nvidia/nv-embedqa-mistral-7b-v2

RUN ANYWHERE

Multilingual text question-answering retrieval, transforming textual information into dense vector representations.

Embedding
Retrieval Augmented Generation
nemo retriever
API Reference

By running the below commands, you accept the NVIDIA AI Enterprise Terms of Use and the NVIDIA Community Models License.

Pull and run nvidia/nv-embedqa-mistral-7b-v2 using Docker (this will download the full model and run it in your local environment)

$ docker login nvcr.io
Username: $oauthtoken
Password: <PASTE_API_KEY_HERE>

Pull and run the NVIDIA NIM with the command below. This will download the optimized model for your infrastructure.

export NGC_API_KEY=<PASTE_API_KEY_HERE>
export LOCAL_NIM_CACHE=~/.cache/nim
mkdir -p "$LOCAL_NIM_CACHE"
docker run -it --rm \
    --gpus all \
    --shm-size=16GB \
    -e NGC_API_KEY \
    -v "$LOCAL_NIM_CACHE:/opt/nim/.cache" \
    -u $(id -u) \
    -p 8000:8000 \
    nvcr.io/nim/nvidia/nv-embedqa-mistral-7b-v2:1.0.1

You can now make a local API call using this curl command:

curl -X "POST" \
  "http://localhost:8000/v1/embeddings" \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
    "input": ["Hello world"],
    "model": "nvidia/nv-embedqa-mistral-7b-v2",
    "input_type": "query"
}'

For more details on getting started with this NIM, visit the NVIDIA NIM Docs.