AI Assistant Configuration

Vector Library Service

Please contact the after-sales team to obtain the deployment script.

Script Deployment

Start Service

bash deploy.sh -m start
# When you see the message "start success", it indicates the service has started successfully.

Stop Service

bash deploy.sh -m stop

Stop Service (Clean Up Containers)

bash deploy.sh -m down

View Running Status

bash deploy.sh -m status

k8s Deployment

Deploy vector-serve and vector-postgres

kubectl -n [namespace] apply -f vectorize-pg.yaml -f vector-serve.yaml

Please Note

Before deployment, confirm the storage system used by the current cluster and modify the storage configuration in the vector-serve deployment file as needed. The default is longhorn.

Copy the model file (please contact support to obtain the download link)

kubectl -n [namespace] cp models--intfloat--multilingual-e5-base.tar.gz vector-serve-xxxx:/root/.cache/huggingface/hub/
kubectl -n [namespace] exec -t vector-serve-xxxx -- bash -c "cd /root/.cache/huggingface/hub && tar xf models--intfloat--multilingual-e5-base.tar.gz"

Modify HENGSHI Service Configuration (Requires Restarting HENGSHI Service)

Single-node Deployment
Edit the /opt/hengshi/conf/hengshi-sense-env.sh file
Uncomment the VECTOR_DB_URL and VECTOR_ENDPOINT variables.

export VECTOR_DB_URL="jdbc:postgresql://<SERVER_IP>:54321/postgres?user=postgres&password=postgres&useUnicode=true&characterEncoding=utf8"
export VECTOR_ENDPOINT="http://<SERVER_IP>:3000/v1/embeddings"

Docker Deployment
Navigate to the deployment directory docker-compose-x.x, and use vi/vim to open the .env file and add the following parameters.

# VECTOR_DB
VECTOR_DB_URL=jdbc:postgresql://<SERVER_IP>:54321/postgres?user=postgres&password=postgres&useUnicode=true&characterEncoding=utf8
VECTOR_ENDPOINT=http://<SERVER_IP>:3000/v1/embeddings

K8s Deployment
Edit the configmap named "hengshi-sense" in the namespace where HENGSHI is deployed, and add the following parameters.

VECTOR_DB_URL: "jdbc:postgresql://<SERVER_IP>:54321/postgres?user=postgres&password=postgres&useUnicode=true&characterEncoding=utf8"
VECTOR_ENDPOINT: "http://<SERVER_IP>:3000/v1/embeddings"

Note:

Replace <SERVER_IP> with the IP address of the deployment machine, which can be obtained using the ifconfig command.

For K8s deployment, you can configure it as the address of a Service, such as vector-serve and vectorize-pg, or use the NodeIP+NodePort format for configuration.

Restart Operations

Standalone

# Please ensure the path is updated to the current deployment path of hengshi, default is /opt/hengshi
/opt/hengshi/bin/hengshi-sense-bin restart hengshi

Docker

# For Docker deployment, execute docker-compose down and then docker-compose up -d to apply the new configuration changes
cd docker-compose-x.x
docker-compose down
docker-compose up -d

K8s

# Replace [namespace] with the actual deployment namespace, and replicas with the actual number of pods deployed, default is 1
kubectl -n [namespace] scale deployment hengshi --replicas=0
kubectl -n [namespace] scale deployment hengshi --replicas=1

Common Errors

query vector db fail

This issue is usually caused by a communication failure between vector-serve and vector-postgres. You can use the following methods to confirm if everything is functioning properly:

• Confirm through the vector-serve interface request:

# Execute on the server where hengshi is located
# For docker/k8s deployment, enter the hengshi-sense container to execute
curl -X POST http://localhost:3000/v1/embeddings   -H 'Content-Type: application/json'   -d '{"input": [" abc"],"model": "intfloat/multilingual-e5-base"}'

# A normal response will return the following information:
{"data":[{"embedding":[-0.003002965124323964,0.03069210797548294,-0.002330577466636896,0.03642740473151207,0.019268367439508438,-0.007615369278937578,-0.03148505836725235,-0.012897394597530365,0.04027864709496498,0.039263855665922165,0.0038333579432219267,-0.02870958484709263,0.0900445431470871,-0.005433060694485903,-0.028027791529893875,-0.0381585918366909,0.013700217008590698,-0.03247552365064621,0.05149281769990921,0.006922998931258917,0.07466138899326324,-0.050848592072725296,0.037725191563367844,-0.025164397433400154,0.03877019137144089,-0.024159127846360207,0.023425891995429993,0.0228315070271492,-0.054315488785505295,0.05397752672433853,0.03425733000040054,-0.05478481948375702,0.03427012637257576"index":0}],"model":"intfloat/multilingual-e5-base"}

• Confirm through vector's query SQL:

# For docker deployment, enter the vector-postgres container
docker ps | grep "postgres-vectorize"
docker exec -it <container_id> bash

# Execute the psql command to enter the SQL console
psql (16.3 (Debian 16.3-1.pgdg120+1))
Type "help" for help.

postgres=# select vectorize.transform_embeddings(input => 'abc', model_name => 'intfloat/multilingual-e5-base')::TEXT;
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 {-0.003002965124323964,0.03069210797548294,-0.002330577466636896,0.03642740473151207,0.01926836743950844,-0.007615369278937578,-0.03148505836725235,-0.012897394597530363,0.04027864709496498,0.039263855665922165,0.0038333579432219263,-0.02870958484709263,0.0900445431470871,-0.005433060694485903,-0.028027791529893875,-0.0381585918366909,0.013700217008590698,-0.03247552365064621,0.05149281769990921,0.006922998931258917,0.07466138899326324,-0.050848592072725296,0.03772519156336784,-0.02516439743340015,0.03877019137144089,-0.024159127846360207,0.023425891995429993,0.0228315070271492,-0.054315488785505295,0.05397752672433853,0.03425733000040054,-0.05478481948375702,0.03427012637257576,0.018632836639881138,0.04853101447224617,0.00798027589917183,-0.00742304464802146,-0.03389187157154083,0.

# For k8s deployment, enter the vector-postgres container
kubectl -n [namespace] exec -it vectorize-pg-xxxx -- bash

# Execute the psql command to enter the SQL console
psql (16.3 (Debian 16.3-1.pgdg120+1))
Type "help" for help.

postgres=# select vectorize.transform_embeddings(input => 'abc', model_name => 'intfloat/multilingual-e5-base')::TEXT;
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 {-0.003002965124323964,0.03069210797548294,-0.002330577466636896,0.03642740473151207,0.01926836743950844,-0.007615369278937578,-0.03148505836725235,-0.012897394597530363,0.04027864709496498,0.039263855665922165,0.0038333579432219263,-0.02870958484709263,0.0900445431470871,-0.005433060694485903,-0.028027791529893875,-0.0381585918366909,0.013700217008590698,-0.03247552365064621,0.05149281769990921,0.006922998931258917,0.07466138899326324,-0.050848592072725296,0.03772519156336784,-0.02516439743340015,0.03877019137144089,-0.024159127846360207,0.023425891995429993,0.0228315070271492,-0.054315488785505295,0.05397752672433853,0.03425733000040054,-0.05478481948375702,0.03427012637257576,0.018632836639881138,0.04853101447224617,0.00798027589917183,-0.00742304464802146,-0.03389187157154083,0.