Dataprep Microservice for Multimodal Data with Redis

July 25, 2025 · View on GitHub

This dataprep microservice accepts the following from the user and ingests them into a Redis vector store:

Videos (mp4 files) and their transcripts (optional)
Images (gif, jpg, jpeg, and png files) and their captions (optional)
Audio (wav files)
PDFs (with text and images)

🚀1. Start Microservice with Docker
🚀2. Status Microservice
🚀3. Consume Microservice

🚀1. Start Microservice with Docker

1.2 Start LVM Microservice (Optional)

This is required only if you are going to consume the generate_captions API of this microservice as described here.

Please refer to this readme to start the LVM microservice. After LVM is up, set up environment variables.

export your_ip=$(hostname -I | awk '{print \$1}')
export LVM_ENDPOINT="http://${your_ip}:9399/v1/lvm"

1.3 Setup Environment Variables

export your_ip=$(hostname -I | awk '{print \$1}')
export EMBEDDING_MODEL_ID="BridgeTower/bridgetower-large-itm-mlm-itc"
export REDIS_URL="redis://${your_ip}:6379"
export WHISPER_MODEL="base"
export INDEX_NAME=${your_redis_index_name}
export HF_TOKEN=${your_hf_api_token}

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-multimodal-redis" -p 6007:5000 --runtime=runc --ipc=host -e no_proxy=$no_proxy -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e REDIS_HOST=$your_ip -e REDIS_URL=$REDIS_URL -e INDEX_NAME=$INDEX_NAME -e LVM_ENDPOINT=$LVM_ENDPOINT -e HF_TOKEN=$HF_TOKEN -e MULTIMODAL_DATAPREP=true -e DATAPREP_COMPONENT_NAME="OPEA_DATAPREP_MULTIMODALREDIS" opea/dataprep-multimodal-redis:latest

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

cd comps/dataprep/multimodal/redis/langchain
docker compose -f compose_redis_multimodal.yaml up -d

🚀2. Status Microservice

docker container logs -f dataprep-multimodal-redis

🚀3. Consume Microservice

Once this dataprep microservice is started, user can use the below commands to invoke the microservice to convert images, videos, text, and PDF files to embeddings and save to the Redis vector store.

This microservice provides 3 different ways for users to ingest files into Redis vector store corresponding to the 3 use cases.

Use case: This API is used for videos accompanied by transcript files (.vtt format), images accompanied by text caption files (.txt format), images accompanied by a spoken audio caption (.wav or .mp3), and PDF files containing a mix of text and images.

Important notes:

Make sure the file paths after files=@ are correct.
Every transcript or caption file's name must be identical to its corresponding video or image file's name (except their extension - .vtt goes with .mp4 and .txt goes with .jpg, .jpeg, .png, or .gif). For example, video1.mp4 and video1.vtt. Otherwise, if video1.vtt is not included correctly in the API call, the microservice will return an error No captions file video1.vtt found for video1.mp4.
It is assumed that PDFs will contain at least one image. Each image in the file will be embedded along with the text that appears on the same page as the image.

Single video-transcript pair upload

curl -X POST \
    -H "Content-Type: multipart/form-data" \
    -F "files=@./video1.mp4" \
    -F "files=@./video1.vtt" \
    http://localhost:6007/v1/dataprep/ingest

Single image-caption pair upload

Text caption file:

curl -X POST \
    -H "Content-Type: multipart/form-data" \
    -F "files=@./image.jpg" \
    -F "files=@./image.txt" \
    http://localhost:6007/v1/dataprep/ingest

Spoken audio caption file:

Note: When an audio caption file is provided, the speech is translated to text using the whisper model. The translated text is then embedded with the image, and ingested into the vector store.

curl -X POST \
    -H "Content-Type: multipart/form-data" \
    -F "files=@./image.jpg" \
    -F "files=@./image.wav" \
    http://localhost:6007/v1/dataprep/ingest

Multiple file pair upload

curl -X POST \
    -H "Content-Type: multipart/form-data" \
    -F "files=@./video1.mp4" \
    -F "files=@./video1.vtt" \
    -F "files=@./video2.mp4" \
    -F "files=@./video2.vtt" \
    -F "files=@./image1.png" \
    -F "files=@./image1.txt" \
    -F "files=@./image2.jpg" \
    -F "files=@./image2.txt" \
    -F "files=@./example.pdf" \
    http://localhost:6007/v1/dataprep/ingest

3.2 Consume generate_transcripts API

Use case: This API should be used when a video has meaningful audio or recognizable speech but its transcript file is not available, or for audio files with speech.

In this use case, this microservice will use whisper model to generate the .vtt transcript for the video or audio files.

Single file upload

curl -X POST \
    -H "Content-Type: multipart/form-data" \
    -F "files=@./video1.mp4" \
    http://localhost:6007/v1/dataprep/generate_transcripts

Multiple file upload

curl -X POST \
    -H "Content-Type: multipart/form-data" \
    -F "files=@./video1.mp4" \
    -F "files=@./video2.mp4" \
    -F "files=@./audio1.wav" \
    http://localhost:6007/v1/dataprep/generate_transcripts

3.3 Consume generate_captions API

Use case: This API should be used when uploading an image, or when uploading a video that does not have meaningful audio or does not have audio.

In this use case, there is no meaningful language transcription. Thus, it is preferred to leverage a LVM microservice to summarize the frames.

Single video upload

curl -X POST \
    -H "Content-Type: multipart/form-data" \
    -F "files=@./video1.mp4" \
    http://localhost:6007/v1/dataprep/generate_captions

Multiple video upload

curl -X POST \
    -H "Content-Type: multipart/form-data" \
    -F "files=@./video1.mp4" \
    -F "files=@./video2.mp4" \
    http://localhost:6007/v1/dataprep/generate_captions

Single image upload

curl -X POST \
    -H "Content-Type: multipart/form-data" \
    -F "files=@./image.jpg" \
    http://localhost:6007/v1/dataprep/generate_captions

3.4 Consume get API

To get names of uploaded files, use the following command.

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

3.5 Consume delete API

To delete uploaded files and clear the database, use the following command.

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