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rag_chain/chain.py

@@ -66,6 +66,7 @@ def format_tall_tree_docs(docs: list[Document]) -> str:
     )
 
 
+@cache
 def create_langsmith_client():
     """Create a Langsmith client."""
     os.environ["LANGCHAIN_TRACING_V2"] = "true"

rag_chain/retrievers_setup.py

@@ -110,7 +110,6 @@ class SparseRetrieverClient:
         model = AutoModelForMaskedLM.from_pretrained(self.model_id)
         return tokenizer, model
 
-    @cache
     def sparse_encoder(self, text: str) -> tuple[list[int], list[float]]:
         """This function encodes the input text into a sparse vector. The encoder is required for the QdrantSparseVectorRetriever.
         Adapted from the Qdrant documentation: Computing the Sparse Vector code.