Passing a new query in retrieval QA chain its building the entire flow. about langflow HOT 1 OPEN

To address the issue of your RAG pipeline rebuilding the entire flow with each new query, consider implementing a caching mechanism for the HuggingFaceEmbeddingsComponent. This involves specifying a cache_folder in its configuration, allowing the component to store and reuse embeddings for subsequent queries, thus avoiding the need to recompute them. Here's a concise example on how to set this up:

from langchain_community.embeddings.huggingface import HuggingFaceEmbeddings
from langflow.interface.custom.custom_component import CustomComponent

hugging_face_embeddings_component = HuggingFaceEmbeddingsComponent().build(
    cache_folder='/path/to/your/cache/folder',  # Path to cache folder
    encode_kwargs={},  # Optional: Additional encoding kwargs
    model_kwargs={},  # Optional: Additional model kwargs
    model_name='sentence-transformers/all-mpnet-base-v2',  # Model name
    multi_process=True  # Optional: Enable multi-processing
)

By leveraging the cache_folder, the component will check for existing embeddings before processing new queries, significantly reducing the computational load and speeding up the response time for your pipeline. This approach optimizes the use of document loaders, text splitters, and embeddings, ensuring efficient reuse across queries.

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