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README_opensearch.md

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Dataprep Microservice with OpenSearch

For dataprep microservice for text input, we provide here the Langchain framework.

🚀1. Start Microservice with Python（Option 1）

1.1 Install Requirements

  • option 1: Install Single-process version (for processing up to 10 files)
apt update
apt install default-jre tesseract-ocr libtesseract-dev poppler-utils -y
# for langchain
cd langchain
pip install -r requirements.txt

1.2 Start OpenSearch Stack Server

Please refer to this readme.

1.3 Setup Environment Variables

export your_ip=$(hostname -I | awk '{print $1}')
export OPENSEARCH_URL="http://${your_ip}:9200"
export INDEX_NAME=${your_index_name}
export PYTHONPATH=${path_to_comps}

1.4 Start Embedding Service

First, you need to start a TEI service.

your_port=6006
model="BAAI/bge-base-en-v1.5"
docker run -p $your_port:80 -v ./data:/data --name tei_server -e http_proxy=$http_proxy -e https_proxy=$https_proxy --pull always ghcr.io/huggingface/text-embeddings-inference:cpu-1.5 --model-id $model

Then you need to test your TEI service using the following commands:

curl localhost:$your_port/embed \
    -X POST \
    -d '{"inputs":"What is Deep Learning?"}' \
    -H 'Content-Type: application/json'

After checking that it works, set up environment variables.

export TEI_ENDPOINT="http://localhost:$your_port"

1.4 Start Document Preparation Microservice for OpenSearch with Python Script

Start document preparation microservice for OpenSearch with below command.

  • option 1: Start single-process version (for processing up to 10 files)
cd langchain
python prepare_doc_opensearch.py

🚀2. Start Microservice with Docker (Option 2)

2.1 Start OpenSearch Stack Server

Please refer to this readme.

2.2 Setup Environment Variables

export EMBEDDING_MODEL_ID="BAAI/bge-base-en-v1.5"
export TEI_ENDPOINT="http://${your_ip}:6006"
export OPENSEARCH_URL="http://${your_ip}:9200"
export INDEX_NAME=${your_index_name}
export HUGGINGFACEHUB_API_TOKEN=${your_hf_api_token}

2.3 Build Docker Image

  • Build docker image with langchain

  • option 1: Start single-process version (for processing up to 10 files)

cd ../../
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)

  • option 1: Start single-process version (for processing up to 10 files)
docker run -d --name="dataprep-opensearch-server" -p 6007:6007 --runtime=runc --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e OPENSEARCH_URL=$OPENSEARCH_URL -e INDEX_NAME=$INDEX_NAME -e TEI_ENDPOINT=$TEI_ENDPOINT -e HUGGINGFACEHUB_API_TOKEN=$HUGGINGFACEHUB_API_TOKEN -e DATAPREP_COMPONENT_NAME="OPEA_DATAPREP_OPENSEARCH" opea/dataprep:latest

2.5 Run with Docker Compose (Option B - deprecated, will move to genAIExample in future)

# for langchain
cd comps/dataprep/deployment/docker_compose
# common command
docker compose -f compose_opensearch.yaml up -d

🚀3. Status Microservice

docker container logs -f dataprep-opensearch-server

🚀4. Consume Microservice

4.1 Consume Upload API

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

Make sure the file path after files=@ is correct.

  • Single file upload
curl -X POST \
    -H "Content-Type: multipart/form-data" \
    -F "files=@./file1.txt" \
    http://localhost:6007/v1/dataprep/ingest

You can specify chunk_size and chunk_size by the following commands.

curl -X POST \
    -H "Content-Type: multipart/form-data" \
    -F "files=@./file1.txt" \
    -F "chunk_size=1500" \
    -F "chunk_overlap=100" \
    http://localhost:6007/v1/dataprep/ingest

We support table extraction from pdf documents. You can specify process_table and table_strategy by the following commands. "table_strategy" refers to the strategies to understand tables for table retrieval. As the setting progresses from "fast" to "hq" to "llm," the focus shifts towards deeper table understanding at the expense of processing speed. The default strategy is "fast".

Note: If you specify "table_strategy=llm", You should first start TGI Service, please refer to 1.2.1, 1.3.1 in https://github.com/opea-project/GenAIComps/tree/main/comps/llms/README.md, and then export TGI_LLM_ENDPOINT="http://${your_ip}:8008".

curl -X POST \
    -H "Content-Type: multipart/form-data" \
    -F "files=@./your_file.pdf" \
    -F "process_table=true" \
    -F "table_strategy=hq" \
    http://localhost:6007/v1/dataprep/ingest
  • Multiple file upload
curl -X POST \
    -H "Content-Type: multipart/form-data" \
    -F "files=@./file1.txt" \
    -F "files=@./file2.txt" \
    -F "files=@./file3.txt" \
    http://localhost:6007/v1/dataprep/ingest
  • Links upload (not supported for llama_index now)
curl -X POST \
    -F 'link_list=["https://www.ces.tech/"]' \
    http://localhost:6007/v1/dataprep/ingest

or

import requests
import json

proxies = {"http": ""}
url = "http://localhost:6007/v1/dataprep/ingest"
urls = [
    "https://towardsdatascience.com/no-gpu-no-party-fine-tune-bert-for-sentiment-analysis-with-vertex-ai-custom-jobs-d8fc410e908b?source=rss----7f60cf5620c9---4"
]
payload = {"link_list": json.dumps(urls)}

try:
    resp = requests.post(url=url, data=payload, proxies=proxies)
    print(resp.text)
    resp.raise_for_status()  # Raise an exception for unsuccessful HTTP status codes
    print("Request successful!")
except requests.exceptions.RequestException as e:
    print("An error occurred:", e)

4.2 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

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 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