[Python] How to Optimize Hybrid Retriever Performance in LangChain for Low Latency and Token...

I’m developing a Retrieval-Augmented Generation (RAG) application using LangChain in Python, and I'm facing performance challenges with my current hybrid retrieval setup.

Current Setup: I'm using an ensemble retriever that combines a TF-IDF retriever and a dense retriever to balance keyword matching and semantic similarity. Here’s a simplified version of my setup:

from langchain.retrievers import EnsembleRetriever, create_tfidf_retriever
from langchain.vectorstores import VectorStore

# Initialize TF-IDF retriever with k=5
tfidf_retriever = create_tfidf_retriever(documents=documents)
tfidf_retriever.k = 5

# Initialize dense retriever with vector store
dense_retriever = vectorstore.as_retriever(search_kwargs={"k": 5})

# Ensemble retriever with balanced weights
ensemble_retriever = EnsembleRetriever(
retrievers=[dense_retriever, tfidf_retriever],
weights=[0.5, 0.5]
)


Problem: While this setup retrieves relevant results, the retrieval latency is high, and it often returns more tokens than necessary, impacting both performance and cost. I experimented with MultiQueryRetriever for better handling of rephrased questions, but it significantly increased retrieval time.

Goal: I’d like to reduce retrieval latency and limit the token count in the retrieved documents without sacrificing relevance.

What I’ve Tried:

• 
  Adjusted k values for each retriever to control retrieval count.
  
  
• 
  Experimented with weights to balance TF-IDF and dense retrieval outputs.
  
  
• 
  Tested MultiQueryRetriever, which improved relevance but increased latency.
  

Question: Is there an efficient way to configure this LangChain setup to reduce retrieval latency and limit token count? Are there adjustments, such as specific k values or alternative retriever configurations, that can help optimize performance in this scenario?

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