Skip to content

Latest commit

 

History

History
135 lines (99 loc) · 3.44 KB

README.md

File metadata and controls

135 lines (99 loc) · 3.44 KB
Raw

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}
export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=${your_langchain_api_key}
export LANGCHAIN_PROJECT="opea/gen-ai-comps:dataprep"

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 LANGCHAIN_TRACING_V2=true
export LANGCHAIN_API_KEY=${your_langchain_api_key}
export LANGCHAIN_PROJECT="opea/dataprep"

2.3 Build Docker Image

cd GenAIComps
docker build -t opea/dataprep-pgvector:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/dataprep/pgvector/langchain/docker/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 TEI_ENDPOINT=$TEI_ENDPOINT opea/dataprep-pgvector:latest

2.5 Run with Docker Compose (Option B)

cd comps/dataprep/langchain/pgvector/docker
docker compose -f docker-compose-dataprep-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

3.2 Consume get_file API

To get uploaded file structures, use the following command:

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

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_file API

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

The file_path here should be the id get from /v1/dataprep/get_file 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_file

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

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