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README_pgvector.md

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Dataprep Microservice with PGVector

🚀1. Start Microservice with Python（Option 1）

1.1 Install Requirements

pip install -r requirements.txt

1.2 Setup Environment Variables

export PG_CONNECTION_STRING=postgresql+psycopg2://testuser:testpwd@${your_ip}:5432/vectordb
export INDEX_NAME=${your_index_name}

1.3 Start PGVector

Please refer to this readme.

1.4 Start Document Preparation Microservice for PGVector with Python Script

Start document preparation microservice for PGVector with below command.

python prepare_doc_pgvector.py

🚀2. Start Microservice with Docker (Option 2)

2.1 Start PGVector

Please refer to this readme.

2.2 Setup Environment Variables

export PG_CONNECTION_STRING=postgresql+psycopg2://testuser:testpwd@${your_ip}:5432/vectordb
export INDEX_NAME=${your_index_name}
export TEI_EMBEDDING_ENDPOINT=${your_tei_embedding_endpoint}
export HUGGINGFACEHUB_API_TOKEN=${your_hf_api_token}

2.3 Build Docker Image

cd GenAIComps
docker build -t opea/dataprep:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/src/Dockerfile .

2.4 Run Docker with CLI (Option A)

docker run  --name="dataprep-pgvector" -p 6007:6007 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e PG_CONNECTION_STRING=$PG_CONNECTION_STRING  -e INDEX_NAME=$INDEX_NAME -e EMBED_MODEL=${EMBED_MODEL} -e TEI_EMBEDDING_ENDPOINT=$TEI_EMBEDDING_ENDPOINT -e HUGGINGFACEHUB_API_TOKEN=${HUGGINGFACEHUB_API_TOKEN} -e DATAPREP_COMPONENT_NAME="OPEA_DATAPREP_PGVECTOR" opea/dataprep:latest

2.5 Run with Docker Compose (Option B)

cd comps/dataprep/deployment/docker_compose
docker compose -f compose_pgvector.yaml up -d

🚀3. Consume Microservice

3.1 Consume Upload API

Once document preparation microservice for PGVector is started, user can use below command to invoke the microservice to convert the document to embedding and save to the database.

curl -X POST \
    -H "Content-Type: application/json" \
    -d '{"path":"/path/to/document"}' \
    http://localhost:6007/v1/dataprep/ingest

3.2 Consume get API

To get uploaded file structures, use the following command:

curl -X POST \
    -H "Content-Type: application/json" \
    http://localhost:6007/v1/dataprep/get

Then you will get the response JSON like this:

[
  {
    "name": "uploaded_file_1.txt",
    "id": "uploaded_file_1.txt",
    "type": "File",
    "parent": ""
  },
  {
    "name": "uploaded_file_2.txt",
    "id": "uploaded_file_2.txt",
    "type": "File",
    "parent": ""
  }
]

4.3 Consume delete API

To delete uploaded file/link, use the following command.

The file_path here should be the id get from /v1/dataprep/get API.

# delete link
curl -X POST \
    -H "Content-Type: application/json" \
    -d '{"file_path": "https://www.ces.tech/.txt"}' \
    http://localhost:6007/v1/dataprep/delete

# delete file
curl -X POST \
    -H "Content-Type: application/json" \
    -d '{"file_path": "uploaded_file_1.txt"}' \
    http://localhost:6007/v1/dataprep/delete

# delete all files and links
curl -X POST \
    -H "Content-Type: application/json" \
    -d '{"file_path": "all"}' \
    http://localhost:6007/v1/dataprep/delete