Update utils.py

utils.py

@@ -5,23 +5,29 @@ from langchain_nomic.embeddings import NomicEmbeddings
 from langchain_core.documents import Document
 from langchain.retrievers.document_compressors import CohereRerank
 from langchain.retrievers import ContextualCompressionRetriever
-from langchain.retrievers import BM25Retriever, EnsembleRetriever
+from langchain.retrievers import EnsembleRetriever
+from langchain_community.retrievers import BM25Retriever
 from langchain_groq import ChatGroq
+
 from dotenv import load_dotenv
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.runnables import Runnable, RunnableMap
 from langchain.schema import BaseRetriever
 from qdrant_client import models
 
+
+from langchain_huggingface.embeddings import HuggingFaceEmbeddings
+
 load_dotenv()
 #Retriever
 def retriever(n_docs=5):
-    vector_database_path = "chromadb"
+    vector_database_path = "chromadb3"
 
-    embeddings_model = NomicEmbeddings(model="nomic-embed-text-v1.5", inference_mode="local")
+    #embeddings_model = NomicEmbeddings(model="nomic-embed-text-v1.5", inference_mode="local")
+    embeddings_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
 
 
-    vectorstore = Chroma(collection_name="chromadb",
+    vectorstore = Chroma(collection_name="chroma_db",
                         persist_directory=vector_database_path,
                     embedding_function=embeddings_model)
 
@@ -49,14 +55,14 @@ def retriever(n_docs=5):
     return retriever
 
 #Retriever prompt
-rag_prompt = """You are an assistant for question-answering tasks.
-The questions that you will be asked will mainly be about SUP'COM (also known as Higher School Of Communication Of Tunis).
+rag_prompt = """You are a medical chatbot designed to answer health-related questions.
+The questions you will receive will primarily focus on medical topics and patient care.
 Here is the context to use to answer the question:
-{context} 
-Think carefully about the above context. 
+{context}
+Think carefully about the above context.
 Now, review the user question:
 {input}
-Provide an answer to this questions using only the above context. 
+Provide an answer to this question using only the above context.
 Answer:"""
 
 # Post-processing