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Embeddings Microservice with Llama Index TEI

🚀1. Start Microservice with Python (Option 1)

Currently, we provide two ways to implement the embedding service:

  1. Build the embedding model locally from the server, which is faster, but takes up memory on the local server.

  2. Build it based on the TEI endpoint, which provides more flexibility, but may bring some network latency.

For both of the implementations, you need to install requirements first.

1.1 Install Requirements

pip install -r requirements.txt

1.2 Start Embedding Service

You can select one of following ways to start the embedding service:

Start Embedding Service with TEI

First, you need to start a TEI service.

your_port=8090
model="BAAI/bge-large-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'

Start the embedding service with the TEI_EMBEDDING_ENDPOINT.

export TEI_EMBEDDING_ENDPOINT="http://localhost:$yourport"
export TEI_EMBEDDING_MODEL_NAME="BAAI/bge-large-en-v1.5"
python embedding_tei.py

Start Embedding Service with Local Model

python local_embedding.py

🚀2. Start Microservice with Docker (Optional 2)

2.1 Start Embedding Service with TEI

First, you need to start a TEI service.

your_port=8090
model="BAAI/bge-large-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'

Export the TEI_EMBEDDING_ENDPOINT for later usage:

export TEI_EMBEDDING_ENDPOINT="http://localhost:$yourport"
export TEI_EMBEDDING_MODEL_NAME="BAAI/bge-large-en-v1.5"

2.2 Build Docker Image

cd ../../../../
docker build -t opea/embedding-tei-llama-index:latest --build-arg https_proxy=$https_proxy --build-arg http_proxy=$http_proxy -f comps/embeddings/tei/llama_index/Dockerfile .

2.3 Run Docker with CLI

docker run -d --name="embedding-tei-llama-index-server" -p 6000:6000 --ipc=host -e http_proxy=$http_proxy -e https_proxy=$https_proxy -e TEI_EMBEDDING_ENDPOINT=$TEI_EMBEDDING_ENDPOINT -e TEI_EMBEDDING_MODEL_NAME=$TEI_EMBEDDING_MODEL_NAME opea/embedding-tei-llama-index:latest

2.4 Run Docker with Docker Compose

cd docker
docker compose -f docker_compose_embedding.yaml up -d

🚀3. Consume Embedding Service

3.1 Check Service Status

curl http://localhost:6000/v1/health_check\
  -X GET \
  -H 'Content-Type: application/json'

3.2 Consume Embedding Service

Use our basic API.

## query with single text
curl http://localhost:6000/v1/embeddings\
  -X POST \
  -d '{"text":"Hello, world!"}' \
  -H 'Content-Type: application/json'

## query with multiple texts
curl http://localhost:6000/v1/embeddings\
  -X POST \
  -d '{"text":["Hello, world!","How are you?"]}' \
  -H 'Content-Type: application/json'

We are also compatible with OpenAI API.

## Input single text
curl http://localhost:6000/v1/embeddings\
  -X POST \
  -d '{"input":"Hello, world!"}' \
  -H 'Content-Type: application/json'

## Input multiple texts with parameters
curl http://localhost:6000/v1/embeddings\
  -X POST \
  -d '{"input":["Hello, world!","How are you?"], "dimensions":100}' \
  -H 'Content-Type: application/json'