Dataprep Microservice for Multimodal Data with Redis¶

This dataprep microservice accepts videos (mp4 files) and their transcripts (optional) from the user and ingests them into Redis vectorstore.

🚀1. Start Microservice with Python（Option 1）¶

1.1 Install Requirements¶

# Install ffmpeg static build
wget https://johnvansickle.com/ffmpeg/builds/ffmpeg-git-amd64-static.tar.xz
mkdir ffmpeg-git-amd64-static
tar -xvf ffmpeg-git-amd64-static.tar.xz -C ffmpeg-git-amd64-static --strip-components 1
export PATH=$(pwd)/ffmpeg-git-amd64-static:$PATH
cp $(pwd)/ffmpeg-git-amd64-static/ffmpeg /usr/local/bin/

pip install -r requirements.txt

1.2 Start Redis Stack Server¶

Please refer to this readme.

1.3 Setup Environment Variables¶

export your_ip=$(hostname -I | awk '{print $1}')
export REDIS_URL="redis://${your_ip}:6379"
export INDEX_NAME=${your_redis_index_name}
export PYTHONPATH=${path_to_comps}

1.4 Start LVM Microservice (Optional)¶

This is required only if you are going to consume the generate_captions API of this microservice as in Section 4.3.

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"

1.5 Start Data Preparation Microservice for Redis with Python Script¶

Start document preparation microservice for Redis with below command.

python prepare_videodoc_redis.py

🚀2. Start Microservice with Docker (Option 2)¶

2.1 Start Redis Stack Server¶

Please refer to this readme.

2.2 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"

2.3 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=${your_redis_index_name}
export HUGGINGFACEHUB_API_TOKEN=${your_hf_api_token}

2.4 Build Docker Image¶

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

2.5 Run Docker with CLI (Option A)¶

docker run -d --name="dataprep-multimodal-redis" -p 6007:6007 --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 LVM_ENDPOINT=$LVM_ENDPOINT -e HUGGINGFACEHUB_API_TOKEN=$HUGGINGFACEHUB_API_TOKEN opea/dataprep-multimodal-redis:latest

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

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

🚀3. Status Microservice¶

docker container logs -f dataprep-multimodal-redis

🚀4. Consume Microservice¶

Once this dataprep microservice is started, user can use the below commands to invoke the microservice to convert videos and their transcripts (optional) to embeddings and save to the Redis vector store.

This mircroservice has provided 3 different ways for users to ingest videos into Redis vector store corresponding to the 3 use cases.

4.1 Consume videos_with_transcripts API¶

Use case: This API is used when a transcript file (under .vtt format) is available for each video.

Important notes:

Make sure the file paths after files=@ are correct.
Every transcript file’s name must be identical with its corresponding video file’s name (except their extension .vtt and .mp4). For example, video1.mp4 and video1.vtt. Otherwise, if video1.vtt is not included correctly in this API call, this microservice will return error No captions file video1.vtt found for video1.mp4.

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/videos_with_transcripts

Multiple video-transcript 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" \
    http://localhost:6007/v1/videos_with_transcripts

4.2 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.

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

Single video upload¶

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

Multiple video upload¶

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

4.3 Consume generate_captions API¶

Use case: This API should be used when a video does not have meaningful audio or does not have audio.

In this use case, transcript either does not provide any meaningful information or does not exist. Thus, it is preferred to leverage a LVM microservice to summarize the video frames.

Single video upload

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

4.4 Consume get_videos API¶

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

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

4.5 Consume delete_videos API¶

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

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