Update advanced_rag.py

advanced_rag.py

@@ -10,7 +10,8 @@ from langchain.llms import HuggingFacePipeline
 from langchain_community.document_loaders import OnlinePDFLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_community.vectorstores import FAISS
-from langchain_community.embeddings import CohereEmbeddings
+# Replace CohereEmbeddings with HuggingFaceEmbeddings
+from langchain.embeddings import HuggingFaceEmbeddings  
 from langchain_community.retrievers import BM25Retriever
 from langchain.retrievers import EnsembleRetriever
 from langchain.prompts import ChatPromptTemplate
@@ -56,7 +57,8 @@ class ElevatedRagChain:
         """
         Initialize the class with a predefined embedding function, weights, and top_k value.
         """
-        self.embed_func   = CohereEmbeddings(model="embed-english-light-v3.0")
+        # Use HuggingFaceEmbeddings with a model that doesn't require an API key.
+        self.embed_func   = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
         self.bm25_weight  = 0.6
         self.faiss_weight = 0.4
         self.top_k        = 5
@@ -101,7 +103,7 @@ class ElevatedRagChain:
          - A DeepSeek model (via a HuggingFace pipeline)
         
         Note: The retrieval is performed using an ensemble of BM25 and FAISS retrievers
-        without applying a Cohere reranker.
+        without applying any additional reranking.
         """
         # Combine BM25 and FAISS retrievers into an ensemble retriever
         self.ensemble_retriever = EnsembleRetriever(