Dataprep Microservice with Redis¶

We have provided dataprep microservice for multimodal data input (e.g., text and image) here.

🚀1. Start Microservice with Docker¶

1.1 Start Redis Stack Server¶

Please refer to this readme.

1.2 Setup Environment Variables¶

export REDIS_URL="redis://${your_ip}:6379"
export INDEX_NAME=${your_index_name}
export HUGGINGFACEHUB_API_TOKEN=${your_hf_api_token}

1.3 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 Build Docker Image¶

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

1.5 Run Docker with CLI (Option A)¶

docker run -d --name="dataprep-redis-server" -p 6007:5000 --runtime=runc --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e REDIS_URL=$REDIS_URL -e INDEX_NAME=$INDEX_NAME -e TEI_EMBEDDING_ENDPOINT=$TEI_EMBEDDING_ENDPOINT -e HUGGINGFACEHUB_API_TOKEN=$HUGGINGFACEHUB_API_TOKEN opea/dataprep:latest

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

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

🚀2. Status Microservice¶

docker container logs -f dataprep-redis-server

🚀3. Consume Microservice¶

3.1 Consume Upload API¶

Once document preparation microservice for Redis 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)

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": ""
  }
]

3.3 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