RAG With GPU

[WithFoxSquirrel]

Checked other resources

• I added a very descriptive title to this question.
• I searched the LangChain documentation with the integrated search.
• I used the GitHub search to find a similar question and didn't find it.

Commit to Help

• I commit to help with one of those options 👆

Example Code

embedding_function = OpenCLIPEmbeddings(device='cuda')
# Create chroma
vectorstore = Chroma(
    collection_name="mm_rag_clip_museum_nvnim", embedding_function=embedding_function, persist_directory="./data/nvnim/"
)

Description

How can i use gpu to accerate the RAG pipeline, for example openclip embeding and so on.
I found if OpenCLIPEmbeddings can't set device , and also you need load data to gpu

System Info

langchain 0.1.9
langchain-chroma 0.1.1
langchain-community 0.0.24
langchain-core 0.1.27
langchain-experimental 0.0.52
langchain-google-genai 0.0.9
langchain-openai 0.0.7
langchain-text-splitters 0.2.1
langchainhub 0.1.2

[dosubot[bot]]

To use GPU acceleration with the OpenCLIPEmbeddings class in LangChain, you need to ensure that the device parameter is set correctly and that the necessary libraries are installed. Here’s how you can do it:

1. Install the required packages:

   pip install open_clip_torch torch

2. Set the device to 'cuda' when initializing the OpenCLIPEmbeddings class:

   from langchain_experimental.open_clip import OpenCLIPEmbeddings
   
   embedding_function = OpenCLIPEmbeddings(
       model_name="ViT-H-14",
       checkpoint="laion2b_s32b_b79k",
       load_fn_kwargs={"device": "cuda"}
   )

3. Create the Chroma vector store with the GPU-accelerated embedding function:

   from langchain.vectorstores import Chroma
   
   vectorstore = Chroma(
       collection_name="mm_rag_clip_museum_nvnim",
       embedding_function=embedding_function,
       persist_directory="./data/nvnim/"
   )

By following these steps, you can ensure that the OpenCLIPEmbeddings class uses GPU acceleration effectively ^[1][2].

To continue talking to Dosu, mention @dosu.