:
```bash
curl http://${host_ip}:8888/v1/docsum \
-H "Content-Type: multipart/form-data" \
-F "type... | ai_ref_knowledge | OPEA Documentation | JSON input: ```bash curl -X POST http://${host_ip}:8888/v1/docsum \ -H "Content-Type: application/json" \ -d '{"type": "audio", "messages": "UklGRigAAABXQVZFZm10IBIAAAABAAEARKwAAIhYAQACABAAAABkYXRhAgAAAAEA"}'
Form input:
```bash
curl http://${host_ip}:8888/v1/docsum \
-H "Content-Type: multipart/form-data" \
-F "type... | JSON input: ```bash curl -X POST http://${host_ip}:8888/v1/docsum \ -H "Content-Type: application/json" \ -d '{"type": "audio", "messages": "UklGRigAAABXQVZFZm10IBIAAAABAAEARKwAAIhYAQACABAAAABkYXRhAgAAAAEA"}'
Form input:
```bash
curl http://${host_ip}:8888/v1/docsum \
-H "Content-Type: multipart/form-data" \
-F "type... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
663558cd-d6e7-4de5-b749-0d1d3bdbbbbe | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 56 | opea-semantic-v1 | 3ed7836e0bfb223d | multipart/form-data" \ -F "type=text" \ -F "messages=" \ -F "max_tokens=32" \ -F "files=@/path to your file (.txt, .docx, .pdf)" \ -F "language=en" \ -F "summary_type=truncate"
::::: | ai_ref_knowledge | OPEA Documentation | multipart/form-data" \ -F "type=text" \ -F "messages=" \ -F "max_tokens=32" \ -F "files=@/path to your file (.txt, .docx, .pdf)" \ -F "language=en" \ -F "summary_type=truncate"
::::: | multipart/form-data" \ -F "type=text" \ -F "messages=" \ -F "max_tokens=32" \ -F "files=@/path to your file (.txt, .docx, .pdf)" \ -F "language=en" \ -F "summary_type=truncate"
::::: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
68d2ecb5-17b7-4676-9549-af4f33d15a28 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 53 | opea-semantic-v1 | 350f81ce01319f89 | multipart/form-data" \ -F "type=text" \ -F "messages=" \ -F "max_tokens=32" \ -F "files=@/path to your file (.txt, .docx, .pdf)" \ -F "language=en" \ -F "summary_type=stuff"
::::: | ai_ref_knowledge | OPEA Documentation | multipart/form-data" \ -F "type=text" \ -F "messages=" \ -F "max_tokens=32" \ -F "files=@/path to your file (.txt, .docx, .pdf)" \ -F "language=en" \ -F "summary_type=stuff"
::::: | multipart/form-data" \ -F "type=text" \ -F "messages=" \ -F "max_tokens=32" \ -F "files=@/path to your file (.txt, .docx, .pdf)" \ -F "language=en" \ -F "summary_type=stuff"
::::: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
6afa7b3c-1587-4a83-8774-198132261460 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 50 | opea-semantic-v1 | 5333f841ee49a2d4 | multipart/form-data" \ -F "type=text" \ -F "messages=" \ -F "max_tokens=32" \ -F "files=@/path to your file (.txt, .docx, .pdf)" \ -F "language=en" \ -F "summary_type=auto"
::::: | ai_ref_knowledge | OPEA Documentation | multipart/form-data" \ -F "type=text" \ -F "messages=" \ -F "max_tokens=32" \ -F "files=@/path to your file (.txt, .docx, .pdf)" \ -F "language=en" \ -F "summary_type=auto"
::::: | multipart/form-data" \ -F "type=text" \ -F "messages=" \ -F "max_tokens=32" \ -F "files=@/path to your file (.txt, .docx, .pdf)" \ -F "language=en" \ -F "summary_type=auto"
::::: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7275de4f-1548-4404-8530-e538b570f69f | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 44 | opea-semantic-v1 | fd69596a7a2a7bb0 | \ -H "Content-Type: multipart/form-data" \ -F "type=video" \ -F "messages=convert your video to base64 data type" \ -F "max_tokens=32" \ -F "language=en" \ -F "stream=true"
::::: | ai_ref_knowledge | OPEA Documentation | \ -H "Content-Type: multipart/form-data" \ -F "type=video" \ -F "messages=convert your video to base64 data type" \ -F "max_tokens=32" \ -F "language=en" \ -F "stream=true"
::::: | \ -H "Content-Type: multipart/form-data" \ -F "type=video" \ -F "messages=convert your video to base64 data type" \ -F "max_tokens=32" \ -F "language=en" \ -F "stream=true"
::::: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
74a8ae4c-6bdc-47a2-b712-0ba4cd9fcbb9 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 57 | opea-semantic-v1 | 758d958068dba85f | :::::{tab-item} map_reduce
Map_reduce mode will split the inputs into multiple chunks, map each document to an individual summary, and consolidate all summaries into a single global summary. `stream=True` is not allowed here. | ai_ref_knowledge | OPEA Documentation | :::::{tab-item} map_reduce
Map_reduce mode will split the inputs into multiple chunks, map each document to an individual summary, and consolidate all summaries into a single global summary. `stream=True` is not allowed here. | :::::{tab-item} map_reduce
Map_reduce mode will split the inputs into multiple chunks, map each document to an individual summary, and consolidate all summaries into a single global summary. `stream=True` is not allowed here. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
77177448-4fe6-416e-9c99-54711918745a | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 36 | opea-semantic-v1 | a46354373b9fe3ac | multipart/form-data" \ -F "type=text" \ -F "messages=" \ -F "files=@/path to your file (.txt, .docx, .pdf)" \ -F "max_tokens=32" \ -F "language=en" \ -F "stream=true"
::::: | ai_ref_knowledge | OPEA Documentation | multipart/form-data" \ -F "type=text" \ -F "messages=" \ -F "files=@/path to your file (.txt, .docx, .pdf)" \ -F "max_tokens=32" \ -F "language=en" \ -F "stream=true"
::::: | multipart/form-data" \ -F "type=text" \ -F "messages=" \ -F "files=@/path to your file (.txt, .docx, .pdf)" \ -F "max_tokens=32" \ -F "language=en" \ -F "stream=true"
::::: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7a602acf-1d80-485b-aef6-a07ca80188af | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 66 | opea-semantic-v1 | 0f244e84024bbbab | modifying the `FRONTEND_SERVICE_PORT` environment variable. For reference, the port mapping in the `compose.yaml` file is shown below: ```yaml docsum-gradio-ui: image: ${REGISTRY:-opea}/docsum-gradio-ui:${TAG:-latest} ... ports: - "${FRONTEND_SERVICE_PORT:-5173}:5173"
After making this change, rebuild and restart the c... | ai_ref_knowledge | OPEA Documentation | modifying the `FRONTEND_SERVICE_PORT` environment variable. For reference, the port mapping in the `compose.yaml` file is shown below: ```yaml docsum-gradio-ui: image: ${REGISTRY:-opea}/docsum-gradio-ui:${TAG:-latest} ... ports: - "${FRONTEND_SERVICE_PORT:-5173}:5173"
After making this change, rebuild and restart the c... | modifying the `FRONTEND_SERVICE_PORT` environment variable. For reference, the port mapping in the `compose.yaml` file is shown below: ```yaml docsum-gradio-ui: image: ${REGISTRY:-opea}/docsum-gradio-ui:${TAG:-latest} ... ports: - "${FRONTEND_SERVICE_PORT:-5173}:5173"
After making this change, rebuild and restart the c... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
7df17988-b649-43ed-866d-7ee30408edda | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 27 | opea-semantic-v1 | 0539b38dde4436ed | to verify the vLLM or TGI service: ```bash curl http://${host_ip}:8008/v1/chat/completions \ -X POST \ -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \ -H 'Content-Type: application/json'
Sample output:
```bash
{"generated_text":"\nDeep learning is a sub-discipline of mac... | ai_ref_knowledge | OPEA Documentation | to verify the vLLM or TGI service: ```bash curl http://${host_ip}:8008/v1/chat/completions \ -X POST \ -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \ -H 'Content-Type: application/json'
Sample output:
```bash
{"generated_text":"\nDeep learning is a sub-discipline of mac... | to verify the vLLM or TGI service: ```bash curl http://${host_ip}:8008/v1/chat/completions \ -X POST \ -d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \ -H 'Content-Type: application/json'
Sample output:
```bash
{"generated_text":"\nDeep learning is a sub-discipline of mac... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
8170b17f-6be4-4614-9125-b92d61c85acc | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 51 | opea-semantic-v1 | 4adb72f7f3b8adbb | :::::{tab-item} stuff
In this mode the LLM microservice generates a summary based on the entire input text. In this case, set `MAX_INPUT_TOKENS` and `MAX_TOTAL_TOKENS` according to the model and device memory. Otherwise, it may exceed the LLM context limit and raise errors when provided a longer context. | ai_ref_knowledge | OPEA Documentation | :::::{tab-item} stuff
In this mode the LLM microservice generates a summary based on the entire input text. In this case, set `MAX_INPUT_TOKENS` and `MAX_TOTAL_TOKENS` according to the model and device memory. Otherwise, it may exceed the LLM context limit and raise errors when provided a longer context. | :::::{tab-item} stuff
In this mode the LLM microservice generates a summary based on the entire input text. In this case, set `MAX_INPUT_TOKENS` and `MAX_TOTAL_TOKENS` according to the model and device memory. Otherwise, it may exceed the LLM context limit and raise errors when provided a longer context. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
89035a79-d5db-41d3-adf6-b75bc90ef1f0 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 64 | opea-semantic-v1 | a86e1e0a106334e3 | multipart/form-data" \ -F "type=text" \ -F "messages=" \ -F "max_tokens=32" \ -F "files=@/path to your file (.txt, .docx, .pdf)" \ -F "language=en" \ -F "summary_type=refine"
::::: | ai_ref_knowledge | OPEA Documentation | multipart/form-data" \ -F "type=text" \ -F "messages=" \ -F "max_tokens=32" \ -F "files=@/path to your file (.txt, .docx, .pdf)" \ -F "language=en" \ -F "summary_type=refine"
::::: | multipart/form-data" \ -F "type=text" \ -F "messages=" \ -F "max_tokens=32" \ -F "files=@/path to your file (.txt, .docx, .pdf)" \ -F "language=en" \ -F "summary_type=refine"
::::: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
915ca499-5e12-4988-bf2f-a534d9ce9d44 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 15 | opea-semantic-v1 | 6bcbf1f21aaf5060 | Navigate to the `docker compose` directory for this hardware platform. ```bash cd $WORKSPACE/GenAIExamples/DocSum/docker_compose/intel/hpu/gaudi
Run `docker compose` with the provided YAML file to start all the services mentioned above as containers. The vLLM or TGI service can be used for DocSum. | ai_ref_knowledge | OPEA Documentation | Navigate to the `docker compose` directory for this hardware platform. ```bash cd $WORKSPACE/GenAIExamples/DocSum/docker_compose/intel/hpu/gaudi
Run `docker compose` with the provided YAML file to start all the services mentioned above as containers. The vLLM or TGI service can be used for DocSum. | Navigate to the `docker compose` directory for this hardware platform. ```bash cd $WORKSPACE/GenAIExamples/DocSum/docker_compose/intel/hpu/gaudi
Run `docker compose` with the provided YAML file to start all the services mentioned above as containers. The vLLM or TGI service can be used for DocSum. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
957f6cfa-1bdf-46ea-a3d2-46f798c3ea11 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 60 | opea-semantic-v1 | 6cf556e3eb0c9276 | multipart/form-data" \ -F "type=text" \ -F "messages=" \ -F "max_tokens=32" \ -F "files=@/path to your file (.txt, .docx, .pdf)" \ -F "language=en" \ -F "summary_type=map_reduce"
::::: | ai_ref_knowledge | OPEA Documentation | multipart/form-data" \ -F "type=text" \ -F "messages=" \ -F "max_tokens=32" \ -F "files=@/path to your file (.txt, .docx, .pdf)" \ -F "language=en" \ -F "summary_type=map_reduce"
::::: | multipart/form-data" \ -F "type=text" \ -F "messages=" \ -F "max_tokens=32" \ -F "files=@/path to your file (.txt, .docx, .pdf)" \ -F "language=en" \ -F "summary_type=map_reduce"
::::: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
95de9b2e-e0e8-4e1a-8e72-d2ea2f1b5c1f | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 52 | opea-semantic-v1 | 4fd84abe1d53ec99 | and `MAX_TOTAL_TOKENS` according to the model and device memory. Otherwise, it may exceed the LLM context limit and raise errors when provided a longer context.
```bash
curl http://${host_ip}:8888/v1/docsum \
-H "Content-Type: multipart/form-data" \
-F "type=text" \
-F "messages=" \
-F "max_tokens=32" \
-F "files=... | ai_ref_knowledge | OPEA Documentation | and `MAX_TOTAL_TOKENS` according to the model and device memory. Otherwise, it may exceed the LLM context limit and raise errors when provided a longer context.
```bash
curl http://${host_ip}:8888/v1/docsum \
-H "Content-Type: multipart/form-data" \
-F "type=text" \
-F "messages=" \
-F "max_tokens=32" \
-F "files=... | and `MAX_TOTAL_TOKENS` according to the model and device memory. Otherwise, it may exceed the LLM context limit and raise errors when provided a longer context.
```bash
curl http://${host_ip}:8888/v1/docsum \
-H "Content-Type: multipart/form-data" \
-F "type=text" \
-F "messages=" \
-F "max_tokens=32" \
-F "files=... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
9cb01ec7-631a-4f81-bd3a-0283872c608e | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 17 | opea-semantic-v1 | 6efa4e0f4b18e671 | WARN[0000] The "http_proxy" variable is not set. Defaulting to a blank string. WARN[0000] The "https_proxy" variable is not set. Defaulting to a blank string.
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 | WARN[0000] The "http_proxy" variable is not set. Defaulting to a blank string. WARN[0000] The "https_proxy" variable is not set. Defaulting to a blank string.
Check if all the containers launched via `docker compose` are running i.e. each container's `STATUS` is `Up` and `Healthy`. | WARN[0000] The "http_proxy" variable is not set. Defaulting to a blank string. WARN[0000] The "https_proxy" variable is not set. Defaulting to a blank string.
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 | |
9fbad018-5801-42c2-a9c6-cae979d81512 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 31 | opea-semantic-v1 | fe9841efe2bc9471 | .doc, .pdf), audio, and video can be uploaded to get a summary of the content. For each type of document, there are different input formats.
::::::{tab-set}
:::::{tab-item} Text | ai_ref_knowledge | OPEA Documentation | .doc, .pdf), audio, and video can be uploaded to get a summary of the content. For each type of document, there are different input formats.
::::::{tab-set}
:::::{tab-item} Text | .doc, .pdf), audio, and video can be uploaded to get a summary of the content. For each type of document, there are different input formats.
::::::{tab-set}
:::::{tab-item} Text | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a04e16ef-adb9-4eca-ad0c-f2be9d96df33 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 28 | opea-semantic-v1 | adf047f8ac9a79d5 | ### LLM Microservice
```bash
curl http://${host_ip}:9000/v1/docsum \
-X POST \
-d '{"query":"Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbeddi... | ai_ref_knowledge | OPEA Documentation | ### LLM Microservice
```bash
curl http://${host_ip}:9000/v1/docsum \
-X POST \
-d '{"query":"Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbeddi... | ### LLM Microservice
```bash
curl http://${host_ip}:9000/v1/docsum \
-X POST \
-d '{"query":"Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbeddi... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
a45a0859-7ecc-4454-9e3e-2eac0f8d4b8b | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 43 | opea-semantic-v1 | 605d1f50cc6ad542 | JSON input: ```bash curl -X POST http://${host_ip}:8888/v1/docsum \ -H "Content-Type: application/json" \ -d '{"type": "video", "messages": "convert your video to base64 data type"}'
Form input:
```bash
curl http://${host_ip}:8888/v1/docsum \
-H "Content-Type: multipart/form-data" \
-F "type=video" \
-F "messages=co... | ai_ref_knowledge | OPEA Documentation | JSON input: ```bash curl -X POST http://${host_ip}:8888/v1/docsum \ -H "Content-Type: application/json" \ -d '{"type": "video", "messages": "convert your video to base64 data type"}'
Form input:
```bash
curl http://${host_ip}:8888/v1/docsum \
-H "Content-Type: multipart/form-data" \
-F "type=video" \
-F "messages=co... | JSON input: ```bash curl -X POST http://${host_ip}:8888/v1/docsum \ -H "Content-Type: application/json" \ -d '{"type": "video", "messages": "convert your video to base64 data type"}'
Form input:
```bash
curl http://${host_ip}:8888/v1/docsum \
-H "Content-Type: multipart/form-data" \
-F "type=video" \
-F "messages=co... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ab740f75-e350-4ec6-9060-e89b1275f8f6 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 3 | opea-semantic-v1 | 8c7fda30ead6cb7d | 1. ASR 2. LLM with vLLM or TGI
This solution is designed to demonstrate the use of the `Intel/neural-chat-7b-v3-3` model on the Intel® Gaudi® AI Accelerators to take a document (.txt,.doc,.pdf), audio, or video file as the input and generate a summary. The steps will involve setting up Docker containers, uploading docu... | ai_ref_knowledge | OPEA Documentation | 1. ASR 2. LLM with vLLM or TGI
This solution is designed to demonstrate the use of the `Intel/neural-chat-7b-v3-3` model on the Intel® Gaudi® AI Accelerators to take a document (.txt,.doc,.pdf), audio, or video file as the input and generate a summary. The steps will involve setting up Docker containers, uploading docu... | 1. ASR 2. LLM with vLLM or TGI
This solution is designed to demonstrate the use of the `Intel/neural-chat-7b-v3-3` model on the Intel® Gaudi® AI Accelerators to take a document (.txt,.doc,.pdf), audio, or video file as the input and generate a summary. The steps will involve setting up Docker containers, uploading docu... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
bc081aa2-3546-4c4a-8694-657a62d28238 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 62 | opea-semantic-v1 | d11a090674d00efb | chunks, generate a summary for the first one, combine it with the second summary, and repeats with all remaining chunks to get the final summary.
In this mode, default `chunk_size` is set to `min(MAX_TOTAL_TOKENS - 2 * input.max_tokens - 128, MAX_INPUT_TOKENS)`. | ai_ref_knowledge | OPEA Documentation | chunks, generate a summary for the first one, combine it with the second summary, and repeats with all remaining chunks to get the final summary.
In this mode, default `chunk_size` is set to `min(MAX_TOTAL_TOKENS - 2 * input.max_tokens - 128, MAX_INPUT_TOKENS)`. | chunks, generate a summary for the first one, combine it with the second summary, and repeats with all remaining chunks to get the final summary.
In this mode, default `chunk_size` is set to `min(MAX_TOTAL_TOKENS - 2 * input.max_tokens - 128, MAX_INPUT_TOKENS)`. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
bd22751c-830d-4c3c-adc3-bdb17728fc7d | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 63 | opea-semantic-v1 | c52849f5998e2099 | In this mode, default `chunk_size` is set to `min(MAX_TOTAL_TOKENS - 2 * input.max_tokens - 128, MAX_INPUT_TOKENS)`.
```bash
curl http://${host_ip}:8888/v1/docsum \
-H "Content-Type: multipart/form-data" \
-F "type=text" \
-F "messages=" \
-F "max_tokens=32" \
-F "files=@/path to your file (.txt, .docx, .pdf)" \
... | ai_ref_knowledge | OPEA Documentation | In this mode, default `chunk_size` is set to `min(MAX_TOTAL_TOKENS - 2 * input.max_tokens - 128, MAX_INPUT_TOKENS)`.
```bash
curl http://${host_ip}:8888/v1/docsum \
-H "Content-Type: multipart/form-data" \
-F "type=text" \
-F "messages=" \
-F "max_tokens=32" \
-F "files=@/path to your file (.txt, .docx, .pdf)" \
... | In this mode, default `chunk_size` is set to `min(MAX_TOTAL_TOKENS - 2 * input.max_tokens - 128, MAX_INPUT_TOKENS)`.
```bash
curl http://${host_ip}:8888/v1/docsum \
-H "Content-Type: multipart/form-data" \
-F "type=text" \
-F "messages=" \
-F "max_tokens=32" \
-F "files=@/path to your file (.txt, .docx, .pdf)" \
... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
bebe2b33-28ae-458a-8b47-4832d3eacb99 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 2 | opea-semantic-v1 | f740377ad8387f4a | The OPEA GenAIComps microservices used to deploy a single node vLLM or TGI megaservice solution for DocSum are listed below:
1. ASR
2. LLM with vLLM or TGI | ai_ref_knowledge | OPEA Documentation | The OPEA GenAIComps microservices used to deploy a single node vLLM or TGI megaservice solution for DocSum are listed below:
1. ASR
2. LLM with vLLM or TGI | The OPEA GenAIComps microservices used to deploy a single node vLLM or TGI megaservice solution for DocSum are listed below:
1. ASR
2. LLM with vLLM or TGI | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
c34dcd5b-e04c-44a3-b22f-5cbf8832b371 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 41 | opea-semantic-v1 | 2abdb76f60d0cefc | :::::{tab-item} Video
Video uploads are not supported through *curl* commands, so use the UI to upload it. It is possible to pass base64 strings of the video file as the value for the message parameter: | ai_ref_knowledge | OPEA Documentation | :::::{tab-item} Video
Video uploads are not supported through *curl* commands, so use the UI to upload it. It is possible to pass base64 strings of the video file as the value for the message parameter: | :::::{tab-item} Video
Video uploads are not supported through *curl* commands, so use the UI to upload it. It is possible to pass base64 strings of the video file as the value for the message parameter: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
cdff1e13-a48c-4bfd-9d01-1fa6fcfa64de | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 6 | opea-semantic-v1 | 767324e4e0dde64b | a laptop, the following port(s) need to be port forwarded when using SSH to log in to the host machine: - 8888: DocSum megaservice port
This port is used for `BACKEND_SERVICE_ENDPOINT` defined in the `set_env.sh` for this example inside the `docker compose` folder. Specifically, for DocSum, append the following to the ... | ai_ref_knowledge | OPEA Documentation | a laptop, the following port(s) need to be port forwarded when using SSH to log in to the host machine: - 8888: DocSum megaservice port
This port is used for `BACKEND_SERVICE_ENDPOINT` defined in the `set_env.sh` for this example inside the `docker compose` folder. Specifically, for DocSum, append the following to the ... | a laptop, the following port(s) need to be port forwarded when using SSH to log in to the host machine: - 8888: DocSum megaservice port
This port is used for `BACKEND_SERVICE_ENDPOINT` defined in the `set_env.sh` for this example inside the `docker compose` folder. Specifically, for DocSum, append the following to the ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d4f2cfeb-dc43-46ce-b50b-b45b5cbb0fac | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 13 | opea-semantic-v1 | 6601e4d6a21aad8a | or TGI | Intel/neural-chat-7b-v3-3 | OPEA Microservice | |ASR | Whisper | openai/whisper-small | OPEA Microservice | |UI | | NA | Gateway Service |
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_... | ai_ref_knowledge | OPEA Documentation | or TGI | Intel/neural-chat-7b-v3-3 | OPEA Microservice | |ASR | Whisper | openai/whisper-small | OPEA Microservice | |UI | | NA | Gateway Service |
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_... | or TGI | Intel/neural-chat-7b-v3-3 | OPEA Microservice | |ASR | Whisper | openai/whisper-small | OPEA Microservice | |UI | | NA | Gateway Service |
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_... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
d514605b-a1ec-4e05-ab90-20082291f447 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 48 | opea-semantic-v1 | af79420ff1d738e7 | ::::::{tab-set} :::::{tab-item} auto
"summary_type" is set to be "auto" by default, in this mode the input token length is checked. If it exceeds `MAX_INPUT_TOKENS`, `summary_type` will automatically be set to `refine` mode. Otherwise, it will be set to `stuff` mode. | ai_ref_knowledge | OPEA Documentation | ::::::{tab-set} :::::{tab-item} auto
"summary_type" is set to be "auto" by default, in this mode the input token length is checked. If it exceeds `MAX_INPUT_TOKENS`, `summary_type` will automatically be set to `refine` mode. Otherwise, it will be set to `stuff` mode. | ::::::{tab-set} :::::{tab-item} auto
"summary_type" is set to be "auto" by default, in this mode the input token length is checked. If it exceeds `MAX_INPUT_TOKENS`, `summary_type` will automatically be set to `refine` mode. Otherwise, it will be set to `stuff` mode. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
db6d2c59-5ea1-4735-be70-dc558ca6f1e9 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 9 | opea-semantic-v1 | 10f889425a3f7f8d | and generate a [user access token](https://huggingface.co/docs/transformers.js/en/guides/private#step-1-generating-a-user-access-token). The [Intel/neural-chat-7b-v3-3](https://huggingface.co/Intel/neural-chat-7b-v3-3) model does not need special access, but the token can be used with other models requiring access.
Set... | ai_ref_knowledge | OPEA Documentation | and generate a [user access token](https://huggingface.co/docs/transformers.js/en/guides/private#step-1-generating-a-user-access-token). The [Intel/neural-chat-7b-v3-3](https://huggingface.co/Intel/neural-chat-7b-v3-3) model does not need special access, but the token can be used with other models requiring access.
Set... | and generate a [user access token](https://huggingface.co/docs/transformers.js/en/guides/private#step-1-generating-a-user-access-token). The [Intel/neural-chat-7b-v3-3](https://huggingface.co/Intel/neural-chat-7b-v3-3) model does not need special access, but the token can be used with other models requiring access.
Set... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ddb2b6f5-41f0-4489-ac85-ba1bb1762f50 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 11 | opea-semantic-v1 | fe9a3e5e8e4e0394 | ## Use Case Setup
DocSum 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
DocSum 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
DocSum 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 | |
e14eb059-e275-4a02-890a-b8130c12cd46 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 42 | opea-semantic-v1 | df294feedb3cf80b | so use the UI to upload it. It is possible to pass base64 strings of the video file as the value for the message parameter:
JSON input:
```bash
curl -X POST http://${host_ip}:8888/v1/docsum \
-H "Content-Type: application/json" \
-d '{"type": "video", "messages": "convert your video to base64 data type"}' | ai_ref_knowledge | OPEA Documentation | so use the UI to upload it. It is possible to pass base64 strings of the video file as the value for the message parameter:
JSON input:
```bash
curl -X POST http://${host_ip}:8888/v1/docsum \
-H "Content-Type: application/json" \
-d '{"type": "video", "messages": "convert your video to base64 data type"}' | so use the UI to upload it. It is possible to pass base64 strings of the video file as the value for the message parameter:
JSON input:
```bash
curl -X POST http://${host_ip}:8888/v1/docsum \
-H "Content-Type: application/json" \
-d '{"type": "video", "messages": "convert your video to base64 data type"}' | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e24425a8-57f9-42aa-8f9b-bcfcbeb54146 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 26 | opea-semantic-v1 | f941c52cf401d450 | ::: ::::
Then try the `cURL` command to verify the vLLM or TGI service:
```bash
curl http://${host_ip}:8008/v1/chat/completions \
-X POST \
-d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \
-H 'Content-Type: application/json' | ai_ref_knowledge | OPEA Documentation | ::: ::::
Then try the `cURL` command to verify the vLLM or TGI service:
```bash
curl http://${host_ip}:8008/v1/chat/completions \
-X POST \
-d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \
-H 'Content-Type: application/json' | ::: ::::
Then try the `cURL` command to verify the vLLM or TGI service:
```bash
curl http://${host_ip}:8008/v1/chat/completions \
-X POST \
-d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":17, "do_sample": true}}' \
-H 'Content-Type: application/json' | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e322ea37-b07b-41b6-bbed-04340e75dffd | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 38 | opea-semantic-v1 | 96557196956088b2 | are not supported through *curl* commands, so use the UI to upload it. It is possible to pass base64 encoded strings of the audio file:
JSON input:
```bash
curl -X POST http://${host_ip}:8888/v1/docsum \
-H "Content-Type: application/json" \
-d '{"type": "audio", "messages": "UklGRigAAABXQVZFZm10IBIAAAABAAEARKwAAIhYA... | ai_ref_knowledge | OPEA Documentation | are not supported through *curl* commands, so use the UI to upload it. It is possible to pass base64 encoded strings of the audio file:
JSON input:
```bash
curl -X POST http://${host_ip}:8888/v1/docsum \
-H "Content-Type: application/json" \
-d '{"type": "audio", "messages": "UklGRigAAABXQVZFZm10IBIAAAABAAEARKwAAIhYA... | are not supported through *curl* commands, so use the UI to upload it. It is possible to pass base64 encoded strings of the audio file:
JSON input:
```bash
curl -X POST http://${host_ip}:8888/v1/docsum \
-H "Content-Type: application/json" \
-d '{"type": "audio", "messages": "UklGRigAAABXQVZFZm10IBIAAAABAAEARKwAAIhYA... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
e9f6371a-0eef-4493-b4ca-dd5ec897c591 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 55 | opea-semantic-v1 | 6720264be6b83386 | Truncate mode will truncate the input text and keep only the first chunk, whose length is equal to `min(MAX_TOTAL_TOKENS - input.max_tokens - 50, MAX_INPUT_TOKENS)`.
```bash
curl http://${host_ip}:8888/v1/docsum \
-H "Content-Type: multipart/form-data" \
-F "type=text" \
-F "messages=" \
-F "max_tokens=32" \
-F "f... | ai_ref_knowledge | OPEA Documentation | Truncate mode will truncate the input text and keep only the first chunk, whose length is equal to `min(MAX_TOTAL_TOKENS - input.max_tokens - 50, MAX_INPUT_TOKENS)`.
```bash
curl http://${host_ip}:8888/v1/docsum \
-H "Content-Type: multipart/form-data" \
-F "type=text" \
-F "messages=" \
-F "max_tokens=32" \
-F "f... | Truncate mode will truncate the input text and keep only the first chunk, whose length is equal to `min(MAX_TOTAL_TOKENS - input.max_tokens - 50, MAX_INPUT_TOKENS)`.
```bash
curl http://${host_ip}:8888/v1/docsum \
-H "Content-Type: multipart/form-data" \
-F "type=text" \
-F "messages=" \
-F "max_tokens=32" \
-F "f... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ec35ef3e-f413-494f-883a-738e6f4041ba | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 10 | opea-semantic-v1 | dcf1f1b6df4bde6b | Set the `host_ip` environment variable to deploy the microservices on the endpoints enabled with ports: ```bash export host_ip=$(hostname -I | awk '{print $1}')
## Use Case Setup | ai_ref_knowledge | OPEA Documentation | Set the `host_ip` environment variable to deploy the microservices on the endpoints enabled with ports: ```bash export host_ip=$(hostname -I | awk '{print $1}')
## Use Case Setup | Set the `host_ip` environment variable to deploy the microservices on the endpoints enabled with ports: ```bash export host_ip=$(hostname -I | awk '{print $1}')
## Use Case Setup | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
ec6db651-80f4-4494-95fd-2dbf33947a1c | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 0 | opea-semantic-v1 | 6482ecf702a16dd3 | # Single node on-prem deployment on Gaudi AI Accelerator
This section covers the single-node on-prem deployment of the DocSum example. It will show how to build a document summarization service using the `Intel/neural-chat-7b-v3-3` model deployed on Intel® Gaudi® AI Accelerators. To quickly learn about OPEA and set up ... | ai_ref_knowledge | OPEA Documentation | # Single node on-prem deployment on Gaudi AI Accelerator
This section covers the single-node on-prem deployment of the DocSum example. It will show how to build a document summarization service using the `Intel/neural-chat-7b-v3-3` model deployed on Intel® Gaudi® AI Accelerators. To quickly learn about OPEA and set up ... | # Single node on-prem deployment on Gaudi AI Accelerator
This section covers the single-node on-prem deployment of the DocSum example. It will show how to build a document summarization service using the `Intel/neural-chat-7b-v3-3` model deployed on Intel® Gaudi® AI Accelerators. To quickly learn about OPEA and set up ... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f2887c5d-e8c4-4e9d-8d89-f3fa24df4c80 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 1 | opea-semantic-v1 | ed4590e3c84eb5e1 | ## Overview
The OPEA GenAIComps microservices used to deploy a single node vLLM or TGI megaservice solution for DocSum 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 DocSum are listed below: | ## Overview
The OPEA GenAIComps microservices used to deploy a single node vLLM or TGI megaservice solution for DocSum are listed below: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f3bc5e17-7103-4126-9339-42f18ca65389 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 20 | opea-semantic-v1 | 37bf7d094accc347 | take a few minutes to download the model files and complete the warm-up process. Once this is finished, the service will be ready for use.
Try the command below to check whether the LLM service is ready. It uses the name of the image to check the status. | ai_ref_knowledge | OPEA Documentation | take a few minutes to download the model files and complete the warm-up process. Once this is finished, the service will be ready for use.
Try the command below to check whether the LLM service is ready. It uses the name of the image to check the status. | take a few minutes to download the model files and complete the warm-up process. Once this is finished, the service will be ready for use.
Try the command below to check whether the LLM service is ready. It uses the name of the image to check the status. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f627aec3-9a64-42bc-b813-683b4a5c084d | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 61 | opea-semantic-v1 | befebf389ec02caa | :::::{tab-item} refine
Refine mode will split the inputs into multiple chunks, generate a summary for the first one, combine it with the second summary, and repeats with all remaining chunks to get the final summary. | ai_ref_knowledge | OPEA Documentation | :::::{tab-item} refine
Refine mode will split the inputs into multiple chunks, generate a summary for the first one, combine it with the second summary, and repeats with all remaining chunks to get the final summary. | :::::{tab-item} refine
Refine mode will split the inputs into multiple chunks, generate a summary for the first one, combine it with the second summary, and repeats with all remaining chunks to get the final summary. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f709c02a-1285-4622-92c8-69201cb7b6ca | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 23 | opea-semantic-v1 | 0f6a1436bc93b1f9 | service docker logs docsum-gaudi-vllm-service 2>&1 | grep complete # If the service is ready, you will get the response like below. INFO: Application startup complete.
:::
:::{tab-item} TGI | ai_ref_knowledge | OPEA Documentation | service docker logs docsum-gaudi-vllm-service 2>&1 | grep complete # If the service is ready, you will get the response like below. INFO: Application startup complete.
:::
:::{tab-item} TGI | service docker logs docsum-gaudi-vllm-service 2>&1 | grep complete # If the service is ready, you will get the response like below. INFO: Application startup complete.
:::
:::{tab-item} TGI | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
f9324dc6-f47d-4348-b738-23f8e2155983 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 29 | opea-semantic-v1 | b42a60fcfe45aa2d | text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."}' \ -H 'Content-Type: application/json'
The output is the summary of the input given to this microservice. | ai_ref_knowledge | OPEA Documentation | text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."}' \ -H 'Content-Type: application/json'
The output is the summary of the input given to this microservice. | text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."}' \ -H 'Content-Type: application/json'
The output is the summary of the input given to this microservice. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
fa2be14a-cfae-4ca6-8d7a-c400f625ab24 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 35 | opea-semantic-v1 | cc4201e15d453423 | ```bash curl http://${host_ip}:8888/v1/docsum \ -H "Content-Type: multipart/form-data" \ -F "type=text" \ -F "messages=2024年9月26日，北京——今日，英特尔正式发布英特尔® 至强® 6性能核处理器（代号Granite Rapids），为AI、数据分析、科学计算等计算密集型业务提供卓越性能。" \ -F "max_tokens=32" \ -F "language=zh" \ -F "stream=true"
Uploading a file:
```bash
curl http://${host_ip}:888... | ai_ref_knowledge | OPEA Documentation | ```bash curl http://${host_ip}:8888/v1/docsum \ -H "Content-Type: multipart/form-data" \ -F "type=text" \ -F "messages=2024年9月26日，北京——今日，英特尔正式发布英特尔® 至强® 6性能核处理器（代号Granite Rapids），为AI、数据分析、科学计算等计算密集型业务提供卓越性能。" \ -F "max_tokens=32" \ -F "language=zh" \ -F "stream=true"
Uploading a file:
```bash
curl http://${host_ip}:888... | ```bash curl http://${host_ip}:8888/v1/docsum \ -H "Content-Type: multipart/form-data" \ -F "type=text" \ -F "messages=2024年9月26日，北京——今日，英特尔正式发布英特尔® 至强® 6性能核处理器（代号Granite Rapids），为AI、数据分析、科学计算等计算密集型业务提供卓越性能。" \ -F "max_tokens=32" \ -F "language=zh" \ -F "stream=true"
Uploading a file:
```bash
curl http://${host_ip}:888... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
fd013d20-0ca8-4f3d-9851-7ffd1a5b1428 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/gaudi.md | unknown | 233d533c-6ba5-4a83-b4fa-9f53d27ecae1 | 21 | opea-semantic-v1 | b1bb32f158e486cc | Try the command below to check whether the LLM service is ready. It uses the name of the image to check the status.
::::{tab-set}
:::{tab-item} vllm | ai_ref_knowledge | OPEA Documentation | Try the command below to check whether the LLM service is ready. It uses the name of the image to check the status.
::::{tab-set}
:::{tab-item} vllm | Try the command below to check whether the LLM service is ready. It uses the name of the image to check the status.
::::{tab-set}
:::{tab-item} vllm | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
0dd1f618-0388-40d8-b640-9962fe934a3a | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/xeon.md | unknown | 29f14fc0-57b6-4af3-929a-f858c9986b2c | 6 | opea-semantic-v1 | 08d75d72a79ae120 | ::::{tab-set} :::{tab-item} vllm
```bash
# vLLM service
docker logs docsum-xeon-vllm-service 2>&1 | grep complete
# If the service is ready, you will get the response like below. INFO: Application startup complete. | ai_ref_knowledge | OPEA Documentation | ::::{tab-set} :::{tab-item} vllm
```bash
# vLLM service
docker logs docsum-xeon-vllm-service 2>&1 | grep complete
# If the service is ready, you will get the response like below. INFO: Application startup complete. | ::::{tab-set} :::{tab-item} vllm
```bash
# vLLM service
docker logs docsum-xeon-vllm-service 2>&1 | grep complete
# If the service is ready, you will get the response like below. INFO: Application startup complete. | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
142cc528-22c8-4faf-8141-a04c24663a7b | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/xeon.md | unknown | 29f14fc0-57b6-4af3-929a-f858c9986b2c | 4 | opea-semantic-v1 | 61009f45862e8cf3 | Run this command to see this info: ```bash docker ps -a
Sample output:
```bash
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
d02da5001212 opea/docsum-gradio-ui:latest "python docsum_ui_gr…" 2 minutes ago Up 19 seconds 0.0.0.0:5173->5173/tcp, [::]:5173->5173/tcp docsum-xeon-ui-server
43de0d8ee9dd opea/docsum:lat... | ai_ref_knowledge | OPEA Documentation | Run this command to see this info: ```bash docker ps -a
Sample output:
```bash
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
d02da5001212 opea/docsum-gradio-ui:latest "python docsum_ui_gr…" 2 minutes ago Up 19 seconds 0.0.0.0:5173->5173/tcp, [::]:5173->5173/tcp docsum-xeon-ui-server
43de0d8ee9dd opea/docsum:lat... | Run this command to see this info: ```bash docker ps -a
Sample output:
```bash
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
d02da5001212 opea/docsum-gradio-ui:latest "python docsum_ui_gr…" 2 minutes ago Up 19 seconds 0.0.0.0:5173->5173/tcp, [::]:5173->5173/tcp docsum-xeon-ui-server
43de0d8ee9dd opea/docsum:lat... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
159f54c4-82d3-47f9-ae56-bee35fc17886 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/xeon.md | unknown | 29f14fc0-57b6-4af3-929a-f858c9986b2c | 7 | opea-semantic-v1 | 20f54d7a817a00f6 | service docker logs docsum-xeon-vllm-service 2>&1 | grep complete # If the service is ready, you will get the response like below. INFO: Application startup complete.
:::
:::{tab-item} TGI | ai_ref_knowledge | OPEA Documentation | service docker logs docsum-xeon-vllm-service 2>&1 | grep complete # If the service is ready, you will get the response like below. INFO: Application startup complete.
:::
:::{tab-item} TGI | service docker logs docsum-xeon-vllm-service 2>&1 | grep complete # If the service is ready, you will get the response like below. INFO: Application startup complete.
:::
:::{tab-item} TGI | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
1758025b-08d5-4af4-8690-17e808ca1423 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/xeon.md | unknown | 29f14fc0-57b6-4af3-929a-f858c9986b2c | 9 | opea-semantic-v1 | 397d96b4b0e695f8 | service docker logs docsum-xeon-tgi-server | grep Connected # If the service is ready, you will get the response like below. 2024-09-03T02:47:53.402023Z INFO text_generation_router::server: router/src/server.rs:2311: Connected
:::
:::: | ai_ref_knowledge | OPEA Documentation | service docker logs docsum-xeon-tgi-server | grep Connected # If the service is ready, you will get the response like below. 2024-09-03T02:47:53.402023Z INFO text_generation_router::server: router/src/server.rs:2311: Connected
:::
:::: | service docker logs docsum-xeon-tgi-server | grep Connected # If the service is ready, you will get the response like below. 2024-09-03T02:47:53.402023Z INFO text_generation_router::server: router/src/server.rs:2311: Connected
:::
:::: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
2629ae07-018e-4ef8-a184-0cb22591b9b6 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/xeon.md | unknown | 29f14fc0-57b6-4af3-929a-f858c9986b2c | 8 | opea-semantic-v1 | af079c56fcfe14a8 | ::: :::{tab-item} TGI
```bash
# TGI service
docker logs docsum-xeon-tgi-server | grep Connected
# If the service is ready, you will get the response like below. 2024-09-03T02:47:53.402023Z INFO text_generation_router::server: router/src/server.rs:2311: Connected | ai_ref_knowledge | OPEA Documentation | ::: :::{tab-item} TGI
```bash
# TGI service
docker logs docsum-xeon-tgi-server | grep Connected
# If the service is ready, you will get the response like below. 2024-09-03T02:47:53.402023Z INFO text_generation_router::server: router/src/server.rs:2311: Connected | ::: :::{tab-item} TGI
```bash
# TGI service
docker logs docsum-xeon-tgi-server | grep Connected
# If the service is ready, you will get the response like below. 2024-09-03T02:47:53.402023Z INFO text_generation_router::server: router/src/server.rs:2311: Connected | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
2d11fc1d-ef89-4ef1-9967-bf3845ab5b12 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/xeon.md | unknown | 29f14fc0-57b6-4af3-929a-f858c9986b2c | 0 | opea-semantic-v1 | cc522721ed11582a | # Single node on-prem deployment on Intel® Xeon® Scalable processor
This section covers the single-node on-prem deployment of the DocSum example. It will show how to build a document summarization service using the `Intel/neural-chat-7b-v3-3` model deployed on Intel® Xeon® Scalable processors. To quickly learn about OP... | ai_ref_knowledge | OPEA Documentation | # Single node on-prem deployment on Intel® Xeon® Scalable processor
This section covers the single-node on-prem deployment of the DocSum example. It will show how to build a document summarization service using the `Intel/neural-chat-7b-v3-3` model deployed on Intel® Xeon® Scalable processors. To quickly learn about OP... | # Single node on-prem deployment on Intel® Xeon® Scalable processor
This section covers the single-node on-prem deployment of the DocSum example. It will show how to build a document summarization service using the `Intel/neural-chat-7b-v3-3` model deployed on Intel® Xeon® Scalable processors. To quickly learn about OP... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
842362b9-1f38-4632-8eb0-bc38b6ee74c5 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/xeon.md | unknown | 29f14fc0-57b6-4af3-929a-f858c9986b2c | 1 | opea-semantic-v1 | 569ff08c686227e5 | 1. ASR 2. LLM with vLLM or TGI
This solution is designed to demonstrate the use of the `Intel/neural-chat-7b-v3-3` model on the Intel® Xeon® Scalable processors to take a document (.txt,.doc,.pdf), audio, or video file as the input and generate a summary. The steps will involve setting up Docker containers, uploading d... | ai_ref_knowledge | OPEA Documentation | 1. ASR 2. LLM with vLLM or TGI
This solution is designed to demonstrate the use of the `Intel/neural-chat-7b-v3-3` model on the Intel® Xeon® Scalable processors to take a document (.txt,.doc,.pdf), audio, or video file as the input and generate a summary. The steps will involve setting up Docker containers, uploading d... | 1. ASR 2. LLM with vLLM or TGI
This solution is designed to demonstrate the use of the `Intel/neural-chat-7b-v3-3` model on the Intel® Xeon® Scalable processors to take a document (.txt,.doc,.pdf), audio, or video file as the input and generate a summary. The steps will involve setting up Docker containers, uploading d... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
941409ea-8668-488d-8078-f1cdfc7bba15 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/xeon.md | unknown | 29f14fc0-57b6-4af3-929a-f858c9986b2c | 10 | opea-semantic-v1 | cf26f795b8f2c5e0 | Navigate to the `docker compose` directory for this hardware platform. ```bash cd $WORKSPACE/GenAIExamples/DocSum/docker_compose/intel/cpu/xeon
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/DocSum/docker_compose/intel/cpu/xeon
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/DocSum/docker_compose/intel/cpu/xeon
To stop and remove all the containers, use the command below: | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation | |
95d07d14-4437-4ff8-9864-712eb28e4002 | OPEA Documentation | file://datasets/opea-docs/tutorial/DocSum/deploy/xeon.md | unknown | 29f14fc0-57b6-4af3-929a-f858c9986b2c | 5 | opea-semantic-v1 | 14a68370ee829f34 | whisper_serv…" 3 minutes ago Up 2 minutes 0.0.0.0:7066->7066/tcp, [::]:7066->7066/tcp docsum-xeon-whisper-server 951abf0ebb5a opea/vllm:latest "python3 -m vllm.ent…" 3 minutes ago Up 2 minutes (healthy) 0.0.0.0:8008->80/tcp, [::]:8008->80/tcp docsum-xeon-vllm-service
Each docker container's log can also be checked usin... | ai_ref_knowledge | OPEA Documentation | whisper_serv…" 3 minutes ago Up 2 minutes 0.0.0.0:7066->7066/tcp, [::]:7066->7066/tcp docsum-xeon-whisper-server 951abf0ebb5a opea/vllm:latest "python3 -m vllm.ent…" 3 minutes ago Up 2 minutes (healthy) 0.0.0.0:8008->80/tcp, [::]:8008->80/tcp docsum-xeon-vllm-service
Each docker container's log can also be checked usin... | whisper_serv…" 3 minutes ago Up 2 minutes 0.0.0.0:7066->7066/tcp, [::]:7066->7066/tcp docsum-xeon-whisper-server 951abf0ebb5a opea/vllm:latest "python3 -m vllm.ent…" 3 minutes ago Up 2 minutes (healthy) 0.0.0.0:8008->80/tcp, [::]:8008->80/tcp docsum-xeon-vllm-service
Each docker container's log can also be checked usin... | opea, enterprise-ai, genai, docs, P1 | OPEA Documentation |
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